msc.bib

# AUTOGENERATED
# Import from: https://repository.tudelft.nl/islandora/search/wagter%20OR%20croon%20OR%20remes%20OR%20karasek%20OR%20smeur%20OR%20dupeyroux%20OR%20hamaza%20OR%20"Scheper, K.Y.W."%20OR%20"popovic, marija"?collection=education&display=tud_csv


@mastersthesis{uuid:7e9dd0aa-4d24-4100-a224-14e71f86cdda,
	abstract  = {},
	author    = {Gervas Montoya, Gabriel },
	keywords  = {},
	note      = {Smeur, E.J.J. (mentor); Varriale, Carmine (graduation committee); Georgopoulos, P. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; Grochowski, Bartłomiej },
	title     = {IUVO: An Emergency Response Flyer},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:7e9dd0aa-4d24-4100-a224-14e71f86cdda},
	year      = {2024}
}

@mastersthesis{uuid:8649d62f-6266-44a4-89de-5b5805d83ae5,
	abstract  = {This paper presents an encoder-decoder-style convolutional neural network (CNN) for the purpose of improving monocular and stereo depth estimation (SDE) estimates, by combining them with the corresponding monocular estimates through a fusion network, assisted by prior information to provide context for the fusion. Video cameras are commonly used for depth perception in robotics, especially weight-sensitive applications, such as on Micro Aerial Vehicles (MAV). The two primary paradigms for vision-based depth perception are monocular and stereo depth or disparity estimation, each having their own strengths and weaknesses. These strengths and weaknesses seem to be complementary, and thus a fusion of the two may result in more accurate predictions. In this paper, we investigate this fusion by training a CNN that combines stereo and monocular depth or disparity estimates. The fusion network is agnostic to the choice of the input networks, providing great flexibility. It was found that such a fusion network, while increasing the computational complexity of the depth perception pipeline, indeed improves the accuracy of the estimates. The number of outlier predictions has been significantly decreased, while also limiting some fundamental limitations of both stereo and monocular methods, such as errors arising from occluded regions.},
	author    = {Tóth, Dani },
	keywords  = {Computer Vision; Deep Learning; CNN; Depth Estimation},
	note      = {de Croon, G.C.H.E. (mentor); van Dijk, Tom (mentor); de Wagter, C. (graduation committee); Eleftheroglou, N. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Deep Learning Fusion of Monocular and Stereo Depth Maps Using Convolutional Neural Networks},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:8649d62f-6266-44a4-89de-5b5805d83ae5},
	year      = {2024}
}

@mastersthesis{uuid:5aeb0475-b3d5-45a4-82c8-7b92fabbb683,
	abstract  = {Quad-planes combine hovering and vertical takeoff and landing capability with fast and efficient forward flight. Regular Quad-planes with dedicated pusher motor can be subject to gust disturbances, and are not well-equipped to deal with actuator faults. Dual-axis Tilt-Rotor quad-planes are more maneuverable due to their overactuation. This also increases their gust resilience and allows them to hover statically after actuator failures. The vehicle in this paper uses an Incremental Nonlinear Dynamic Inversion (INDI ) controller, combined with a nonlinear Sequential Quadratic Programming (SQP) Control Allocation (CA ) algorithm, which can also find hover solutions in the case of actuator failures. We investigate both a combined allocation of linear and angular accelerations, as well as a cascaded allocation scheme. Due to large required changes in roll and pitch angles, the cascaded approach is selected in this research. Introduction of a tertiary control effort term, separation of attitude and actuator command optimization and a simulated Fault Detection and Identification ( FDI) mechanism led to repeated successful recovery from a motor failure in hover. Position tracking was demonstrated under failure in the recon- figured flight condition. Index Terms- Tilt-rotor, dual-axis tilt, quad-plane, FTC, over- actuated, control allocation},
	author    = {Voß, Nico },
	keywords  = {Tilt-rotor; FTC; Quadplane; control allocation; Overactuation},
	note      = {Smeur, E.J.J. (mentor); Mancinelli, A. (mentor); Bombelli, A. (graduation committee); de Visser, C.C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Fault Tolerant Control in Over-Actuated Hybrid Tilt-Rotor Unmanned Aerial Vehicles},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:5aeb0475-b3d5-45a4-82c8-7b92fabbb683},
	year      = {2024}
}

@mastersthesis{uuid:eb522c6b-1b1d-4988-8a7a-e2846dc697c5,
	abstract  = {The estimation of optical flow, which determines the movement of objects in a visual scene, is a crucial problem in computer vision. It is essential for applications such as autonomous navigation, where precise motion estimation is critical for performance and safety.<br/><br/>Frame-based cameras capture sequences of still images at regular intervals, from which optical flow is traditionally extracted using optimization-based or learning-based methods. Recently, event-based cameras, which detect changes in pixel brightness asynchronously, have gained traction due to their high temporal resolution and robustness to motion blur, and many algorithms have been developed to estimate optical flow from this data. IDNet is a learning-based approach that achieves state-of-the-art performance. However, IDNet and similar models face two major challenges: they require labeled ground-truth data for training, which is scarce and difficult to collect, and they rely on recurrent neural networks (RNNs) with a fixed number of refinement iterations. This fixed iteration scheme does not adapt to scene complexity, limiting accuracy for complex flows and increasing computational effort for simpler patterns.<br/><br/>The aim of this project is to explore, implement, and evaluate potential methods to address these two mentioned limitations and enhance the capabilities of models like IDNet.<br/><br/>To remove the need for ground-truth data, a self-supervised learning paradigm was implemented by introducing a novel contrast maximization loss that assesses the blur present when accumulating raw events for a certain time interval and compensating it with the predicted flow. To assess the effectiveness of this method, models were trained on the benchmark MVSEC dataset, showing improved results over previous methods with up to 15% on some sequences and an 8% improvement on average. Based on these experiments and results, further research directions were proposed.<br/><br/>As for the problem of the current fixed iteration scheme, Deep Equilibrium Models were found to provide a promising pathway to solving it. These novel models reformulate their iterative structure into a root-finding problem and utilize traditional solvers to find a solution based on some tolerance, providing a trade-off between speed and accuracy. Moreover, they allow for direct differentiation through the network using only their final estimate, compared to previous methods that keep track of their state through all iterations, leading to an O(1) memory consumption. Implementing these and some additional ideas, the trained DEQ IDNet model reached competitive performance on the DSEC dataset while consuming 15% less memory. Yet, further work is needed to close the gap and achieve state-of-the-art performance.},
	author    = {Shokolarov, Aleksandar },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (graduation committee); Wu, Y. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Self-Supervised Learning of Event-Based Optical Flow via Deep Equilibrium Models},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:eb522c6b-1b1d-4988-8a7a-e2846dc697c5},
	year      = {2024}
}

@mastersthesis{uuid:bd671c3b-afc3-4215-a724-dd69512f4715,
	abstract  = {Neglecting actuator dynamics in nonlinear control and control allocation can lead to performance degradation, especially when considering fast dynamic systems. This thesis provides a novel method to account for actuator dynamics in the control allocation solution, dynamic incremental nonlinear control allocation, or D-INCA. The incremental approach allows for the implementation of a first order discrete-time actuator dynamics model in the quadratic programming (QP) solver. This model is used to find the optimal command inputs in addition to the desired physical actuator deflections, hereby compensating for actuator dynamics delays. Whereas, the baseline incremental nonlinear control allocation (INCA) approach requires pseudo-control hedging of the outer loop reference to increase closed loop stability margins under actuator dynamics delays. To its advantage, D-INCA does not require feedback of higher order output derivatives than INCA and can be used with nonlinear non-control affine systems. Furthermore, with adaptive D-INCA, or AD-INCA, an actuator dynamics parameter estimator is introduced to adapt the actuator model online, minimizing actuator tracking errors after actuator failures. The proposed methods are applied to a fighter aircraft model with an over-actuated innovative control effectors suite and results are compared to the baseline INCA controller.},
	author    = {Stam, Noah },
	keywords  = {Dynamic Control Allocation; Adaptive control; Nonlinear control},
	note      = {de Visser, C.C. (mentor); Smeur, E.J.J. (graduation committee); Mooij, E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Adaptive dynamic incremental nonlinear control allocation: An actuator fault-tolerant control solution for high-performance aircraft},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:bd671c3b-afc3-4215-a724-dd69512f4715},
	year      = {2024}
}

@mastersthesis{uuid:b4f643b2-a64f-4fb4-a18f-5012364f7b0f,
	abstract  = {Spiking neural networks implemented for sensing and control of robots have the potential to achieve lower latency and power consumption by processing information sparsely and asynchronously. They have been used on neuromorphic devices to estimate optical flow for micro air vehicles navigation, however robotic implementations have been limited to hardware setups with sensing and processing as separate systems. This article investigates a new approach for training a spiking neural network for optical flow to be deployed on the speck2e device from Synsense. The method takes into account the restrictions of the speck2e in terms of network architecture, neuron model, and number of synaptic operations and it involves training a recurrent neural network with ReLU activation functions, which is subsequently converted into a spiking network. A system of weight rescaling is applied after conversion, to ensure optimal information flow between the layers. Our study shows that it is possible to estimate optical flow with Integrate-and-Fire neurons. However, currently, the optical flow estimation performance is still hampered by the number of synaptic operations. As a result, the network presented in this work is able to estimate optical flow in a range of [-4, 1] pixel/s.},
	author    = {Branca, Francesco },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Optical Flow Determination using Neuromorphic Hardware with Integrate &amp; Fire Neurons},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:b4f643b2-a64f-4fb4-a18f-5012364f7b0f},
	year      = {2024}
}

@mastersthesis{uuid:6a74fb80-425c-4366-8110-fecfb4a1a5fc,
	abstract  = {Olfactory learning in <i>Drosophila </i>larvae exemplifies efficient neural processing in a small-scale network with minimal power consumption. This system enables larvae to anticipate important outcomes based on new and familiar odor stimuli, a process crucial for survival and adaptation. Central to this learning mechanism is the olfactory pathway model, which embodies the principles of synaptic plasticity and associative learning through prediction error coding mediated by specific neuromodulating neurons in the mushroom body, like dopaminergic neurons. There is a pressing need to develop novel computational frameworks that capture the spatio-temporal processes while remaining compatible with the constraints of small-scale neural networks. These frameworks should draw inspiration from the biophysical properties of neurons within the olfactory pathway model, enabling accurate emulation of neural dynamics and efficient learning processes using spiking neural networks. This thesis proposes a framework based on a phenomenological conductance-based leaky integrate-and-fire (COBALIF) neuron model, inspired by the olfactory pathway model of <i>Drosophila</i> larvae. By first prototyping the spiking neural network in Intel's Lava Python-based framework, we validated the design on a neuron and system level for a neuromorphic hardware implementation. This was the foundation of a programmable, neuromorphic FPGA architecture capable of adaptive optimization, employed on a Zynq 7000 SoC FPGA. By implementing this architecture in a single-precision floating-point format, we model the real-time neural dynamics of the COBALIF neuron in one-tenth of a millisecond precision. Moreover, our FPGA implementation serves as a feasible prototype for deploying such biologically inspired neurons and their spatio-temporal dependencies in digital design, paving the way for scaling up to small-scale networks.},
	author    = {Usa, Lyana },
	keywords  = {Olfactory learning; Drosophila; Spatio-temporal processes; Spiking neural networks; Neuromorphic hardware; FPGA architecture; Digital design},
	note      = {Frenkel, C. (mentor); Makinwa, K.A.A. (graduation committee); de Croon, G.C.H.E. (graduation committee); Nawrot, M. P. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Electrical Engineering, Mathematics and Computer Science},
	title     = {Novel Neuromorphic Hardware Inspired by the Olfactory Pathway Model of the <i>Drosophila</i>: Leveraging bio-plausible computational primitives in digital circuits for spatio-temporal processing},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:6a74fb80-425c-4366-8110-fecfb4a1a5fc},
	year      = {2024}
}

@mastersthesis{uuid:491a7744-dc49-405c-ba38-5198b3e839a8,
	abstract  = {Climate change poses a serious threat to ecosystems and increases the need for accurate and rigorous monitoring of ecosystems. Current monitoring solutions are often bulky, expensive, and lack critical functionalities such as on-board inference capabilities, robust wireless connections, and a diverse sensor suite. Ecological monitoring projects often suffer from inefficiencies caused by the large time delays between collecting data and analyzing said data, as well as having to spend large amounts of time in the field setting up the sensors manually. This thesis addresses many of these issues by designing a sensor with an extensive sensor suite, robust wireless capabilities and an on-board audio classifier able to perform real-time inference. Furthermore, attention is paid to making the system extendable in the future and allow for potentially integrating the sensors with a drone delivery- and retrieval system. The system tests performed indicate that the system has great potential given more time to tweak some of its identified shortcomings.},
	author    = {de Waal, Luke },
	keywords  = {Ecological Monitoring; Internet of Things; Edge Computing},
	note      = {Hamaza, S. (mentor); Rajan, R.T. (mentor); Smeur, E.J.J. (graduation committee); Hendriks, R.C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Aerospace Engineering},
	title     = {Towards a Robust Wireless Real-Time Ecological Monitoring System},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:491a7744-dc49-405c-ba38-5198b3e839a8},
	year      = {2024}
}

@mastersthesis{uuid:3193131c-6b68-46a2-afe1-964a044dd6f9,
	abstract  = {Quad-planes are a type of vehicle which combine the hovering capability of quadcopters and the forward flight efficiency of winged aircraft. Flight tests conducted on a dual-axis tilting-rotor quad-plane, designed to fly without aerodynamic surfaces, observed that the quad-plane suffered from insufficient roll authority during fast, forward flight. Subsequent wind tunnel testing confirmed a two- to fourfold reduction in roll moment generation from propellers mounted in front of the wing at similar levels of tilt as their rear counterparts, caused by propeller-wing interactions. To address the mismatch in actuator effectiveness shown by the wind tunnel experiment, the effect of the propeller-wing interactions was incorporated into the aero-propulsive model by means of a global polynomial, the structure of which was found using multivariate orthogonal function modelling. New flight tests demonstrated that, by including the propeller-wing interactions in the control allocation, the vehicle is capable of tracking a figure 8 maneuver without aerodynamic surfaces, and without compromising tracking performance.},
	author    = {Wechtler, Noah },
	keywords  = {},
	note      = {Smeur, E.J.J. (mentor); Mancinelli, A. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Implications of Propeller-Wing Interactions on the Control of Aerodynamic-Surface-Free Tilt-Rotor Quad-Planes},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:3193131c-6b68-46a2-afe1-964a044dd6f9},
	year      = {2024}
}

@mastersthesis{uuid:9a295d44-1e95-4911-a4a2-4a96c498fe79,
	abstract  = {Drones are increasingly used nowadays, primarily for visual inspection tasks facilitated by onboard cameras. The field of aerial manipulation tries to expand the capabilities of drones by attaching a manipulator, enabling physical interaction. Unfortunately, the usability of aerial manipulators is hindered by disturbances resulting from the movements of the manipulator. These disturbances, including reaction forces and a shifting centre of mass, not only affect manipulation accuracy but also pose safety risks by potentially destabilizing the drone. In this thesis, a design is presented that addresses this challenge by leveraging the theory of dynamic balance. <br/>A new design approach of making a manipulator fly, instead of the common approach of mounting a manipulator arm to a drone was used. This new approach avoids interference with the drone's components, allowing to focus on the design of the manipulator arm. Furthermore, it made it possible to create a manipulator which can manipulate above, to the side and underneath itself. This makes the presented manipulator arm more versatile than common aerial manipulators whose workspace is mostly located only above or below the drone. The kinematics, workspace and balance conditions of the manipulator arm are presented. Furthermore, the design's workspace is optimised while the mass of the manipulator is minimized in a bilevel optimisation. Finally, the design is validated both by simulation and measurements performed with the built prototype.<br/>The design presented is the first inherently fully dynamically balanced manipulator with omnidirectional workspace which can be used for aerial manipulation.<br},
	author    = {Bom, Alexander },
	keywords  = {Inherent Dynamic Balance; Full Dynamic Balance; Aerial Manipulation; Mechanism Design; Bilevel Optimisation; Manipulator; UAM},
	note      = {van der Wijk, V. (mentor); Hamaza, S. (mentor); Herder, J.L. (graduation committee); Goosen, J.F.L. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering},
	title     = {Design of an inherently fully dynamically balanced aerial manipulator with omnidirectional workspace},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:9a295d44-1e95-4911-a4a2-4a96c498fe79},
	year      = {2024}
}

@mastersthesis{uuid:9ab2b4ba-8f91-4891-8190-4a96f77c471e,
	abstract  = {New insights into the landing behavior of bumblebees show an adaptive strategy where the optical flow expansion of the landing target is step-wise regulated. In this article, the potential benefits of this approach are studied by replicating the landing experiment with a quadrotor. To this end, an open-loop switching method is developed, enabling fast steps in divergence. An adaptive control law is used to deal with non-linear system dynamics, where the control gain is scheduled based on the control effectiveness of the actuator inputs during the steps. It is demonstrated that the quadrotor can reliably land on the target from varying initial positions, and the switching strategy shows a slight reduction in landing time compared to a constant divergence strategy with the same average divergence over distance. This strategy also reduces the maximum velocity during the landing.},
	author    = {Hazelaar, Sander },
	keywords  = {Visual Servoing; Autonomous Landing; Quadrotor Control; Non-linear systems; Computer Vision},
	note      = {de Croon, G.C.H.E. (mentor); Yedutenko, M. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Adaptive Visual Servoing Control for Quadrotors: A Bio-inspired Strategy Using Active Vision},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:9ab2b4ba-8f91-4891-8190-4a96f77c471e},
	year      = {2024}
}

@mastersthesis{uuid:a164abc6-0103-4fa0-b7ac-c15bce2bce64,
	abstract  = {These days, people see more and more applications for drones, including monitoring rainforests to protect plant and animal species. However, drones face challenges when navigating through the dense and cluttered vegetation of the forest. These environments necessitate advanced autonomous detection and navigation to make the drone traverse robustly and fly safely. In addition, the forest brings extra challenges, such as blocked signals for GPS localisation, remote control, and remote supervising.<br/><br/>In this thesis project, a drone is designed, built, and programmed to navigate autonomously in the rainforest with complete onboard computing and no GPS localisation. This 500-gram drone is being extensively tested and optimized in real forest conditions, and a dataset is being created from its autonomous flights to simulate various configurations of the path-planning algorithm. The results of these simulations on this dataset are then used for thorough research on how the algorithm can downscale to smaller systems and how this affects performance.<br/><br/>By using the results of this research on downscaling, a 100-gram drone is built and programmed to fly in forest conditions with complete onboard computation. Challenging on this small-size drone is the use of low-quality lightweight sensors and processor. The processor only weighs 10 grams, and the depth camera weighs 8 grams. Unique on this small drone is the 3D path planning fully computed onboard and the implementation of a new type of depth camera.},
	author    = {Zwanenburg, Andreas },
	keywords  = {autonomous navigation; drone; downscaling},
	note      = {Wisse, M. (mentor); Hamaza, S. (graduation committee); Benders, D. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering},
	title     = {A lightweight quadrotor autonomy system: To navigate in densely cluttered forest environments},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:a164abc6-0103-4fa0-b7ac-c15bce2bce64},
	year      = {2024}
}

@mastersthesis{uuid:f7ec9e1c-15db-4982-b2fa-4ba0f51a5b91,
	abstract  = {Aerial manipulators, characterized by their ability to actively engage with the environment, are gaining popularity for their versatility in performing diverse tasks.<br/>This research focuses on augmenting the capabilities of aerial manipulators through the integration of tactile feedback, specifically employing a compliant bio-inspired three-fingered manipulator equipped with tactile capacitive sensors on each finger. The manipulator is affixed to a drone, enabling tactile-guided navigation for precise object localization, subsequent grasping, and perching. Additionally, a grasp evaluator assesses grasp quality, allowing the system to adapt by suggesting alternative grasp locations after an initial attempt is unsuccessful. A comparative analysis between the system’s performance using tactile feedback and open-loop perching/grasping in perching scenarios demonstrates that the grasp evaluator improves the perching success rate by 55%-point and increases the allowable object uncertainty by 0.14 [m]. These findings highlight the efficacy of this approach in advancing aerial manipulator capabilities.},
	author    = {Jadoenathmisier, Anish },
	keywords  = {},
	note      = {Hamaza, S. (mentor); de Croon, G.C.H.E. (graduation committee); Pool, D.M. (graduation committee); Bredenbeck, A. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Aerial Perching via Active Touch: Embodying Robust Tactile Grasping on Aerial Robots},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:f7ec9e1c-15db-4982-b2fa-4ba0f51a5b91},
	year      = {2023}
}

@mastersthesis{uuid:319f4f93-0590-4f9e-8029-2911f61db477,
	abstract  = {},
	author    = {Bononi Bello, Chris },
	keywords  = {},
	note      = {Snellen, M. (mentor); Yin, F. (graduation committee); Smeur, E.J.J. (graduation committee); Heblij, S. (mentor); de Haan, W (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Aircraft Noise and Climate Effects},
	title     = {The development of a distributed electric propulsion (DEP) noise model},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:319f4f93-0590-4f9e-8029-2911f61db477},
	year      = {2023}
}

@mastersthesis{uuid:bfad7b0e-f6db-49d0-8642-5e4fbc6e3861,
	abstract  = {The transportation of payloads utilizing multiple drones presents a promising application for lifting heavier loads that exceed the payload capacity of a single drone. However, the cable-suspended payload introduces significant challenges to the system, and this research area remains relatively unexplored. In this work, a novel solution for payload-carrying application is proposed. First, the dynamics of cable-suspended payload transportation using multiple quadrotors, taking into account the influence of drag forces on the quadrotors are studied. A nonlinear optimization is employed to control the payload while distributing the control effort required for manipulating the suspended load over the drones in the formation while ensuring both tension constraints and collision avoidance between drones in the formation. The feasible path commands for formation agents are computed from the optimization. One of the critical aspects for controlling such a system is the load-introduced force, which exhibits rapid and complex variations. To address this, an extended state observer is employed to estimate the load force, eliminating the need for a tension sensor. In pursuit of a robust framework, a formation reset strategy is also developed, allowing to maintain load tracking performance and ensure the safety of formation agents, even in the event of a malfunction in one of the drones. A series of simulations are conducted to validate the effectiveness and robustness against disturbance and suspension failure of the proposed strategy and controllers. Results demonstrate that the whole multi-lift system can handle external disturbances, model uncertainties regarding drone inertia, mass and load mass, as well as suspension failures.},
	author    = {Liu, Huamin },
	keywords  = {payload transportation; tension optimization; extended state observer; formation recovery},
	note      = {Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Collaborative Payload Carrying with Multiple MAVs},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:bfad7b0e-f6db-49d0-8642-5e4fbc6e3861},
	year      = {2023}
}

@mastersthesis{uuid:34213dbf-32ad-4f8d-b0f0-ed398608d682,
	abstract  = {In this study, we present a first step towards a cutting-edge software framework that will enable autonomous racing capabilities for nano drones. Through the integration of neural networks tailored for real-time operation on resource-constrained devices. A lightweight Convolutional Neural Network, with the Gatenet architecture, is adjusted for reduced computational demand and is successfully deployed on a GAP8 processor at a rate of 16$Hz$. This network provides gates' size and location data for the subsequent positioning algorithm. A second neural network, trained through reinforcement learning, governs the drone's guidance and control systems, demonstrating a remarkable rate of 167$Hz$ on an STM32F405 processor. The attitude rates and thrust outputted by this network are then fed to an attitude rate PID controller.<br/><br/>The research shows that state-of-the-art neural networks for drone racing can be deployed on nano drones, despite their limited processing power. Nonetheless, the study demonstrated specific limitations, such as the perception network's sensitivity to white pixels in the image reducing its effectiveness when light sources are present in the scene. These findings underscore the importance of dataset composition and the need for diverse training scenarios to enhance the neural network's generalizability and performance in real-world applications.},
	author    = {Magri, Federico },
	keywords  = {Reinforcement Learning; Convolutional neural network; Nano Drones; Quantization},
	note      = {de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Ferede, R. (mentor); Bahnam, S.A. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Modular Neural Network Navigation for Autonomous Nano Drone Racing},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:34213dbf-32ad-4f8d-b0f0-ed398608d682},
	year      = {2023}
}

@mastersthesis{uuid:c56b290f-8c43-4649-8360-13b7652710e8,
	abstract  = {Capable of both vertical take-off and landing and forward flight, tail-sitters are a versatile class of UAVs with a large range of potential applications. A variant of tailsitters using tilt-rotors instead of ailerons for pitch and roll control has been proposed to mitigate the reduced control authority at low to zero velocities. The control of the translational dynamics for this platform is uniquely challenging. The extended flight envelope requires the controller to be able to perform hover and forward flight which are two flight phases with very different dynamics. Additionally, the tilt-rotor mechanism used to control the system is highly nonlinear which adds to the challenge. This paper presents a novel acceleration controller using Nonlinear Model Predictive Control (NMPC) in addition to the use of behavioural cloning to mimic the NMPC using a feedforward neural network. It is shown that behavioural cloning does successfully transfer general flight characteristics but that the performance is degraded with respect to the NMPC. Additionally, a sensitivity analysis was performed to investigate the effects of improper parameter estimation on controller performance. The most interesting result from this analysis is the strong sensitivity of both controllers to changes in centre of gravity location and mass.},
	author    = {van Wissen, Alexis },
	keywords  = {UAV; Tailsitter; Tilt-rotor; Acceleration Control; Nonlinear Model Predictive Control; Behavioural Cloning},
	note      = {Smeur, E.J.J. (mentor); Ma, Z. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Tilt-rotor Tailsitter Global Acceleration Control: Behavioural Cloning of a Nonlinear Model Predictive Controller},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:c56b290f-8c43-4649-8360-13b7652710e8},
	year      = {2023}
}

@mastersthesis{uuid:bd663e11-947f-4ae1-ae2d-860d44fba7af,
	abstract  = {A key important part of a warehouse operation is to keep track of the products in the warehouse. Traditionally, handheld scanners are used to scan the products to perform a stock count. The advancements in robotics have paved the way for new technologies that can improve the scanning process. This work shows how prior knowledge and warehouse structure can be used to perform scanning operations. The method uses a localized camera in the warehouse whose estimates drift over time and the knowledge about the environment to estimate the correct location of the product using factor graphs. The proposed method shows by exploiting the structure of the warehouse the drift in the VIO(Visual Inertial Odometry) can be reduced and the position estimation of the location labels can be improved},
	author    = {KALYANASUNDARAM, DARSHAN },
	keywords  = {Factor graphs; Inventory scanning; Warehouse},
	note      = {de Croon, G.C.H.E. (mentor); Alonso Mora, J. (mentor); Ozo, Michaël (mentor); Caesar, H.C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering},
	title     = {Factor graphs for inventory label scanning in warehouse},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:bd663e11-947f-4ae1-ae2d-860d44fba7af},
	year      = {2023}
}

@mastersthesis{uuid:ab98e53a-5931-4b78-98ec-f0cb6df20986,
	abstract  = {Gas Source Localization (GSL) is a challenging field of research within the robotics community. Existing methods vary widely and each has its own strengths and weaknesses. Existing GSL evaluations vary in environment size, wind conditions, and gas simulation fidelity, thereby complicating objective comparison between algorithms. They also lack photo-realistic rendering for the integration of obstacle avoidance. In this paper, we propose GSL-Bench, a benchmarking suite to evaluate the performance of GSL algorithms. GSL-Bench features high-fidelity graphics and gas simulation. Realism is further increased by simulating relevant gas and wind sensors. Scene generation is simplified with the introduction of AutoGDM+, capable of procedural environment generation, CFD and particle-based gas dispersion simulation. To illustrate GSL-Bench's capabilities, three algorithms are compared in six warehouse settings of increasing complexity: E. Coli, dung beetle and a random walker. Our results demonstrate GSL-Bench's ability to provide valuable insights into algorithm performance.},
	author    = {Erwich, Hajo },
	keywords  = {gas sensing; benchmarking; simulation; source localization; gas dispersion; odour souce localization},
	note      = {de Croon, G.C.H.E. (mentor); Duisterhof, B.P. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {GSL-Bench: High Fidelity Gas Source Localization Benchmarking},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:ab98e53a-5931-4b78-98ec-f0cb6df20986},
	year      = {2023}
}

@mastersthesis{uuid:6320374a-b84d-4bbf-be48-10cee914b9e0,
	abstract  = {Recent literature in real-time trajectory planning has proposed using Control Barrier Functions (CBFs) as collision constraints in Model Predictive Control (MPC) for efficient guidance, a concept referred to as MPC-CBF. This concept has been explored for both first and second-order CBFs. However, these approaches relied on an analytical description of the environment. Building upon this, we propose combining MPC-CBF with Euclidean Signed Distance Fields (ESDFs), eliminating the need for such an analytical model of the environment. Notably, we extend this approach to a new field by applying it to Unmanned Aerial Vehicles (UAVs). Through simulations, we compare flown trajectories and noise robustness for distance constraints, first-order CBF constraints and second-order CBF constraints. First-order CBF constraints outperform distance constraints, excelling in path planning and noise resilience. Second-order CBF constraints face challenges due to numerical approximations of the hessian of the ESDF and stricter dependency on an accurate acceleration model, limiting their practicality for UAVs. The proposed control framework was tested by safely maneuvering an enterprise inspection drone around a Boeing 787-9 aircraft inside an aircraft hangar, confirming its effectiveness in collision avoidance and real-world scenarios.},
	author    = {de Vries, Rinto },
	keywords  = {mav; mpc; mavlab; model predictive control; cbf; control barrier function; trajectory planning; esdf; euclidean signed distance field; uav; drone; collision avoidance; obstacle avoidance},
	note      = {Smeur, E.J.J. (mentor); Horstink, Thomas (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Application of Control Barrier Functions to Collision Free Model Predictive Control: Robust UAV Trajectories with MPC-CBF and Euclidean Signed Distance Fields},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:6320374a-b84d-4bbf-be48-10cee914b9e0},
	year      = {2023}
}

@mastersthesis{uuid:1edec476-3b58-458d-a4a6-cbba30b783e6,
	abstract  = {In recent years, Artificial Neural Networks (ANN) have become a standard in robotic control. However, a significant drawback of large-scale ANNs is their increased power consumption. This becomes a critical concern when designing autonomous aerial vehicles, given the stringent constraints on power and weight. Especially in the case of blimps, known for their extended endurance, power-efficient control methods are essential. Spiking neural networks (SNN) can provide a solution, facilitating energy-efficient and asynchronous event-driven processing. <br/>In this paper, we have evolved SNNs for accurate altitude control of a non-neutrally buoyant indoor blimp, relying solely on onboard sensing and processing power. The blimp's altitude tracking performance significantly improved compared to prior research, showing reduced oscillations and a minimal steady-state error. The parameters of the SNNs were optimized via an evolutionary algorithm, using a Proportional-Derivative-Integral (PID) controller as the target signal. We developed two complementary SNN controllers while examining various hidden layer structures. The first controller responds swiftly to control errors, mitigating overshooting and oscillations, while the second minimizes steady-state errors due to non-neutral buoyancy-induced drift. Despite the blimp's drivetrain limitations, our SNN controllers ensured stable altitude control, employing only 160 spiking neurons.},
	author    = {Burgers, Tim },
	keywords  = {Neurorobotics; Machine Learning for Robot Control; Aerial Systems: Mechanics and Control; Spiking Neural Network (SNN); Evolutionary Algorithm - EA; Blimp},
	note      = {de Croon, G.C.H.E. (mentor); Stroobants, S. (mentor); de Wagter, C. (graduation committee); Bombelli, A. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Evolving Spiking Neural Networks to Mimic PID Control: Applied to Autonomous Blimps},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:1edec476-3b58-458d-a4a6-cbba30b783e6},
	year      = {2023}
}

@mastersthesis{uuid:21b4203a-abbb-47d7-bbd5-df042d8d7b53,
	abstract  = {The design of aerial robots capable of perching poses significant challenges, from requiring pilots to master precise manoeuvres, to devising hardware and software capable of adapting to diverse perch structures and complex field environments. The Slapper drone presented in this paper tackles these challenges through three main innovations. First, a lightweight, vision-based system for autonomous perch detection using onboard flight hardware detects (imperfect) cylindrical objects found in both natural and artificial environments. Second, an onboard flight planning algorithm autonomously handles the detection, approach and perching flight phases, removing the need for a pilot. Third, a completely passive gripper utilises bistable shell structures to allow for perching on general long narrow features without any precise control inputs or power consumption. This design was successfully validated through both simulation and multiple indoor flights to result in reliable autonomous quadrotor perching in real-world environments.},
	author    = {McGinley, Seamus },
	keywords  = {},
	note      = {Hamaza, S. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Vision-guided Quadrotor Perching on Imperfectly Cylindrical Structures},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:21b4203a-abbb-47d7-bbd5-df042d8d7b53},
	year      = {2023}
}

@mastersthesis{uuid:98b694a1-72fc-4a4f-907b-d511cc7f9bdd,
	abstract  = {Insects have long been recognized for their ability to navigate and return home using visual cues from their nest’s environment. However, the precise mechanism underlying this remarkable homing skill remains a subject of ongoing investigation. Drawing inspiration from the learning flights of honey bees and wasps, we propose a robot navigation method based on directly learning the home vector directions from visual percepts during the learning flight. Subsequently, the robot will travel away from the nest, come back by odometric means, and eliminate the resultant drift by inferring the home vector orientation from the currently experienced view. In this study, a convolutional neural network is employed as learning mechanism in both simulated and real forest environments. Additionally, a comprehensive performance analysis reveals that the network’s homing abilities closely resemble those observed in real insects, all while only utilizing visual and odometric senses. If all images contain sufficient texture and illumination, the average errors<br/>of the inferred home vectors remain below 24°. Moreover, our investigation reveals a noteworthy insight: the trajectory followed during the initial learning flight, for sample image acquisition, exerts a pronounced impact on the network’s output. For instance, a higher density of sample points in proximity to the nest results in a more consistent return.},
	author    = {Firlefyn, Michiel },
	keywords  = {Insect-inspired; Visual homing; Bio-inspired; Navigation},
	note      = {de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Direct Learning of Home Vector Direction: Incited by Existing Insect-Inspired Approaches for Local Navigation and Wayfinding},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:98b694a1-72fc-4a4f-907b-d511cc7f9bdd},
	year      = {2023}
}

@mastersthesis{uuid:ca95c8cb-8df3-4d43-9d17-c7b7f54eb1ea,
	abstract  = {Dynamic obstacle avoidance remains a crucial research area for autonomous systems, such as Micro Aerial Vehicles (MAVs) and service robots. <br/>Efforts to develop dynamic collision avoidance techniques in unknown environments have proliferated in recent years. While these methods exhibit impressive and reliable performance in simpler environments, their efficacy in more challenging settings remains an area ripe for enhancement. The difficulty of these environments arises from a multitude of factors, and currently, no standardized approach exists to quantify this complexity. Additionally, to fairly compare different dynamic collision avoidance strategies, it's essential to assess them in environments with a similar degree of difficulty. Therefore, devising a metric capable of accurately gauging the intricacy of dynamic environments becomes imperative.<br/><br/><br/>Building on this context, this master's thesis endeavors to fill this critical gap through three contributions: 1) The establishment and validation of map difficulty metrics that represent the difficulty of dynamic environments, 2) The introduction of a robust benchmarking pipeline to critically validate the representativeness of the proposed metrics and evaluate various collision avoidance strategies, and 3) The provision of a framework for comparative analysis of different planning strategies, utilizing the introduced map difficulty metric. <br/><br/>The proposed survivability metric effectively captures environmental complexity. Its validity is evidenced by a notable correlation with the success rates of typical collision avoidance methods, with over 1.7 million collision avoidance trials on over six hundred maps, securing a Spearman's Rank correlation coefficient (SRCC) of over 0.9. This metric serves as an indispensable tool for facilitating fair comparisons in this dynamic research domain. More importantly, it offers valuable insights for the future refinement and improvement of dynamic collision avoidance strategies, making a contribution to the continuous advancement of autonomous systems.},
	author    = {SHI, MOJI },
	keywords  = {Dynamic Collision Avoidance; Benchmark Study; Measurement},
	note      = {Alonso Mora, J. (mentor); Chen, G. (mentor); Wisse, M. (graduation committee); Hamaza, S. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics},
	title     = {Evaluating Dynamic Environment Difficulty for Obstacle Avoidance Benchmarking},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:ca95c8cb-8df3-4d43-9d17-c7b7f54eb1ea},
	year      = {2023}
}

@mastersthesis{uuid:bcac496c-6757-4067-b1dd-5d8356486bf8,
	abstract  = {Event cameras are novel bio-inspired sensors that capture motion dynamics with much higher temporal resolution than traditional cameras, since pixels react asynchronously to brightness changes. They are therefore better suited for tasks involving motion such as motion segmentation. However, training event-based networks still represents a difficult challenge, as obtaining ground truth is very expensive and error-prone. In this article, we introduce EV-LayerSegNet, the first self-supervised CNN for event-based motion segmentation. Inspired by a layered representation of the scene dynamics, we show that it is possible to learn affine optical flow and segmentation masks separately, and use them to deblur the input events. The deblurring quality is then measured and used as self-supervised learning loss.},
	author    = {Farah, Youssef },
	keywords  = {Event-based vision; Self-supervised learning; Deep Learning; Motion Segmentation; Affine layered motion model},
	note      = {de Croon, G.C.H.E. (mentor); Mooij, E. (graduation committee); Ellerbroek, Joost (graduation committee); Paredes Valles, F. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {EV-LayerSegNet: Self-supervised Motion Segmentation using Event-based Cameras},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:bcac496c-6757-4067-b1dd-5d8356486bf8},
	year      = {2023}
}

@mastersthesis{uuid:e52f0ee0-a859-4177-80e4-268dfd65deca,
	abstract  = {Neuromorphic sensors, like for example event cameras, detect incremental changes in the sensed quantity and communicate these via a stream of events. Desired properties of these signals such as high temporal resolution and asynchrony are not always fully exploited by algorithms that process these signals. Spiking neural networks (SNNs) have emerged as the algorithms that promise to maximally attain these characteristics and are likely the key to achieving a fully neuromorphic computing pipeline. But, this means that if the SNN is to take full advantage, the event stream must be sent directly and unaltered to the SNN, which in turn implies that all temporal integration should occur inside the SNN. Therefore, it is interesting to investigate the mechanisms that achieve this. This thesis does so through evaluating and comparing the performance of different memory mechanisms in SNNs found in the literature, as well as through an in depth analysis of the inner workings of these mechanisms. The mechanisms include spiking neural dynamics (leaks and thresholds), explicit recurrent connections, and propagation delays. We demonstrate our concepts on two small scale generated 1D moving pixel tasks in preliminary experiments first. After that, we extend our research to compare the memory mechanisms on a real-world neuromorphic vision processing task, in which the networks regress angular velocity given event based input. We find that both explicit recurrency and delays improve the prediction accuracy of the SNN, compared to having just spiking neuronal dynamics. Analysis of the inner workings of the networks shows that the threshold and reset mechanism of spiking neurons play an important role in allowing longer neuron timescales (lower membrane leak). Forgetting (at the right time) turns out to play an important role in memory. Additionally, it becomes apparent that optimizing an SNN with explicit recurrent connections or learnable delays does not lead to the formation of robust spiking neuronal dynamics. In fact, spiking neuronal dynamics are largely ignored, as after optimization virtually no input current is integrated onto the membrane potential in these cases. Instead, we consistently find that a recurrent SNN prefers to build a state solely with the explicit recurrent connections, while an SNN with delays prefers to just use the delays. Therefore, our SNNs with explicit recurrent connections and delays are in fact better described as binary activated RNNs and ANNs, respectively.},
	author    = {Lammers, Laurens },
	keywords  = {Spiking Neural Networks; SNN; Event-based vision; Memory; Recurrent Connections; Delays; Supervised Learning; Surrogate Gradient},
	note      = {Hagenaars, J.J. (mentor); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Memory Mechanisms in Spiking Neural Networks},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:e52f0ee0-a859-4177-80e4-268dfd65deca},
	year      = {2023}
}

@mastersthesis{uuid:232b5015-df70-424b-91ab-149ed4d8416a,
	abstract  = {class="MsoNormal" style="margin-bottom:0cm;line-height:normal">Executing quadrotor trajectories accurately and therefore safely is a challenging task. State-of-the-art adaptive controllers achieve impressive trajectory tracking results with slight performance degradation in varying winds or payloads, but at the cost of computational complexity. Requiring additional embedded computers onboard, adding weight and requiring power. Given the limited computational resources onboard, a trade-off between accuracy and complexity must be considered. To this end, we implement "Neural-Fly" a lightweight adaptive neural controller to adapt to propeller damage, a common occurrence in real-world flight. The adaptive neural architecture consists of two components: (I) offline learning of a condition invariant representation of the aerodynamic forces through Deep Neural Networks (II) fast online adaptation to the current propeller condition using a composite adaptation law. We deploy this flight controller fully onboard the flight controller of the Parrot Bebop 1,showcasing its computational efficiency. The adaptive neural controller improves tracking performance by ≈60% over the nonlinear baseline, with minimal performance degradation of just ≈20% with increasing propeller damage.},
	author    = {Villanueva Aguado, Mauro },
	keywords  = {Adaptive Control; Quadrotor; Trajectory tracking; Propeller Damage; Neural Networks},
	note      = {de Wagter, C. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {An Adaptive Neural Network Quadrotor Trajectory Tracking Controller Tolerant to Propeller Damage},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:232b5015-df70-424b-91ab-149ed4d8416a},
	year      = {2023}
}

@mastersthesis{uuid:6b397485-750f-4e97-99ec-536ae2933d60,
	abstract  = {Fixed-wing aircraft fly longer, faster, and further than rotorcraft, but cannot take off or land vertically. Hybrid drones combine VTOL with a wing for forward flight, but the hovering system generally makes them less efficient than a pure fixed-wing. We propose an alternative, in which a rotorcraft is used to assist the fixed-wing UAV with the VTOL portions of the flight. This paper takes the first steps towards this alternative by developing and testing an overactuated rotorcraft that can autonomously dock onto a target at fixed-wing velocities. The control system uses Incremental Non-Linear Dynamic Inversion Control (INDI) to achieve linear accelerations with lateral and longitudinal motors, enabling robust horizontal control independent of attitude. A relative guidance algorithm for the docking approach path is presented, along with a vision sensing approach using ArUco markers and IR LEDs. Successful docking and separation were achieved in the wind tunnel at speeds of up to $15$m/s.},
	author    = {Laffita van den Hove d'Ertsenryck, Jonathas },
	keywords  = {},
	note      = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Rigid airborne docking between a fixed-wing UAV and an over-actuated multicopter},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:6b397485-750f-4e97-99ec-536ae2933d60},
	year      = {2023}
}

@mastersthesis{uuid:5135b7b8-3c4c-46cf-a0cc-e2fbf6da5fff,
	abstract  = {},
	author    = {Lodder, Erwin },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Stroobants, S. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Neuro-evolution learned neuromorphic control for a vision-based 3D landing},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:5135b7b8-3c4c-46cf-a0cc-e2fbf6da5fff},
	year      = {2023}
}

@mastersthesis{uuid:5d786e19-6871-4478-bda8-43f7cab20633,
	abstract  = {Pitot tube-free airspeed estimation methods exist for fixed-wing and multirotor configurations, but lack direct applicability to hybrid unmanned air vehicles due to their wide flight envelope and changing dynamics during transition. This work proposes a novel synthetic air data system for the Variable Skew Quad Plane (VSQP) hybrid vehicle to allow airspeed estimation from hover to high speed forward flight and provide pitot tube fault detection. An Extended Kalman Filter fuses Global Navigation Satellite System (GNSS) and inertial measurements using model-independent kinematics equations to estimate wind and airspeed without the use of the pitot tube. The filter is augmented by a simplified vehicle force model. Pitot tube fault detection is achieved with a simple thresholding operation on the pitot tube measurement and the airspeed estimation residual. Accurate airspeed estimation was validated with logged test flight data, achieving an overall 1.62 m/s root mean square error. Using the airspeed estimation, quick detection (0.16 s) of a real-life abrupt pitot tube fault was demonstrated. This new airspeed estimation method provides an innovative approach for increasing the fault tolerance of the VSQP and similar quad-plane vehicles.},
	author    = {Larocque, Frédéric },
	keywords  = {variable skew quad plane; synthetic air data system; airspeed; Hybrid vehicles; Extended Kalman Filter; pitot tube; Fault Detection},
	note      = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); De Ponti, T.M.L. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
	title     = {Synthetic Air Data System for Pitot Tube Failure Detection on the Variable Skew Quad Plane},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:5d786e19-6871-4478-bda8-43f7cab20633},
	year      = {2023}
}

@mastersthesis{uuid:35f8de9d-98a3-457e-8f02-33d2a40de595,
	abstract  = {},
	author    = {Ronsse, Louis },
	keywords  = {},
	note      = {Hamaza, S. (mentor); Dransfeld, C.A. (mentor); van Oosterom, S.J.M. (graduation committee); Jigjid, K. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; De Vusser, Mathis },
	title     = {Design of an Aerial-Aquatic Inspection Drone},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:35f8de9d-98a3-457e-8f02-33d2a40de595},
	year      = {2023}
}

@mastersthesis{uuid:99e01573-9ec5-4c67-8b65-e266ad83098a,
	abstract  = {},
	author    = {Çelebi, Doruk },
	keywords  = {},
	note      = {Remes, B.D.W. (mentor); Giovanardi, Bianca (graduation committee); Westerbeek, S.H.J. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; de Bruijn, Marnix },
	title     = {Maritime Drone Swarm},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:99e01573-9ec5-4c67-8b65-e266ad83098a},
	year      = {2023}
}

@mastersthesis{uuid:3fcc6230-86fc-4848-a6ed-16dd46fea640,
	abstract  = {Due to rising energy prices, an increasing number of households are experiencing difficulties with the affordability of their energy bills. As a result, households are unable to heat or cool their homes, or use electrical appliances as desired. This is known as energy poverty. This research focuses on energy poverty within housing associations. As two-thirds of households experiencing energy poverty live in housing association homes, this research is specifically targeted at housing associations. The research examines the possible gap between what housing associations are doing to combat energy poverty for their tenants, and what tenants would like to see housing associations do for them. Since renovation is simply too expensive and takes several years, it is excluded from consideration. As a result, housing associations will need to take other measures to help their tenants. This research will look at these taken measures and provides recommendations to housing associations to reduce and possibly solve the gap between what they can do and what tenants want to happen. The main question of this thesis is: What can housing associations do to close the gap between them and their tenants in the social housing sector regarding combating energy poverty?<br/>This research will be carried out based on a qualitative study in which literature will be reviewed, and housing associations and tenant organisations will be interviewed. The aim is to identify the gap between what is desired by tenants and capable of housing associations and to draw up recommendations for housing associations to assist their tenants as well as possible. The recommendations of the research indicate that many of the gaps found during the comparison of the focus groups have to do with communication, both improving communication itself, and setting up communication between tenants and the association to reduce energy poverty.<br},
	author    = {Cairo, Marvin },
	keywords  = {Energy Poverty; housing association; tenants},
	note      = {Hoekstra, J.S.C.M. (mentor); Qian, QK (mentor); Croon, T.M. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Architecture and the Built Environment},
	title     = {Energy poverty, bridging the gap between housing association and tenant: What measures housing associations can take to aid their tenants who are struggling with energy poverty},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:3fcc6230-86fc-4848-a6ed-16dd46fea640},
	year      = {2023}
}

@mastersthesis{uuid:ef354713-924e-4907-a44f-95b67efa638e,
	abstract  = {Although deep reinforcement learning (DRL) is a highly promising approach to learning robotic vision-based control, it is plagued by long training times. This report introduces a DRL setup that relies on self-supervised learning for extracting depth information valuable for navigation. Specifically, a literature study is conducted to investigate the effects of learning how to synthesize one view from the other in a stereo-vision setup without relying on any preliminary knowledge of the camera extrinisics and how it can be integrated for its downstream use for an obstacle avoidance task. As such, the literature study concludes that competitive geometry-free monocular-to-stereo image view synthesis is feasible due to recent developments in computer vision. The scientific paper further develops concepts proposed in the literature study and benchmarks the proposed architectures on depth estimation benchmarks for KITTI. Competitive results are achieved for view synthesis and despite sub-optimal performance compared to state-of-the-art monocular depth estimation, an ability to encode depth and detect shapes is present and, therefore, satisfactory for the application to DRL. Additionally, the research examines the benefits of using the latent space of a view synthesis architecture compared to other feature extractor methods as an input to the PPO agent implemented as auxiliary tasks. This method achieves quicker convergence and better performance for an obstacle avoidance task in a simulated indoor environment than the autoencoding feature extractor and end-to-end DRL methods. It is only outperformed by the monocular depth estimation feature extractor method. Overall, this research provides valuable insights for developing more efficient and effective DRL methods for monocular camera-based drones. Finally, the complementary code for this research can be found: \url{https://github.com/ldenridder/drl-obstacle-avoidance-view-synthesis}.},
	author    = {den Ridder, Luc },
	keywords  = {Autonomous Navigation; UAV; Deep Reinforcement Learning; Self-supervised learning; Auxiliary tasks; Monocular Vision; Depth Estimation; Feature Extraction; simulation},
	note      = {de Croon, G.C.H.E. (mentor); Wu, Y. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Improving DRL Of Vision-Based Navigation By Stereo Image Prediction},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:ef354713-924e-4907-a44f-95b67efa638e},
	year      = {2023}
}

@mastersthesis{uuid:2557c822-2360-4d2c-a6e9-0e05182c5c15,
	abstract  = {Unmanned Aerial Vehicles (UAVs) are increasingly being used in various applications, which demand longer endurance, extended range, and high maneuverability. These requirements necessitate the development of effective control methods for Hybrid UAVs. In this paper, we propose an outer loop Incremental Nonlinear Dynamic Inversion (INDI) controller for Hybrid UAVs, based on an analytically derived control effectiveness to control the linear acceleration of the UAV. The control effectiveness is derived in a new frame that does not show singularities, technically allowing controlled flight at all attitudes. For trajectory tracking purposes, a Proportional Derivative (PD) controller is added. In simulation the proposed controller shows comparable results to already existing INDI controllers for hover and forward flight. When performing loop the loops it is shown that the proposed control system is able to handle high roll angles, while the already existing INDI controller crashed.},
	author    = {Engelen, Koen },
	keywords  = {},
	note      = {Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Aerobatic maneuvering of Autonomous Hybrid UAVs: Trajectory Tracking using INDI in the Control Frame},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:2557c822-2360-4d2c-a6e9-0e05182c5c15},
	year      = {2023}
}

@mastersthesis{uuid:9b27d79d-d876-466d-b980-562c03552e6b,
	abstract  = {Prolonging the endurance of fixed-wing UAVs is crucial for achieving complex missions, yet their limited battery life poses a significant challenge. In response, this research proposes a novel approach to extend the endurance of fixed-wing UAVs by enabling autonomous soaring in an orographic wind field. The goal of our research is to develop a controller that can identify feasible soaring regions and autonomously maintain position control without using any throttle. Soaring flight is desirable as it results in a low energy cost with zero throttle usage. However, without throttle usage, the longitudinal motion of the UAV is an under-actuated system, presenting control challenges. The concept of a target gradient line (TGL) is introduced as part of the control algorithm that addresses these challenges and autonomously finds the equilibrium soaring position where sink rate and updraft are in equilibrium. Experimental tests showed promising results, demonstrating the controller’s effectiveness in maintaining autonomous soaring flight in a non-static wind field. We also demonstrate a single degree of control freedom in the soaring position through manipulation of the TGL.},
	author    = {Suys, Tom },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Remes, B.D.W. (mentor); Hwang, S. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Autonomous Control for Orographic Soaring of Fixed-Wing UAVs},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:9b27d79d-d876-466d-b980-562c03552e6b},
	year      = {2023}
}

@mastersthesis{uuid:fd5e484b-bdd4-42e7-8cdc-70de94462858,
	abstract  = {Aerial physical interaction opens the door for many operations at height to be automatised using aerial robots. This research presents a novel manipulator design mounted on a traditional quadrotor, which utilises both mechanical and software compliance to perform physical interaction on vertical walls and overhanging surfaces, such as those found under bridges. A centralised impedance control scheme allows direct control of the end-effector pose without needing separate modes for free-flight and contact. A spring-loaded prismatic joint provides passive compliance while doubling as a force-feedback for the impedance controller through measuring the spring displacement. Simulation and flight experiments prove the feasibility and robustness of this approach for exchanging high forces at height, with a total of 44 successful experiments carried out in four sets. An average maximum force of 5.66 N or 19.3\% of the system's weight was achieved over one set of 11 experiments.},
	author    = {Brummelhuis, Martijn },
	keywords  = {Aerial Manipulation; Aerial Physical Interaction; Impedance control; Centralised modeling; UAV; ROS; Drone; Force exertion},
	note      = {Hamaza, S. (mentor); Smeur, E.J.J. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {A Centralised Approach to Aerial Manipulation on Overhanging Surfaces},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:fd5e484b-bdd4-42e7-8cdc-70de94462858},
	year      = {2023}
}

@mastersthesis{uuid:efa9ee47-b200-4a52-b61d-d2c8e5b6fb78,
	abstract  = {Reaching fast and autonomous flight requires computationally efficient and robust algorithms. To this end, we train Guidance &amp; Control Networks to approximate optimal control policies ranging from energy-optimal to time-optimal flight. We show that the policies become more difficult to learn the closer we get to the time-optimal ’bang-bang’ control profile. We also assess the importance of knowing the maximum angular rotor velocity of the quadcopter and show that over- or underestimating this limit leads to less robust flight. We propose an algorithm to identify the current maximum angular rotor velocity onboard and a network that adapts its policy based on the identified limit. Finally, we extend previous work on Guidance &amp; Control Networks by learning to take consecutive waypoints into account. We fly a 4×3m track in similar lap times as the differential-flatness-based minimum snap benchmark controller while benefiting from the flexibility that Guidance &amp; Control Networks offer.},
	author    = {Origer, Sebastien },
	keywords  = {},
	note      = {de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Izzo, Dario (mentor); Ferede, R. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Guidance &amp; Control Networks for Time-Optimal Quadcopter Flight},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:efa9ee47-b200-4a52-b61d-d2c8e5b6fb78},
	year      = {2023}
}

@mastersthesis{uuid:bffb47bf-5864-4b18-921b-588b3a664866,
	abstract  = {An effective distribution of flight control commands over many aircraft actuators (engines, control surfaces, flaps, etc.) can be achieved with constrained optimisation. Active-Set methods solve these problems efficiently, but their computational time requirements are still prohibitive for aircraft with many actuators or slower digital flight control processors. This work shows how these methods can be improved in these regards, by updating the required matrix factorisations at lower computational costs, rather than solving a separate optimisation problem at every step of the iterative algorithm. Additionally, it is shown how the sparsity of the problem matrices can be exploited. Both open-loop simulations and flight tests have been performed, which show that worst-case timings for a 6-rotor multicopter UAV can be improved by 65% over a current Active-Set solver. Furthermore, methods are presented that remedy numerical stability issues occurring in micro-controller floating point arithmetic but introduce a small but measurable adverse effect on the flight behaviour.},
	author    = {Blaha, Till },
	keywords  = {Control Allocation; Quadratic Optimisation; Active-Set algorithm},
	note      = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Computationally Efficient Control Allocation Using Active-Set Algorithms},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:bffb47bf-5864-4b18-921b-588b3a664866},
	year      = {2023}
}

@mastersthesis{uuid:3e922cc4-83a0-43aa-a223-a67e554d2e92,
	abstract  = {Decentralised drone swarms need real time collision avoidance, thus requiring efficient, real time relative localisation. This paper explores different data inputs for vision based relative localisation. It introduces a novel dataset generated in <i>Blender</i>, providing ground truth optic flow and depth. Comparisons to <i>MPI Sintel</i>, an industry/research standard optic flow dataset, show it to be a challenging and realistic dataset. Two Deep Neural Network (DNN) architectures (YOLOv3 &amp; U-Net) were trained on this data, comparing optic flow to colour images for relative positioning. The results indicate that using optic flow provides a significant advantage in relative localisation. The flow based YOLOv3 had an mAP of 48%, 9% better than the RGB based YOLOv3, and 23% better than its equivalent U-Net. Its IoU<sub>0.5</sub> of 63% was also 14% better than the RGB based YOLOv3, and 51% than its equivalent U-Net. As an input, it generalises better than RGB, as test clips with variant drones show. For these variants, the optical flow based networks outperformed the RGB based networks by a factor of 10.},
	author    = {Mink, Raoul },
	keywords  = {Deep learning; drone detection; dataset creation},
	note      = {de Croon, G.C.H.E. (mentor); de Wagter, C. (graduation committee); Zarouchas, D. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Deep Vision-based Relative Localisation by Monocular Drones},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:3e922cc4-83a0-43aa-a223-a67e554d2e92},
	year      = {2023}
}

@mastersthesis{uuid:a4f3199c-71f6-4182-bd98-30db62db8018,
	abstract  = {Aerial platforms designed for water jet placement are gaining interest in the areas of fire-fighting, washing, and irrigation. A novel, lightweight, and simplistic design is proposed that reduces the number of actuators and limits ineffective water discharge. External camera feedback was used for position control as a first step towards autonomous flight. An initial prototype of an unmanned hydro-propelled aerial vehicle (UHAV) connected to a water hose was designed and fabricated. Flight tests were conducted to show that attitude control with uniaxial thrust-vectoring of two nozzles was impossible due to undamped vibrations and coupling effects. By redesigning the PID controller, pitch rate damping was accomplished. Furthermore, a design trade-off led to the introduction of a canting keel to reduce bank-yaw coupling effects due to asymmetric nozzle deflections. Flight tests proved that the iterated design with a hose length of 3m was capable of disturbance rejection and setpoint tracking. An external camera was used to show that the Lucas-Kanade optical flow algorithm and the implementation of the YOLOv5 segmentation model can be used for positional water jet placement. By increasing the pitch rate damping, improving the water jet detection algorithm and implementing a cost function for water discharge at the area of interest, autonomous missions can be flown in the future.},
	author    = {van Beurden, Xander },
	keywords  = {},
	note      = {de Wagter, C. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Stability control and positional water jet placement for a novel tethered unmanned hydro-propelled aerial vehicle using real-time water jet detection},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:a4f3199c-71f6-4182-bd98-30db62db8018},
	year      = {2023}
}

@mastersthesis{uuid:90916a92-95bc-44eb-889e-81555ddd494f,
	abstract  = {This project proposes and evaluates a novel concept for an airspeed instrument aimed at small hybrid unmanned aerial vehicles. The working principle is to relate the power spectra of the wall-pressure fluctuations beneath the turbulent boundary layer formed over the vehicle’s body to its airspeed. The instrument consists of two microphones, flush mounted on the UAV’s nose cone, that capture the pseudo-sound caused by the coherent turbulent structures, and a micro-controller that processes the signals from the microphones and computes the airspeed. Dedicated models were constructed, using data obtained from wind tunnel and flight experiments, that take the power spectra of the microphones’ signals as an input and provide the airspeed as an output. The model structure is a feed-forward neural network with a single hidden layer, trained using a second-order gradient descent algorithm, following a supervised learning approach. The models were validated using only flight data, with the best one achieving a mean approximation error of 0.043 m/s and having a standard deviation of 1.039 m/s. It was also shown that the airspeed could be successfully predicted for a wide range of angles of attack, given that they are known, thus necessitating the vehicle to be equipped with a dedicated angle of attack sensor.},
	author    = {Makaveev, Momchil },
	keywords  = {Tail-sitter; Airspeed; Hydrodynamic Pressure Fluctuations; Pseudo-sound; Turbulent boundary layer; Microphones; Power Spectral Density; Artificial Neural Networks},
	note      = {Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Microphones as Airspeed Sensors for Micro Air Vehicles},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:90916a92-95bc-44eb-889e-81555ddd494f},
	year      = {2023}
}

@mastersthesis{uuid:f5c8a6b4-a43b-43f1-9a2d-72d850515369,
	abstract  = {We present a computationally cheap 3D bug algorithm for drones, using stereo vision. Obstacle avoidance is important, but difficult for robots with limited resources, such as drones. Stereo vision requires less weight and power than active distance measurement sensors, but typically has a limited Field of View (FoV). In addition, the stereo camera is fixed on the drone, preventing sensor movement. For obstacle avoidance, bug algorithms require few resources. We base our proposed algorithm, Frustumbug, on the Wedgebug algorithm, since this bug algorithm copes with a limited FoV. Since Wedgebug only focuses on 2D problems, the Local-epsilon-Tangent-Graph (LETG) is used to extend the path planning to 3D. Disparity images are obtained through an optimised stereo block matching algorithm. Obstacles are expanded in disparity space to obtain the configuration space. Furthermore, Frustumbug has an improved robustness to noisy range sensor data, and includes reversing, climbing and descending manoeuvres to avoid or escape local minima. The algorithm has been extensively tested with 225 flights in two challenging simulated environments, with a success rate of 96%. Here, 3.6% did not reach the goal and 0.4% collided. Frustumbug has been implemented on a 20 gram stereo vision system, and guides drones safely around obstacles in the real world, showing its potential for small drones to reach their targets fully autonomously.},
	author    = {Meester, Ruben },
	keywords  = {Stereo vision; obstacle avoidance; Drones},
	note      = {de Croon, G.C.H.E. (mentor); van Dijk, Tom (mentor); de Wagter, C. (graduation committee); Verhoeven, C.J.M. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Frustumbug: a 3D Mapless Stereo-Vision-based Bug Algorithm for Micro Air Vehicles},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:f5c8a6b4-a43b-43f1-9a2d-72d850515369},
	year      = {2023}
}

@mastersthesis{uuid:4e100997-a5b3-4863-a312-4721296fcdba,
	abstract  = {Flying-wings show great potential for a vast number of applications, in both commercial and military sectors, thanks to their long range and fast forward flight, but suffer due to their lack of vertical take-off and landing capabilities. This paper presents a proof of concept for a novel landing method for a conventional flying wing that does not introduce additional weight dedicated only to the landing phase, with the aim of controlling a deep-stalled flying-wing in a powered flat spin. Through cyclic actuation of the servo motors and elevons, lateral forces as well as moments can be generated to control the position and attitude of the rotation plane. A successful indoor experiment was performed with a modified Parrot Disco in a controlled environment. Outdoor tests, however, failed to replicate the indoor results due to additional challenges present in the real flight conditions. A number of key challenges were identified, and the insights gained in this research lay an initial foundation for future work on this topic.},
	author    = {Barbera, Matteo },
	keywords  = {flying wing; Deep stall; samara; flat spin; control algorithm; proof-of-concept; MAV; UAV; drone; landing},
	note      = {de Wagter, C. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Towards landing a deep-stalled flying-wing in a powered flat spin: a proof of concept},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:4e100997-a5b3-4863-a312-4721296fcdba},
	year      = {2022}
}

@mastersthesis{uuid:1136170f-3c4b-43b8-8b43-09e1e52d3bfd,
	abstract  = {Relative localization (RL) is essential for the successful operation of micro air vehicle (MAV) swarms. Achieving accurate 3-D RL in infrastructure-free and GPS-denied environments with only distance information is a challenging problem that has not been satisfactorily solved. In this work, based on the range-based peer-to-peer RL using the ultra-wideband (UWB) ranging technique, we develop a novel UWB-based cooperative relative localization (CRL) solution which integrates the relative motion dynamics of each host-neighbor pair to build a unified dynamic model and takes the distances between the neighbors as bonus information. Observability analysis using differential geometry shows that the proposed CRL scheme can expand the observable subspace compared to other alternatives using only direct distances between the host agent and its neighbors. In addition, we apply the kernel-induced extended Kalman filter (EKF) to the CRL state estimation problem with the novel-designed Logarithmic-Versoria (LV) kernel to tackle heavy-tailed UWB noise. Sufficient conditions for the convergence of the fixed-point iteration involved in the estimation algorithm are also derived. Comparative Monte Carlo simulations demonstrate that the proposed CRL scheme combined with the LV-kernel EKF significantly improves the estimation accuracy owing to its robustness against both the measurement outliers and incorrect measurement covariance matrix initialization. Moreover, with the LV kernel, the estimation is still satisfactory when performing the fixed-point iteration only once for reduced computational complexity.},
	author    = {Liu, Changrui },
	keywords  = {Cooperative Localization; Relative Localization; Ultra-wideband; Kernel method; Kalman Filtering; Observability},
	note      = {de Croon, G.C.H.E. (mentor); Pfeiffer, S.U. (mentor); Mazo, M. (graduation committee); de Wagter, C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Cooperative Relative Localization in MAV Swarms with Ultra-wideband Ranging},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:1136170f-3c4b-43b8-8b43-09e1e52d3bfd},
	year      = {2022}
}

@mastersthesis{uuid:baf5b7df-0e0f-45da-8b70-c7c95ead79b6,
	abstract  = {By combining the ability to hover with a wing for fast and efficient horizontal flight, hybrid unmanned aircraft extend the flight envelope and therefore mission capabilities of unmanned aircraft. However, this comes at a cost: increased complexity control-wise and being more susceptible to wind disturbances. This susceptibility to wind gusts is particularly problematic for tailsitters as during hovering and vertical flight their wing is perpendicular to horizontal wind disturbances, often leading to actuator saturation. This paper presents a novel tailsitter micro air vehicle with two leading edge tilting rotors serving as its only actuators. It is shown that thrust vectoring generates sufficient control moment generation alleviating actuator saturation. Incremental nonlinear dynamic inversion (INDI) is implemented for attitude control and is demonstrated to compensate for unmodeled forces and moments whilst only relying on actuator control effectiveness and knowledge of actuator dynamics.},
	author    = {Lovell-Prescod, Gervase },
	keywords  = {UAV; Hybrid MAVs; Tailsitter; Actuator saturation; Incremental control; INDI; Tilting rotors},
	note      = {Smeur, E.J.J. (mentor); Ma, Z. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Attitude Control of a Tilt-rotor Tailsitter Micro Air Vehicle Using Incremental Control},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:baf5b7df-0e0f-45da-8b70-c7c95ead79b6},
	year      = {2022}
}

@mastersthesis{uuid:1caff7b3-5c17-4b80-abea-19c629ce6051,
	abstract  = {Event based vision has recently attracted a lot of attention. High data rates and robustness to lighting variations make it a valid option for indoor navigation. The previously developed FAITH algorithm calculates a possible Focus of Expansion<br/>area based on negative half-planes generated by optic flow and by employing a RANSAC search, a fast and reliable Focus of Expansion estimation can be performed. This paper builds upon this algorithm by verifying and validating the<br/>algorithm, improving the derotation capabilities and optimising for computational efficiency. Compared to earlier work, a higher accuracy and an increased robustness are realised by improving the data handling. Simulator results show accuracies in the range of 2 to 5 degrees. Online testing on a drone shows accuracies of up to 5 degrees while obtaining calculation times of only<br/>2 · 10−3s and rates of 140Hz. Comparing the method to an alternative shows higher accuracy and better suitability to normal flow. Further research may contribute to more stable results and explore different hardware solutions.},
	author    = {Knoops, Stefan },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Verification &amp; Validation of Focus of Expansion estimation algorithm employing event-based optic flow},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:1caff7b3-5c17-4b80-abea-19c629ce6051},
	year      = {2022}
}

@mastersthesis{uuid:574db806-6096-4600-9926-3d737d1ee7da,
	abstract  = {Nano quadcopters are small, agile, and cheap platforms well suited for deployment in narrow, cluttered environments. Due to their limited payload, nano quadcopters are highly constrained in processing power, rendering conventional vision-based methods for autonomous navigation incompatible. Recent machine learning developments promise high-performance perception at low latency, while novel ultra-low power microcontrollers augment the visual processing power of nano quadcopters. In this work, we present NanoFlowNet, an optical flow CNN that, based on the semantic segmentation architecture STDC-Seg, achieves real-time dense optical flow estimation on edge hardware. We use motion boundary ground truth to guide the learning of optical flow, improving performance with zero impact on latency. Validation on MPI-Sintel shows the high performance of the proposed method given its constrained architecture. We implement the CNN on the ultra-low power GAP8 microcontroller and demonstrate it in an obstacle avoidance application on a 34 g Bitcraze Crazyflie nano quadcopter.},
	author    = {Bouwmeester, Rik },
	keywords  = {MAV; CNN; edge AI; optical flow},
	note      = {de Croon, G.C.H.E. (mentor); Paredes-Vallés, Federico (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {NanoFlowNet: Real-time optical flow estimation on a nano quadcopter},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:574db806-6096-4600-9926-3d737d1ee7da},
	year      = {2022}
}

@mastersthesis{uuid:a4305f40-c095-45c2-bc7e-84e23efa70d6,
	abstract  = {Abstract— Stereo vision is a commonly applied method to achieve depth perception on Micro Air Vehicles (MAVs). Stereo matching algorithms are often optimized for specific environments and camera properties, using the ground truth error as a supervisor. However, in practical applications ground truth data is usually not available. Therefore, in this research, we finetune several conventional stereo matching algorithms (BM, SGBM, and ELAS) and a neural network (AnyNet) using self-supervision. The settings of the conventional algorithms are optimized with NSGA-II, using the reconstruction error and disparity density as objective functions. AnyNet is finetuned with the reconstruction error, as well as with the disparity map of conventional methods. We conclude that finetuning the parameters of conventional stereo algorithms using the reconstruction error can lead to a slight improvement in performance compared with the general settings, depending on the stereo algorithm. The performance of the conventional methods is comparable to that of AnyNet on a major portion of the image. However, removing the values with low confidence in the disparity map of ELAS and interpolating the missing disparities leads to an accuracy well above AnyNet.},
	author    = {Stikker, Roelof },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Pfeiffer, S.U. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Self-supervised finetuning of stereo matching algorithms},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:a4305f40-c095-45c2-bc7e-84e23efa70d6},
	year      = {2022}
}

@mastersthesis{uuid:66c34a84-5b47-49dd-b560-2836d9696e3c,
	abstract  = {Incremental Nonlinear Dynamics Inversion (INDI) flight controllers are sensor-based control systems, that are robust towards model uncertainty and with good disturbance rejection characteristics. These controllers show coupling effects in structural modes when implemented in specific flying vehicles with low-frequency structural motions. This paper investigates different INDI implementations, standard INDI, hybrid INDI, and notch filter placement in the INDI loop via simulation and flight tests on the Nederdrone. System identification of the structural characteristics of the vehicle and the system’s yaw dynamics are executed via ground vibration and hover flight tests. Closed-loop behaviour of theINDI inner-loop, disturbance rejection performance, and outer loop step-tracking performance was assessed with dedicated flight tests. The investigated INDI solutions show similar disturbance rejection and outer-loop step tracking performance, while the hybrid INDI solution performs a better nonlinear dynamic inversion. <br/>Index Terms—INDI, complementary filter, unmanned vehicle, flight control system structural motion coupling},
	author    = {Collicelli, Alessandro },
	keywords  = {},
	note      = {Smeur, E.J.J. (mentor); Pollack, T.S.C. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Incremental Nonlinear Dynamic Inversion controller - structural vibration coupling: Study of the phenomenon and the existing solutions},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:66c34a84-5b47-49dd-b560-2836d9696e3c},
	year      = {2022}
}

@mastersthesis{uuid:4e4e333d-643f-43b9-99cb-650d697f5baa,
	abstract  = {In this work, we present the ADAPT, a novel reconfigurable force-balanced parallel manipulator with pantograph legs for spatial motions applied underneath a drone. The reconfigurable aspect allows different motion-based 3-DoF operation modes like translational, rotational, mixed, planar without disassembly. For the purpose of this study, the manipulator is used in translation mode only. A kinematic model is developed and validated for the manipulator. The design and motion capabilities are also validated both by conducting dynamics simulations of a simplified model on MSC ADAMS, and experiments on the physical setup.<br/>The force-balanced nature of this novel design decouples the motion of the manipulator’s end-effector from the base, zeroing the reaction forces, making this design ideally suited for aerial manipulation in unmanned aerial vehicles (UAVs) applications, or generic floating-base applications.},
	author    = {SURYAVANSHI, KARTIK },
	keywords  = {Reactionless Force Balancing; Configurable Robot; Mechanism Design; Parallel Robot; Aerial Manipulation},
	note      = {van der Wijk, V. (mentor); Hamaza, S. (mentor); Herder, J.L. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering},
	title     = {ADAPT: A 3 Degrees of Freedom Reconfigurable Force Balanced Parallel Manipulator for Aerial Applications},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:4e4e333d-643f-43b9-99cb-650d697f5baa},
	year      = {2022}
}

@mastersthesis{uuid:6215dd57-8d16-466b-a286-341538675d2d,
	abstract  = {Flapping wing micro aerial vehicles (FWMAVs) are known for their flight agility and maneuverability. However, their in-gust flight performance and stability is still inferior to their biological counterparts. To this end, a simplified in-gust dynamic model, which could capture the main gust effects on FWMAVs, has been identified with real in-gust flights' data of a FWMAV, the Flapper Drone. Based on this model, an adaptive position and velocity controller was proposed with gain scheduling and implemented for in-gust flights under gust speeds up to 2.4 m/s. With this airflow-sensing based adaptive controller, the in-gust hovering stability of the Flapper Drone has been improved when the gust's intensity and frequency changes, comparing with the original fixed-gain cascaded PID controller case.},
	author    = {Wang, Chenyao },
	keywords  = {DelFly; Bio-inspired Aerial Robotics; Flapping Wing MAV; Modeling; Adaptive Control; In-gust Flights; Onboard Airflow Sensing},
	note      = {Hamaza, S. (mentor); de Croon, G.C.H.E. (graduation committee); Wang, S. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
	title     = {A Bio-inspired Sensing Approach to in-Gust Flight of Flapping Wing MAVs},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:6215dd57-8d16-466b-a286-341538675d2d},
	year      = {2022}
}

@mastersthesis{uuid:05f743a5-39c8-4860-9976-1eee532184a9,
	abstract  = {Applications of Unmanned Aerial Vehicles (UAV's) are often limited by flight endurance. To address the limitation of endurance, we propose a regenerative soaring method in this paper. The atmospheric energy from updrafts generated by obstacles such as hills and ships can be harvested by UAV's using a regenerative electric drivetrain. With fixed-wing aircraft, the vehicle can hover with specific wind conditions, and the battery can be recharged in the air while wind hovering. In order to research the feasibility of this regenerative soaring method, we present a model to estimate hovering locations and the amount of extractable power using the proposed method. The resulting modular regeneration simulation tool can efficiently determine the possible hovering locations and provide an estimate of the power regeneration potential for each hovering location, given the UAV's aerodynamic characteristics and wind-field conditions. Furthermore, a working regenerative drivetrain test setup was constructed and characterised that showcased promising conversion efficiencies and can be incorporated into existing UAV's easily.},
	author    = {Gossye, Midas },
	keywords  = {},
	note      = {Remes, B.D.W. (mentor); Hwang, S. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Developing a modular tool to simulate regeneration power potential using orographic wind-hovering UAVs},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:05f743a5-39c8-4860-9976-1eee532184a9},
	year      = {2022}
}

@mastersthesis{uuid:41222049-fb57-4f26-9b9e-85939af9fa63,
	abstract  = {In this thesis, we develop a novel aerial manipulator system with an omni-directional workspace. The system comprises of a quadrotor platform equipped with a rotating five-bar linkage and serves the purpose of demonstrating the ability to perform contour tracing tasks on complex shapes, whilst airborne. In order to remove the dependency on additional force sensors and keep the design lightweight, an onboard force estimation scheme is implemented based on the generalized momentum of the system, using the torque feedback from the manipulator's motors. The computed force estimate feeds in a position-based impedance controller with the purpose of maintaining continuous contact through the manipulator's end-effector as the system traces contours of unknown curved geometry. Results demonstrate the estimator's ability to track the applied forces, while the impedance controller shows adequate contour following. The preliminary results obtained on both stationery and flight experiments validate this approach and show potential for aerial contact inspections of more complex structures.},
	author    = {Abu-Jurji, Hani },
	keywords  = {},
	note      = {Hamaza, S. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Sensorless Impedance Control for Curved Surface Inspections Using the Omni-Drone Aerial Manipulator},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:41222049-fb57-4f26-9b9e-85939af9fa63},
	year      = {2022}
}

@mastersthesis{uuid:df815057-9ab6-42ee-8290-ce8099ffda68,
	abstract  = {This paper presents the design of an Incremental Nonlinear Dynamic Inversion (INDI) controller for the novel platform VSQP. Part of the identified challenges is the develop- ment of a model for the actuator effectiveness and lift especially as a function of skew, the newly added degree of freedom. In particular it is assumed that the actuator effectiveness changes linearly with actuator state and that aerodynamic forces change quadratically with airspeed and depend mainly on the chordwise component of airspeed. Moreover, the position of the moving actuators is expressed as a function of the corresponding moment arm and the skew angle. The models and assumptions are verified through static and dynamic wind tunnel tests at the OJF of TuDelft. A WLS routine is used to solve the control allocation for the overactuated guidance loop. A lower cost is assigned to the use of the push motor so to steer the control allocation in its favor rather than commanding changes in attitude. A gradual switch of the hover motors in transition is achieved by scheduling the lift-pitch effectiveness with airspeed. Therefore, as airspeed increases the outerloop INDI controller evaluates that changing pitch to achieve a certain vertical acceleration set point results in an increasingly cheaper command allocation than changing thrust. An automatic skew controller is designed based on the developed control moment and lift models. The skew angle is scheduled with airspeed so to perform transition while also maximizing control authority. Finally, the controller is validated by performing multiple transitions inside the OJF windtunnel.},
	author    = {De Ponti, Tomaso },
	keywords  = {incremental nonlinear dynamic inversion, weighted least squares, variable skew quad plane, control moment modelling, lift modelling, transition},
	note      = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Incremental Nonlinear Dynamic Inversion Controller for a Variable Skew Quad Plane},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:df815057-9ab6-42ee-8290-ce8099ffda68},
	year      = {2022}
}

@mastersthesis{uuid:fdd8e2fa-1372-4f79-aa05-6ab152e848e1,
	abstract  = {In recent years the popularity of VTOL (Vertical Take-Off and Landing) drones has increased significantly. Due to their hybrid design, these drones can take off and land vertically and fly horizontally, enabling them to land in difficult terrain and have a more extensive range than the Quadcopter counterpart. However, this hybrid design also introduces complex dynamics that are difficult to model. For adequate control, this requires an adaptive element that can compensate for the modeling errors. Due to the significant change in flight conditions, adaptations must be made effectively over the entire flight envelope of a VTOL drone. This thesis introduces an adaptive controller that can cope with the large flight envelope and varying flight conditions of the VTOL drone and can adapt the controller effectively and store previous adaptations with multivariate B-splines during real-time flights.},
	author    = {Kanhai, Prawien },
	keywords  = {Adaptive control; INDI; Multivariate B-spline; VTOL},
	note      = {Smeur, E.J.J. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Adaptive control with Multivariate B-Splines and INDI: A case study for Vertical take-off and landing drones},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:fdd8e2fa-1372-4f79-aa05-6ab152e848e1},
	year      = {2022}
}

@mastersthesis{uuid:b7070c31-9db1-4a0c-8605-fb871914501b,
	abstract  = {The use of micro air vehicles (MAV) is becoming increasingly mainstream and with them their applications have become more demanding across the board. The application of MAV’s in large GNSS-denied environments often asks for a distributed and scalable localisation system with minimal reliance on static localisation hardware. In this research a distributed ultra-wideband (UWB) localisation system that takes advantage of the collaborative capabilities of a swarm of MAV’s has been developed and tested in both simulation and practice. Additionally, a modular UWB simulator has been developed which enables researchers to test UWB localisation schemes for a swarm of MAV’s. It has been found that when taking advantage of the UWB inter-agent ranging capabilities of a swarm of micro air vehicles, one can increase the coverage of an UWB setup in spaces with coverage-issues and conversely increase the accuracy of an existing UWB setup that has full UWB coverage.},
	author    = {Dupon, Fréderic },
	keywords  = {UWB; Localisation; Crazyflie; Swarm},
	note      = {de Croon, G.C.H.E. (mentor); Pfeiffer, S.U. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {UWB Localisation: Distributed UWB inter-ranging for MAV swarms in large GNSS-denied environments},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:b7070c31-9db1-4a0c-8605-fb871914501b},
	year      = {2022}
}

@mastersthesis{uuid:41895fac-aa59-47db-9c01-5e2879460b57,
	abstract  = {This paper proposes a control strategy based on incremental nonlinear dynamic inversion (INDI), meant for trajectory tracking purposes. The controller extends the conven- tional capabilities of INDI by including actuator dynamics in the inversion law and introducing a state dependent compensation term to reduce the effort of the error controller. A complementary filter is employed to reduce the degrading effect introduced by the filtering-induced delay in the feedback loop. Both simulated and real flight tests are conducted on a quadrotor configuration with artificially slowed down actuators and a drag plate mounted on top, to better observe the effect of actuator dynamics and state dependent dynamics in trajectory tracking accuracy. Simulations show that the combination of the two additional features increases tracking accuracy both in the short and long term response. It is also found that an overestimation of the state compensation term leads to instability, which makes the strategy not robust to model mismatch. Real flight tests, involving the tracking of a series of doublets on the pitch attitude and a lemniscate of Bernoulli, show that, as the complexity of the maneuver increases, the less the state compensation term effectively contributes to an improved tracking when the model is incomplete. On the other hand, trajectory tracking accuracy due to the consideration of actuator dynamics shows consistency and improvement respect to conventional INDI solutions.},
	author    = {Campolucci, Pietro },
	keywords  = {},
	note      = {Smeur, E.J.J. (mentor); Mancinelli, A. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Model and Actuator Based Trajectory Tracking for Incremental Nonlinear Dynamic Inversion Controllers},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:41895fac-aa59-47db-9c01-5e2879460b57},
	year      = {2022}
}

@mastersthesis{uuid:be142c0a-3475-4571-b9c5-9118d397c51a,
	abstract  = {In an effort to develop a new relative sensing method for drone swarms, the suitability of event cameras is assessed for propeller detection. Benchmark tests were conducted for different propellers under different lighting and background conditions, varying the observation distance and spinning frequency. The different tests were evaluated on event count, frequency, and clustering, as these are the most characteristic properties of the propeller-generated signal. A propeller detection metric was derived as a fuzzy classifier to assess detectability. It was observed that the sensor employed is limiting the detection range due to low resolution, with a maximum detection range of 75 cm. While at low spinning frequencies it is possible to detect the propeller at such distance, for higher frequences (6000 to 8000 RPMs) the range decreases to 45 cm for the tests with highest blade to background contrast and two-blade propellers. It was observed that lower contrasts reduce the successful detections only to low frequencies, and three-blade propellers become completely indetectable due to the static overlap between the blades. Therefore, it is concluded that, at this stage of the technology, the use case of event cameras for relative sensing is constrained to close distances with high contrast.},
	author    = {Barberia Chueca, Alejandro },
	keywords  = {},
	note      = {Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Onboard Drone Detection with Event Cameras},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:be142c0a-3475-4571-b9c5-9118d397c51a},
	year      = {2022}
}

@mastersthesis{uuid:3ffa7f45-8631-4224-a16b-4e2be097e35b,
	abstract  = {Event cameras and spiking neural networks (SNNs) allow for a highly bio-inspired, low-latency and power efficient implementation of optic flow estimation. Just recently, a hierarchical SNN was proposed in which motion selectivity is learned from raw event data in an unsupervised manner using spike-timing-dependent plasticity (STDP). However, real-life applications of this SNN are currently still limited by the fact that the exact choice of neuron parameters depends on the spatiotemporal properties of the input. Furthermore, tuning the network is a challenging task due to the high degree of coupling between the various parameters. Inspired by neurons in biological brains that modify their intrinsic parameters through a process called intrinsic plasticity, this research proposes update rules which adapt the voltage threshold and maximum synaptic delay during inference. This allows applying the already trained network to a wider range of operating conditions and simplifies the tuning process. Starting with a detailed parameter analysis, primary functions and undesired side effects are assigned to each parameter. The update rules are then designed in such a way as to eliminate these side effects. Unlike existing update rules for the voltage threshold, this work does not attempt to keep the firing activity of output neurons within a specific range, but instead aims to adjust the threshold such that only the correct output maps spike. In particular, the voltage threshold is adapted such that output spikes occur in no more than two maps per retinotopic location. The maximum synaptic delay is adapted such that the resulting apparent pixel velocities of the input match those of the data used during training. A sensitivity analysis is presented which illustrates the effects of newly introduced parameters on the network performance. Furthermore, the adapted network is tested on real event data recorded onboard a drone avoiding obstacles. Due to the difficulties in matching the output of the adapted SNN to the ground truth data, quantitative results are inconclusive. However, qualitative results show a clear improvement in both the density and correctness of optic flow estimates.},
	author    = {Eggers, Yvonne },
	keywords  = {Optic flow estimation; spiking neural networks; neuron adaptation; computer vision; dynamic vision sensors; intrinsic plasticity; spike-timing dependent plasticity},
	note      = {de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Intrinsic Plasticity for Robust Event-Based Optic Flow Estimation},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:3ffa7f45-8631-4224-a16b-4e2be097e35b},
	year      = {2022}
}

@mastersthesis{uuid:b43a9703-082c-47c7-a56e-d50794ee8c1c,
	abstract  = {Developing optimal controllers for aggressive high speed quadcopter flight remains a major challenge in the field of robotics. Recent work has shown that neural networks trained with supervised learning are a good candidate for real-time optimal quadcopter control. In these methods, the networks (termed G\&amp;CNets) are trained using optimal trajectories obtained from a dynamical model of the quadcopter by means of a direct transcription method. A major problem with these methods is the effects of unmodeled dynamics. In this work we identify these effects for G\&amp;CNets trained for power optimal full state-to-rpm feedback. We propose an adaptive control strategy to mitigate the effects of unmodeled roll, pitch and yaw moments. Our method works by generating optimal trajectories with constant external moments added to the model and training a network to learn the policy that maps state and external moments to the corresponding optimal rpm command. We demonstrate the effectiveness of our method by performing power-optimal hover-to-hover flights with and without moment feedback. The flight tests show that the inclusion of this moment feedback significantly improves the controller's performance. Additionally we compare the adaptive controller's performance to a time optimal Bang-Bang controller for consecutive waypoint flight and show significantly faster lap times on a 3x4m track.},
	author    = {Ferede, Robin },
	keywords  = {},
	note      = {de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Izzo, Dario (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {An Adaptive Control Strategy for Neural Network based Optimal Quadcopter Controllers},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:b43a9703-082c-47c7-a56e-d50794ee8c1c},
	year      = {2022}
}

@mastersthesis{uuid:823d959a-17b8-4fd9-bc45-a0ace45d29ca,
	abstract  = {Insects have — over millions of years of evolution — perfected many of the systems that roboticists aim to achieve; they can swiftly and robustly navigate through different environments under various conditions while at the same time being highly energy efficient. To reach this level of performance and efficiency one might want to look at and take inspiration from how these insects achieve their feats. Currently, no dataset exists that allows bio-inspired navigation models to be evaluated over long real- life routes. We present a novel dataset containing omnidirectional event vision, frame-based vision, depth frames, inertial measurement (IMU) readings, and centimeter-accurate GNSS positioning over kilometer long stretches in and around the TUDelft campus. The dataset is used to evaluate familiarity-based insect-inspired neural navigation models on their performance over longer sequences. It demonstrates that current scene familiarity models are not suited for long-ranged navigation, at least not in their current form.},
	author    = {Verheyen, Jan },
	keywords  = {Long-range navigation; Neuromorphic systems; Event- based Camera; RGB Camera; GPS; GNSS},
	note      = {de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Insect-Inspired Visual Guidance: are current familiarity-based models ready for long-ranged navigation?},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:823d959a-17b8-4fd9-bc45-a0ace45d29ca},
	year      = {2022}
}

@mastersthesis{uuid:83ff9b16-d3bb-4ac7-aa8a-26f4a73a6e6d,
	abstract  = {Drones have been an emerging trend in the last few years. They are used in multiple industries<br/>already, from photography and videography to racing. More use cases are now being conceived,<br/>such as using drones to deliver packages and food to people at home, using drones for inspections,<br/>or even using them as rescue searching vehicles in hostile environments. Even more possibilities<br/>open up once the drones bundle their forces to create swarms. The lifting capabilities of drones are<br/>still somewhat limited, but in a swarm they might be able to lift heavy payloads. This report covers<br/>the design of a concept of a payload carrying swarm, intended to lift cargo up to 500 kg to even the<br/>top of a tall building.},
	author    = {Bracke, Aaron },
	keywords  = {},
	note      = {Smeur, E.J.J. (mentor); Eleftheroglou, N. (graduation committee); van 't Hoff, J.A. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; Džubinský, Maxim },
	title     = {DroneCrane},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:83ff9b16-d3bb-4ac7-aa8a-26f4a73a6e6d},
	year      = {2022}
}

@mastersthesis{uuid:5184c93c-fafb-4069-a6b0-75acdb42f081,
	abstract  = {},
	author    = {Xausa, Marco },
	keywords  = {},
	note      = {Dransfeld, C.A. (mentor); Hamaza, S. (mentor); Hwang, S. (mentor); Gomes de Paula, N.C. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; van der Saag, Jelmer },
	title     = {Aerial Deployment of an Autonomous Remote Sensing Network},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:5184c93c-fafb-4069-a6b0-75acdb42f081},
	year      = {2022}
}

@mastersthesis{uuid:a5713055-c4a4-4a6e-8cdc-4c2ac1e4e300,
	abstract  = {In recent years, efforts are focused on developing an acoustic based autonomous detect and avoidance system for UAVs to minimize interference with other air traffic. The purpose of this research is to study the potential of artificial neural networks for fast, grid-free acoustic source localization. A multi-layer perceptron has been trained to localize simulated white noise acoustic point sources using a converted version of the cross spectral matrix. The ANN based method shows similar localization behaviour to different frequencies as conventional beamforming. A new ANN architecture is proposed that uses the converted cross spectral matrices of multiple different frequencies as input to improve the localization accuracy. The multi input model has shown to have a mean absolute error of approximately 0.27[m]. The proposed model has also been applied on real world recording data of an aircraft flyover. The ANN based method has shown to be able to obtain a prediction within approximately 0.05[s], compared to approximately 1000-2000[s] for conventional beamforming. However, the magnitude and inconsistency of the localization error for the recording is higher compared to the simulated white noise source.},
	author    = {ten Oever, Erik },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {An artificial neural network based method for grid-free acoustic source localization using multiple input frequencies},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:a5713055-c4a4-4a6e-8cdc-4c2ac1e4e300},
	year      = {2022}
}

@mastersthesis{uuid:703d5b28-75c1-4d8b-a1a6-93510aed7b29,
	abstract  = {Unmanned Aerial Vehicles, UAVs, serve many purposes<br/>these days, such as short-range inspections<br/>and long-distance search and rescue missions. Long-distance missions can entail a search in a building. Such missions require a large aircraft for endurance and a small aircraft for manoeuvrability in a building.<br/><br/>This paper proposes a novel combination of a quadrotor and a hybrid biplane capable of joint hover, joint forward flight, and mid-air disassembly followed by separate flight. During joint flight, the quadcopter and the biplane have no intercommunication.<br/><br/>This paper covers the design of a release system and a joint control strategy. Firstly, the in-flight<br/>release is successfully tested in joint hover up to a forward pitch angle of -18 [deg]. Secondly, three control strategies for the quadrotor are compared:<br/>a proportional angular rate damper, a proportional angular acceleration damper, and constant thrust without attitude control.<br/>In all cases, the biplane uses a cascaded INDI attitude controller. Simulation and practical tests show that for intentional attitude changes, the different strategies<br/>are of minimal influence. However, the angular rate damper<br/>strategy for disturbance rejection has the lowest roll angle error and requires the smallest input command.<br},
	author    = {Schröter, Shawn },
	keywords  = {},
	note      = {Smeur, E.J.J. (mentor); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {We fly as one: Design and Joint Control of a Conjoined Biplane and Quadrotor},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:703d5b28-75c1-4d8b-a1a6-93510aed7b29},
	year      = {2022}
}

@mastersthesis{uuid:3e2b645f-5ef2-41f5-9e8f-70d64fc8b2a6,
	abstract  = {Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning on neuromorphic hardware. However, when such rules are implemented to learn different new tasks, they usually require a significant amount of work on task-dependent fine-tuning. This thesis aims to make this process easier by employing an evolutionary algorithm that evolves suitable synaptic plasticity rules for the task at hand. More specifically, we provide a set of various local signals, a set of mathematical operators, and a global reward signal, after which a Cartesian genetic programming process finds an optimal learning rule from these components. In this work, we first test the algorithm in basic binary pattern classification tasks. Then, using this approach, we find learning rules that successfully solve an XOR and cart-pole task, and discover new learning rules that outperform the baseline rules from literature.},
	author    = {LU, Jingyi },
	keywords  = {Spiking Neural Networks(SNNs)); synaptic plasticity; meta-learning; genetic programming; evolutionary algorithms; reinforcement learning},
	note      = {de Croon, G.C.H.E. (mentor); Hagenaars, J.J. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Evolving-to-Learn with Spiking Neural Networks},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:3e2b645f-5ef2-41f5-9e8f-70d64fc8b2a6},
	year      = {2022}
}

@mastersthesis{uuid:7735d01c-b4cd-4173-a584-652f269c078c,
	abstract  = {Semantic segmentation methods have been developed and applied to single images for object segmentation. However, for robotic applications such as high-speed agile Micro Air Vehicles (MAVs) in Autonomous Drone Racing (ADR), it is more interesting to consider temporal information as video sequences are correlated over time. In this work, we evaluate the performance of state-of-the-art methods such as Recurrent Neural Networks (RNNs), 3D Convolutional Neural Networks (CNNs), and optical flow for video semantic segmentation in terms of accuracy and inference speed on three datasets with different camera motion configurations. The results show that using an RNN with convolutional operators outperforms all methods and achieves a performance boost of 10.8% on the KITTI (MOTS) dataset with 3 degrees of freedom (DoF) motion and a small 0.6% improvement on the CyberZoo dataset with 6 DoF motion over the single-frame-based semantic segmentation method. The inference speed was measured on the CyberZoo dataset, achieving 321 fps on an NVIDIA GeForce RTX 2060 GPU and 30 fps on an NVIDIA Jetson TX2 mobile computer.},
	author    = {Tran, Tommy },
	keywords  = {Micro Air Vehicle; Semantic Segmentation; Deep Learning; Convolutional Neural Network; Recurrent Neural Network; Optical Flow},
	note      = {de Croon, G.C.H.E. (mentor); Xu, Y. (mentor); de Wagter, C. (graduation committee); van Gemert, J.C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Semantic Segmentation using Deep Neural Networks for MAVs},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:7735d01c-b4cd-4173-a584-652f269c078c},
	year      = {2022}
}

@mastersthesis{uuid:0fc90d7b-7aa3-4501-be7f-ac31330957b6,
	abstract  = {This paper reviews the application of grammatical evolution for the optimisation of low level parameters and high level behaviors for two drone behaviors, namely wall-following and navigation. In order to optimise these low level parameters and high level behaviors, grammatical evolution was applied to behavior trees. Grammatical evolution provided a significant improvement in the wall-following behavior of a drone, creating a more robust behavior. There was no improvement for the navigation behavior however, with the success rate of navigating deteriorating in some cases. The evolved wallfollowing behavior was compared and tested against another wall-following controller from literature, and shown to be superior. A real-life experiment was also conducted for the wall-following behavior, which led to positive results after correcting for the reality gap. For the wall-following behavior, the grammatical evolution promoted a continuous scanning behavior, which greatly increased it’s awareness of obstacles. Significant recommendations were given to improve the results of the grammatical evolution for both behaviors.},
	author    = {Groen, Chris },
	keywords  = {},
	note      = {Li, S. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Grammatical Evolution for Optimising Drone Behaviors},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:0fc90d7b-7aa3-4501-be7f-ac31330957b6},
	year      = {2022}
}

@mastersthesis{uuid:bb6ace70-512e-4e0e-a834-6b065ece52c5,
	abstract  = {Abstract—Ultra-wideband (UWB) ranging is a very suitable method for indoor localisation of unmanned aerial vehicles (UAVs). Current solutions of UWB ranging however either focus on achieving a high accuracy or focus on scalability. In this research a positioning algorithm for UAVs is presented that combines high accuracy performance with a high level of system scalability. The localisation method uses commercially available off the shelf components and is implemented by connecting two UWB sensors to a micro aerial vehicle. From<br/>both sensors, time-difference of arrival (TDOA) measurements were collected during flights and additionally, a tag-TDOA between the two UWB sensors was measured which estimates the angle-of-arrival of the incoming signals. It was found that state estimation using TDOA measurements from both UWB sensors has a reduced positioning error compared to the algorithm using TDOA measurements from one UWB sensor, without significantly affecting yaw estimation accuracy. Furthermore, the tag-TDOA measurement did not improve the estimation accuracy at the implemented baseline of 0.22 metres as the<br/>measurement error was too large compared to the baseline.},
	author    = {van Beurden, Bas },
	keywords  = {UAV; Localisation; UWB; TDOA; Angle of arrival; MAV; MAVLAB; EKF; State Estimation},
	note      = {Pfeiffer, S.U. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Scalable Positioning Method for MAV Localisation using Two onboard UWB Tags},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:bb6ace70-512e-4e0e-a834-6b065ece52c5},
	year      = {2021}
}

@mastersthesis{uuid:0cf0f29d-7bdd-4050-817b-2486ed6461d9,
	abstract  = {Micro robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge regarding the available computational power to perform the required control tasks. Thus, spiking neural networks (SNNs) are a promising research direction. By mimicking the biological process for transferring information between neurons using spikes or impulses, they allow for low power consumption and asynchronous event-driven processing. In this work, we propose an evolved altitude controller based on a SNN for an airship which relies solely on the sensory feedback provided by an airborne radar sensor. Starting from the design of a a lightweight, low-cost, open-source airship, we also present a low-control-effort SNN architecture, an evolutionary framework for training the network in a simulated environment, and a control scheme for ameliorating the performance of the system in real-world scenarios. The system's performance is evaluated through real-world experiments, demonstrating the advantages of our approach by comparing it with an artificial neural network (ANN) and a linear controller (PID). The results show an accurate tracking of the altitude command while ensuring efficient management of the control effort. The main contributions of this work are presented in the scientific paper, corresponding to Part I of the document. Besides the research on altitude control based on SNNs and their comparison with an ANN and a PID, this thesis includes an in-depth review of the relevant literate on the main topics covered, in Part II. Finally, a detailed explanation of the methodologies used, the conclusions and recommendations for future work are proposed in Part III.},
	author    = {Gonzalez Alvarez, Marina },
	keywords  = {Neuromorphic; Spiking Neural Networks; Micro Air Vehicle; Robot Control; Autonomous System},
	note      = {Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (graduation committee); Corradi, Federico (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Evolved Neuromorphic Altitude Controller for an Autonomous Blimp},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:0cf0f29d-7bdd-4050-817b-2486ed6461d9},
	year      = {2021}
}

@mastersthesis{uuid:0f908624-ddf3-4329-817e-3170d2b6b656,
	abstract  = {Flow sensing exists widely in nature to help animals perform certain tasks. It has also been widely adopted in engineering applications with different types of sensing instrumentation. In particular, in the field of aerospace engineering, airflow sensing is crucial to vehicle state evaluation and flight control. This project surveys the key mechanisms from biological features in nature that enable flow sensing and expands towards the application motivation to identify a suitable airflow sensor that can be equipped to a flapping wing micro air vehicle (FWMAV) for onboard airflow sensing. <br/><br/>The selection of sensors is first narrowed down to three major types of airflow sensors from the state of art that have the most potential to be integrated onboard a flapping wing MAV, considering the sensor performance need, size, weight and power (SWaP) restrictions. Two thermal-based commercially available low-cost airflow sensors RevP and RevC from Modern Device have been selected after the trade-off analysis. <br/><br/>A full workflow of calibrating and evaluating the two airflow sensors' directional sensitivity has been carried out through two wind tunnel campaigns. Their performance under grid-generated turbulence is compared with a constant temperature hot-wire anemometer. This series of tests leads to the conclusion that the RevP airflow sensor has better performance and is therefore chosen to be placed onboard a flapping wing MAV Delfly Nimble. <br/><br/>Both mounted tests and tethered hovering tests with the Delfly Nimble are performed to further examine the airflow sensor RevP's measurement performance under different influence factors such as MAV throttle levels, MAV body pitch angles and freestream speeds. In the end, it is concluded that as a proof of concept, the RevP sensor is capable of performing effective measurements for low flow speeds less than 4 m/s, within the pitching angle range of -30 to 30 degrees. Although this is the first achieved tethered hover flight with onboard airflow sensing for a flapping wing MAV, its limited payload and onboard power supply demands an even smaller and less power consuming design of airflow sensors to enable further applications such as autonomous reactive control under wind disturbances.},
	author    = {Wang, Sunyi },
	keywords  = {DelFly; Flapping Wing MAV; Airflow sensing; Sensor selection; Low speed},
	note      = {van Oudheusden, B.W. (mentor); de Croon, G.C.H.E. (graduation committee); Olejnik, D.A. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Thermistor-based airflow sensing on a flapping wing micro air vehicle},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:0f908624-ddf3-4329-817e-3170d2b6b656},
	year      = {2021}
}

@mastersthesis{uuid:d3baec43-71d4-4f7f-ae27-2fdfdae7fea3,
	abstract  = {Aircraft with disruptive designs have no high-fidelity and accurate flight models. At the same time, developing models for stochastic phenomena for traditional aircraft configurations are costly, and classical control methods cannot operate beyond the predefined operation points or adapt to unexpected changes to the aircraft. The Proximal Policy Option Critic (PPOC) is an end-to-end hierarchical reinforcement learning method that alleviates the need for a high-fidelity flight model and allows for adaptive flight control. This research contributes to the development and analysis of online adaptive flight control by comparing PPOC against a non-hierarchical method called Proximal Policy Optimization (PPO) and PPOC with a single Option (PPOC-1). The methods are tested on an extendable mass-spring-damper system and aircraft model. Subsequently, the agents are evaluated by their sample efficiency, reference tracking capability and adaptivity. The results show, unexpectedly, that PPO and PPOC-1 are more sample efficient than PPOC. Furthermore, both PPOC agents are able to successfully track the height profile, though the agents learn a policy that results in noisy actuator inputs. Finally, PPOC with multiple learned Options has the best adaptivity, as it is able to adapt to structural failure of the horizontal tailplane, sign change of pitch damping, and generalize to different aircraft.},
	author    = {Ge, Zhouxin },
	keywords  = {Reinforcement Learning; Hierarchical Reinforcement Learning; Flight Control Systems; Policy Gradient; Proximal Policy Optimization; Option-Critic architecture},
	note      = {van Kampen, E. (mentor); de Croon, G.C.H.E. (graduation committee); Mitici, M.A. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {End-to-End Hierarchical Reinforcement Learning for Adaptive Flight Control: A method for model-independent control through Proximal Policy Optimization with learned Options},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:d3baec43-71d4-4f7f-ae27-2fdfdae7fea3},
	year      = {2021}
}

@mastersthesis{uuid:b18cead1-951b-4a21-a4bb-0ba36f1768ee,
	abstract  = {The continuous improvement and miniaturisation of elements in drones have been essential for making flapping-wing drones a reality. This thesis presents an integral approach for accurate indoor position control and estimation on flapping-wing drones. The approach considers three main aspects to enhance transient response of the drone. The first one is an experimental velocity/attitude flapping-wing model for drag compensation, obtained through system identification techniques. The second one is a voltage-dependent variable thrust model for enhancing height control. Thirdly, a characterisation of ground effects to determine the height for stable hovering. For the state estimation, an extended Kalman filter fuses UWB position measurements with IMU data. Due to the well-known multi-path effects of UWB, the Kalman filter includes an adaptive noise parameter based on height. The novel control strategy was validated with real flight tests, where position control improved by a factor of 1.5, reaching a mean absolute error of 10cm in positions in x and y, and 4.9cm for position in z.},
	author    = {Gonzalez Archundia, Guillermo },
	keywords  = {Mavlab; UWB; drag compensation; FWMAV; position control; thrust control; ground effect},
	note      = {de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Position controller for a flapping-wing drone using ultra wide band},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:b18cead1-951b-4a21-a4bb-0ba36f1768ee},
	year      = {2021}
}

@mastersthesis{uuid:9ddca1f8-3956-42b7-a683-b418fcd89df2,
	abstract  = {Drones need to be able to detect and localize each other if they are to collaborate in multi-robot teams or swarms. Typically, computer vision methods based on visual appearance are investigated to this end. In contrast, in this work, a method based on dense optical flow (OF) is developed that detects dynamic objects. This is achieved by comparing the flow vectors of dense OF with the direction to the Focus of Expansion (FoE) in the image plane. A simulation in AirSim is developed to validate this approach and to create datasets for motion-based object detection of MAVs. This simulation includes ground-truth FoE, depth, OF and IMU data. The results show that this method performs well if the OF vector's magnitude is large enough and its angle is sufficiently different from those of static world points. We expect that the presented method will serve as a useful baseline for deep learning methods that use dense optical flow as input.},
	author    = {Vroon, Erik },
	keywords  = {Optical Flow; Neural Network; Object Detection; Micro Air Vehicle; Focus of Expansion},
	note      = {de Croon, G.C.H.E. (mentor); Rojer, Jim (mentor); Guo, J. (graduation committee); de Visser, C.C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Motion-based MAV Detection in GPS-denied Environments},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:9ddca1f8-3956-42b7-a683-b418fcd89df2},
	year      = {2021}
}

@mastersthesis{uuid:9b68db7c-ac32-422e-8749-a8e0bc1fc4ca,
	abstract  = {Self-supervised deep learning methods have leveraged stereo images for training monocular depth estimation. Although these methods show strong results on outdoor datasets such as KITTI, they do not match performance of supervised methods on indoor environments with camera rotation. Indoor, rotated scenes are common for less constrained applications and pose problems for two reasons: abundance of low texture regions and increased complexity of depth cues for images under rotation. In an effort to extend self-supervised learning to more generalised environments we propose two additions. First, we propose a novel Filled Disparity Loss term that corrects for ambiguity of image reconstruction error loss in textureless regions. Specifically, we interpolate disparity in untextured regions, using the estimated disparity from surrounding textured areas, and use L1 loss to correct the original estimation. Our experiments show that depth estimation is substantially improved on low-texture scenes, without any loss on textured scenes, when compared to Monodepth by Godard et al. Secondly, we show that training with an application's representative rotations, in both pitch and roll, is sufficient to significantly improve performance over the entire range of expected rotation. We demonstrate that depth estimation is successfully generalised as performance is not lost when evaluated on test sets with no camera rotation. Together these developments enable a broader use of self-supervised learning of monocular depth estimation for complex environments.},
	author    = {Keltjens, Benjamin },
	keywords  = {Self-supervision; Depth Estimation; Textureless},
	note      = {de Croon, G.C.H.E. (mentor); van Dijk, Tom (mentor); van Gemert, J.C. (graduation committee); Smeur, E.J.J. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:9b68db7c-ac32-422e-8749-a8e0bc1fc4ca},
	year      = {2021}
}

@mastersthesis{uuid:5aa0ed78-c775-4a1b-a0ab-5145f85e5e9d,
	abstract  = {An idea was proposed to allow an autonomous drone to have indefinite flight times over the ocean by applying renewable energy technologies and theory to generate electricity in flight. This is considered less as a way to save energy, but to permit the use of such a drone from a ship not capable of safely retrieving it. One novel component of this idea is to use the wind updraft created by the motion of a ship or natural air currents as the wind source for an on-board turbine generator. The second component is to use the existing drive system as the on-board turbine in a 'hybrid rotor' design to reduce the need for extra parts and complexity. This report analyzes the potential for such a system compared to a more intuitive airborne solar system, and to the combination of both concepts. While indefinite flight time is paramount, the goal is to maximize the "mission" time to charge/idle time ratio. The process for determining fitness is a simulation of the aircraft flying on its mission and charging when needed (and if possible) for a full year for varying designs of aircraft and rotor. The results of all the tests show that the main idea is infeasible because not enough energy can be generated from the inefficient propeller and the updrafts are insufficient and inconsistent. The alternatives of solar and combined power systems function better but are still subject to high failure rates. The most promising system is to use a separate turbine and propeller and also include solar panels to achieve the most effectiveness both when in powered flight and while charging. This constitutes a compromise on the 'hybrid rotor' part of the idea. The conclusion of this report is that further improvements to the design and control of the most successful configuration are possible could result in a fully functional system.},
	author    = {Dvorsky, Nicholas },
	keywords  = {Drone; Range Improvement; soaring; static; Solar; Turbine; updraft; Endurance},
	note      = {Zaaijer, M B (mentor); de Croon, G.C.H.E. (graduation committee); Schmehl, R. (graduation committee); Remes, B.D.W. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Electrical Engineering, Mathematics and Computer Science},
	title     = {Feasibility of using electric drone main rotors for electricity generation vs. solar panels for indefinite flight},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:5aa0ed78-c775-4a1b-a0ab-5145f85e5e9d},
	year      = {2021}
}

@mastersthesis{uuid:f46c66f3-60e5-4285-9cf3-978826387526,
	abstract  = {Flapping-Wing Air Vehicles (FWAV) are autonomously flying vehicles that use their flapping wings to simultaneously stay aloft and enable controllable flight. FWAVs that are capable of controllable flight are reported in literature, though a theoretical background of the aerodynamic performance of different attitude control mechanisms is absent in literature and the robustness of attitude control mechanisms with respect to body motions is oftentimes omitted. The aim of this thesis is to develop a theoretical framework for the aerodynamic response of flapping wings that includes variation of attitude control parameters and motion of the vehicle body. This framework can be used to assist in research into new attitude control mechanisms for FWAVs that are not yet capable of attitude control, such as the compliant Atalanta FWAV. Analytical aerodynamic and kinematic descriptions are combined to analyze the aerodynamic performance of two suggested attitude control mechanisms: stroke amplitude variations and control of the angle of attack by means of pitching stiffness variations. It is shown in this research that both mechanisms have a significant influence on the lift production of a flapping wing, though this influence changes significantly when body motions are introduced. It is found that variations of the stroke amplitude provide the most predictable variations in lift for all cases of body motion that were considered, provided that the wing’s pitching hinge stiffness is high enough to ensure stable flapping kinematics under the influence of body motion.},
	author    = {Roulaux, Bas },
	keywords  = {Flapping-Wing Air Vehicles; Flapping-Wing Aerodynamics; Attitude Control},
	note      = {Goosen, J.F.L. (mentor); Remes, B.D.W. (graduation committee); van der Wijk, V. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Precision and Microsystems Engineering},
	title     = {Attitude Control of Flapping-Wing Air Vehicles},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:f46c66f3-60e5-4285-9cf3-978826387526},
	year      = {2021}
}

@mastersthesis{uuid:019566ca-34de-4ddd-87ac-86364ef2759b,
	abstract  = {Micro air vehicles (MAVs) are increasingly being considered for aerial tasks such as delivery of goods and surveillance due to their lightweight, compact design and manoeuvrability. To safely and reliably carry out these tasks and navigate to its objective, especially in complex and cluttered environments, the MAV is also required to sense and avoid (S&amp;A) obstacles. Due to the MAVs limitations in weight, power and processing power, vision systems usually prove ideal for sensing the environment, being a cheap, lightweight, power efficient and a rich source of information. They do however require adequate computational resources and most importantly, good visibility. When the environment does not host these conditions, for instance when flying though dust, smoke or fog, other sensors need to be utilised that can provide more robust sensing to ensure safe and reliable operation. Radar sensors are mostly unaffected by atmospheric conditions and have been used extensively in the aerospace industry for this purpose. These sensors were traditionally heavy and power hungry, only applicable on ground or in large craft. However other radar sensors have since come about that are more suited for use in small MAVs. Specifically, lightweight, power efficient and compact frequency modulated continuous wave (FMCW) radars have increasingly been used in advanced driver assistance systems as auxiliary sensors, however there has been little work to integrate them on MAVs. This sensor provides the range, horizontal bearing and radial velocity (Doppler shift) of any objects in the field of view, which can then be used for multi-target tracking (MTT) [38]. The major disadvantage of the sensor is the limited field of view (approximately 80 degrees horizontal) and noisy nature of the sensor, especially in cluttered environments. The challenge is to explore filtering, tracking and avoidance algorithm pipelines to extract meaningful information from the raw data and investigate the sensor’s effectiveness with respect to obstacle avoidance on MAVs. This will include algorithms such as data association, estimation and avoidance, as well as an investigation of neural networks to aid in processing the raw data and provide some filtering. This will be accomplished by integrating the sensor on a MAV and testing and tuning the algorithms both in real life (in the cyberzoo flying arena of the aerospace faculty), and using data gathered as part of an obstacle detection and avoidance dataset that was generated during this project. This will hopefully allow MAVs to operate safer, either using a standalone radar or integrated with other sensors.},
	author    = {Wessendorp, Nikhil },
	keywords  = {},
	note      = {Dupeyroux, J.J.G. (mentor); de Croon, G.C.H.E. (mentor); Fioranelli, F. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Obstacle Avoidance onboard MAVs using a FMCW RADAR},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:019566ca-34de-4ddd-87ac-86364ef2759b},
	year      = {2021}
}

@mastersthesis{uuid:a4497602-a257-4561-8e4c-5baa36e8cd6f,
	abstract  = {The most common used classification of drones is Micro air vehicles (MAV), with quadrotors being the most conventional MAV [1]. Its relatively small size and rotary wings provide high maneuverability, including vertical takeoff and hovering, making them useful in confined and hard to reach spaces. Another benefit of the MAV in comparison to other drone classifications is its relatively smaller production costs [1]. Because of these features they play an increasing role within our society by aiding in human tasks, by applying them in for example site inspection, agriculture and rescue missions [2–4]. However, its agility comes at a cost which takes the form of high power consumption [5]. Making improving MAV designs a challenge.},
	author    = {Booster, Quincy },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Urban MAV: A visual odometry dataset},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:a4497602-a257-4561-8e4c-5baa36e8cd6f},
	year      = {2021}
}

@mastersthesis{uuid:8f90894b-933a-4649-90a0-1bbc1de6c0d6,
	abstract  = {For most robotics applications, optimal control remains a promising solution for solving complex control tasks. One example is the time-optimal flight of Micro Air Vehicles (MAVs), where strict computational requirements fail to resolve such algorithms onboard. Recent work on the use of deep neural networks for guidance and control (G&amp;CNets) has shown that these biologically inspired models approximate well the optimal control solution while requiring a fraction of the computational cost. Although previous attempts resulted in successful flight tests, training occurred on large-scale datasets based on a 3-DoF model. Since model refinement leads to higher generation time, in this work, we show that G&amp;CNets trained on small-sized datasets can mimic the optimal control solution of a full 6-DoF quadrotor model. The cost function used in the generation process penalizes the altitude error and mixes both time and power-optimal objectives weighted by a varying homotopy parameter. Trained networks output the vertical thrust command and body rates based on the vehicle's position, velocity, and attitude. The proposed controller transfers well onboard for different flight scenarios: (i) longitudinal, lateral and diagonal flight; (ii) hovering with and without the effect of disturbances and (iii) waypoint tracking experiment. Through a Monte-Carlo test campaign, it is demonstrated that G&amp;CNets trained on small datasets provide similar results to those with 100 times more samples. To the best of our knowledge, this work is the first implementation of a high-dimensional G&amp;CNet in the control loop of a real MAV.},
	author    = {Chotalal, Rohan },
	keywords  = {Optimal Control; Neural Networks; Micro air vehicles},
	note      = {de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {High-Dimensional Optimal State-Feedback Mapping using Deep Neural Networks for Agile Quadrotor Flight},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:8f90894b-933a-4649-90a0-1bbc1de6c0d6},
	year      = {2021}
}

@mastersthesis{uuid:85b358c9-8018-4e0c-867e-f35fe26716cb,
	abstract  = {Micro Air Vehicles (MAVs) are able to support humans in dangerous operations, such as search and rescue operations at night on unknown terrain. These scenes require a great amount of autonomy from the MAV, as they are often radio and GPS-denied. As MAVs have limited computational resources and energy storage, onboard navigation tasks have to be performed efficient and fast. To address this challenge, this research proposes an approach to visual obstacle detection and avoidance onboard an MAV. The algorithmic approach is based on event-based optic flow, using a monocular event-based camera. This camera captures the apparent motion in the scene, has microsecond latency and very low power consumption, therefore a good fit for onboard navigation tasks. Firstly, a literature study is performed to provide theoretical concepts and foundation for the obstacle avoidance approach. A processing pipeline is designed, based on the use of event-based normal optic flow. This pipeline consists of three sections: course estimation, obstacle detection and obstacle avoidance. A novel course estimation method 'FAITH' is proposed which uses optic flow half-planes along with a fast RANSAC scheme. The object detection method is based on DBSCAN clustering of optic flow vectors, using the time-to-contact and vector location as clustering variables. The performance of these methods is experimentally demonstrated by three experiments: in a simulated environment, offline on real sensor data and online onboard an MAV. As currently no event-based obstacle avoidance datasets are publicly available, a dataset is recorded as supplement to this and future research. Approximately 1350 runs of event-based camera, RADAR, IMU and OptiTrack data are recorded, manually avoiding either a single or two poles using an MAV in the flying arena of the TU Delft. This dataset is used in this research to determine the performance of the course estimation method using real sensor data. The course estimation method is shown to have state-of-the-art accuracy and beyond state-of-the-art computation time on both simulated data and the recorded dataset. The final experiment shows the obstacle detection and avoidance approach integrated onboard an MAV in a real-time obstacle avoidance task. The approach is shown to have a success rate of 80% in a frontal obstacle avoidance task on a low-textured 50-cm wide pole. The contribution of this research is an obstacle detection and avoidance approach using a monocular event-based camera onboard an MAV, along with the novel course estimation algorithm 'FAITH'.},
	author    = {Dinaux, Raoul },
	keywords  = {Obstacle detection; Obstacle Avoidance; micro air vehicles; Event-based vision; course estimation; optic flow; neuromorphic},
	note      = {de Croon, G.C.H.E. (mentor); Dupeyroux, J.J.G. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Obstacle Detection and Avoidance onboard an MAV using a Monocular Event-based Camera},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:85b358c9-8018-4e0c-867e-f35fe26716cb},
	year      = {2021}
}

@mastersthesis{uuid:665779ce-5080-43da-8519-4cd17e2f105d,
	abstract  = {Time-optimal model-predictive control is essential in achieving fast and adaptive quadcopter flight. Due to the limited computational performance of onboard hardware, aggressive flight approaches have relied on off-line trajectory optimization processes or non time-optimal methods. In this work we propose a computational efficient model predictive controller (MPC) that approaches time-optimal flight and runs onboard a consumer quadcopter. The proposed controller is built on the principle that constrained optimal control problems (OCPs) have a so-called 'bang-bang' solution. Our solution plans a bang-bang maneuver in the critical direction while aiming for a 'minimum-effort' approach in non-critical direction. Control parameters are computed by means of a bisection scheme using an analytical path prediction model. The controller has been compared with a classical PID controller and theoretical time-optimal trajectories in simulations. We identify the consequences of the OCP simplifications and propose a method to mitigate one of these effects. Finally, we have implemented the proposed controller onboard a consumer quadcopter and performed indoor flights to compare the controller's performance to a PID controller. Flight experiments have shown that the controller runs at 512hz onboard a Parrot Bebop quadcopter and is capable of fast, saturated flight, outperforming traditional PID controllers in waypoint-to-waypoint flight while requiring only minimal knowledge of the quadcopter's dynamics.},
	author    = {Westenberger, Jelle },
	keywords  = {Optimal Control; Model Predictive Control; Unmanned Aerial Vehicle; micro air vehicles},
	note      = {de Croon, G.C.H.E. (mentor); de Wagter, C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Time-Optimal Control for Tiny Quadcopters},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:665779ce-5080-43da-8519-4cd17e2f105d},
	year      = {2021}
}

@mastersthesis{uuid:9f478516-7129-4e6f-9b84-d66fad419d51,
	abstract  = {Widespread usage of Micro Aerial Vehicles (MAVs) has led to various airspace safety breaches, including near mid-air collisions with other aircraft. To ensure safe integration into general aviation, it is paramount that MAVs are equipped with an autonomous detect and avoid system when flying beyond the visual line-of-sight of the operator. The purpose of this research is to investigate the feasibility of acoustic-based aircraft detection, which has generally been overlooked in favor of optical or radar-based technology. Effective sound-based aircraft detection on-board an MAV requires suppressing the dynamic ego-noise it generates during flight, which would otherwise pollute the recorded environmental sound. This paper proposes using a recurrent neural network to predict the generated noise, given a sequence of MAV flight data, so that it can be effectively removed from noisy recordings. For aircraft detection, a convolutional neural network in combination with Mel spectrogram features is designed to classify noise-free environmental sound as either aircraft or non-aircraft, achieving 97.5\% accuracy. To reconstruct the noisy environment of an MAV flight, these noise-free sounds are mixed with ego-noise at mix ratios up to 1.00. When evaluating in these mismatched conditions, accuracy decreases to 95.0\% and 47.5\% with- and without ego-noise suppression, respectively. Although ego-noise suppression can not prevent a drop in performance, the large difference between the mismatched conditions does demonstrate the benefits of the proposed denoising approach on aircraft detection.},
	author    = {van der Woude, Mark },
	keywords  = {Aircraft Detection; Sound Event Classification; Ego-Noise Suppression; Mel Spectrogram; Artificial Neural Networks},
	note      = {van Dijk, Tom (mentor); de Croon, G.C.H.E. (mentor); Snellen, M. (graduation committee); de Wagter, C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Acoustic-Based Aircraft Detection and Ego-Noise Suppression: for Micro Aerial Vehicles},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:9f478516-7129-4e6f-9b84-d66fad419d51},
	year      = {2021}
}

@mastersthesis{uuid:022cdfe2-737e-44f4-8497-0159a826b9ea,
	abstract  = {Safe Curriculum Learning constitutes a collection of methods that aim at enabling Rein- forcement Learning (RL) algorithms on complex systems and tasks whilst considering the safety and efficiency aspect of the learning process. On the one hand, curricular reinforce- ment learning approaches divide the task into more gradual complexity stages to promote learning efficiency. On the other, safe learning provides a framework to consider a system’s safety during the learning process. The latter’s contribution is significant on safety-critical systems, such as transport vehicles where stringent (safety) requirements apply. This pa- per proposes a black box safe curriculum learning architecture applicable to systems with unknown dynamics. It only requires knowledge of the state and action spaces’ orders for a given task and system. By adding system identification capabilities to existing safe cur- riculum learning paradigms, the proposed architecture successfully ensures safe learning proceedings of tracking tasks without requiring initial knowledge of internal system dynam- ics. More specifically, a model estimate is generated online to complement safety filters that rely on uncertain models for their safety guarantees. This research explicitly targets linearised systems with decoupled dynamics in the experiments provided in this article as proof of concept. The paradigm is initially verified on a mass-spring-damper system. After that, the architecture is applied to a quadrotor where it is able to successfully track the system’s four degrees of freedom independently, namely attitude angles and altitude. The RL agent is able to safely learn an optimal policy that can track an independent reference on each degree of freedom.},
	author    = {De Buysscher, Diego },
	keywords  = {Reinforcement Learning; Curriculum Learning; Safe Learning; Machine Learning; Q-Learning; flight control},
	note      = {de Croon, G.C.H.E. (mentor); van Kampen, E. (graduation committee); Mooij, E. (graduation committee); Pollack, T.S.C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Safe Curriculum Learning for Linear Systems With Unknown Dynamics in Primary Flight Control},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:022cdfe2-737e-44f4-8497-0159a826b9ea},
	year      = {2021}
}

@mastersthesis{uuid:1eed4184-5c4a-4e88-b54d-8c9751f79ebf,
	abstract  = {Current state-of-the-art airline planning models are required to decrease models either in size or complexity due to computational limitations, limiting the <br/>operational applicability to problems of representative sizes. Models return suboptimal solutions, especially when confronted with factors of uncertainty. Considering the growing interest in the application of Machine Learning techniques in the Operations Research domain, and the proven success in other fields such as robotics, this research investigates the applicability of these techniques for airline planning. An Advantage Actor-Critic (A2C) Reinforcement Learning agent is applied to the airline fleet planning problem. Because of the increased computational efficiency of using an A2C agent, the problem is increased in size and the highly volatile uncertainty in fuel price is implemented.<br/>Conversion was achieved, and when evaluating the quality of the solutions compared to a deterministic model, the performance was very satisfactory. The A2C agent was able to outperform the deterministic model, with an increasing performance as more complexity was added to the problem. It was found that<br/>the introduction of additional uncertainty has a major effect on the optimal actions, which the agent was able to adapt to adequately.},
	author    = {Geursen, Izaak },
	keywords  = {Airline Fleet Planning; Stochastic Optimization; Reinforcement Learning; Advantage Actor-Critic; Fuel Price Uncertainty},
	note      = {Lopes Dos Santos, Bruno (mentor); Yorke-Smith, Neil (graduation committee); de Croon, Guido (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Fleet Planning Under Demand and Fuel Price Uncertainty Using Actor-Critic Reinforcement Learning},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:1eed4184-5c4a-4e88-b54d-8c9751f79ebf},
	year      = {2021}
}

@mastersthesis{uuid:e44e5449-519b-4a21-81e3-a96f1bbb2811,
	abstract  = {Multi-agent robotic systems could benefit from reinforcement learning algorithms that are able to learn behaviours in a small number trials, a property known as sample efficiency. This research investigates the use of learned world models to create more sample-efficient algorithms. We present a novel multi-agent model-based reinforcement learning algorithm: Multi-Agent Model-Based Policy Optimization (MAMBPO), utilizing the Centralized Learning for Decentralized Execution (CLDE) framework, and demonstrate state-of-the-art performance in terms of sample efficiency on a number of benchmark domains. CLDE algorithms allow a group of agents to act in a fully decentralized manner after training. This is a desirable property for many systems comprising of multiple robots. Current CLDE algorithms such as Multi-Agent Soft Actor-Critic (MASAC) suffer from limited sample efficiency, often taking many thousands of trials before learning desirable behaviours. This makes these algorithms impractical for learning in real-world robotic tasks. MAMBPO utilizes a learned world model to improve sample efficiency compared to its model-free counterparts. We demonstrate on two simulated multi-agent robotics tasks that MAMBPO is able to reach similar performance to MASAC with up to 3.7 times fewer samples required for learning. Doing this, we take an important step towards making real-life learning for multi-agent robotic systems possible.},
	author    = {Willemsen, Daniël },
	keywords  = {model-based; Reinforcement Learning; Multi-agent; Decentralized Control; deep learning},
	note      = {Coppola, M. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Sample-efficient multi-agent reinforcement learning using learned world models},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:e44e5449-519b-4a21-81e3-a96f1bbb2811},
	year      = {2021}
}

@mastersthesis{uuid:35ad1e1c-a3c3-456a-a899-06dac1dc3398,
	abstract  = {As the application areas of Unmanned Aerial Vehicles (UAVs) keep expanding, new flight areas are encountered more often. Small UAVs, named Micro Air Vehicles (MAVs), even fly in areas like sewage pipes. These areas introduce new difficulties such as aerodynamic effects caused by the ground and/or ceiling. In this paper two main contributions are presented that deal with the aerodynamic effects caused by the ceiling: 1) an adaptive model describing the ceiling effect using onboard measurements, which can be altered to describe other aerodynamic effects that occur when flying in constrained spaces, 2) incorporating the adaptive model into an Incremental Nonlinear Dynamic Inversion (INDI) controller. The controller is implemented and tested onto a MAV (Crazyflie). The results have shown stability improvements for close ceiling flight. Moreover the minimal distance the MAV can fly from the ceiling is decreased using the new controller.},
	author    = {Kemmeren, Max },
	keywords  = {INDI; Quadcopter; Ceiling effect; aerodynamic disturbance; Controller; Crazyflie; parameter estimation},
	note      = {Smeur, E.J.J. (mentor); de Wagter, C. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Improving the Performance of INDI Flight Control for a Quadrotor in the Ceiling Effect},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:35ad1e1c-a3c3-456a-a899-06dac1dc3398},
	year      = {2021}
}

@mastersthesis{uuid:302426ff-a4a4-4f8a-967a-331ea71b1ba1,
	abstract  = {Obstacle avoidance is an essential topic in the field of autonomous drone research. When choosing an avoidance algorithm, many different options are available, each with their advantages and disadvantages. As there is currently no consensus on testing methods, it is quite challenging to compare the performance between algorithms. In this paper, we present AvoidBench, a benchmarking suite capable of evaluating the performance of vision-based obstacle avoidance algorithms for multi-rotors in simulation. Utilising a set of performance metrics, AvoidBench assigns performance scores to obstacle avoidance algorithms by subjecting them to a series of tasks. Using both Airsim and Unreal engine under the hood, we are able to provide high-fidelity visuals and dynamics, leading to a relatively small gap between simulation and reality. AvoidBench comes included with a simple, but powerful C++ and Python API which provides functionality for procedural environment generation, custom benchmark design, and an easy-to-use framework for users to implement their own vision-based obstacle avoidance methods. Implementing an obstacle avoidance method can be done entirely in a single file, allowing anyone to share and compare their obstacle detection and avoidance algorithms with others.},
	author    = {Veder, Rano },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {AvoidBench: A high-fidelity vision-based obstacle avoidance benchmarking suite for multi-rotors},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:302426ff-a4a4-4f8a-967a-331ea71b1ba1},
	year      = {2020}
}

@mastersthesis{uuid:15f2daf8-1596-4514-8b1b-e139881cfaf3,
	abstract  = {A high-level neural controller for leader-follower flight is presented. State of the art range-based relative localization schemes that rely exclusively on onboard sensors present an additional challenge to the leader-follower control problem since they restrict the flight conditions that guarantee observability. This novel controller was developed over an evolutionary process in which the simulation environment resembled the real-life constraints a group of MAVs would encounter. During the learning stage, a group of three agents is used, where one acts as a leader and flies a random trajectory, and the other two act as followers guided by a candidate controller that dictates the desired velocity commands. In the end, when equipped with the best-evolved controller, the follower agents are able to showcase a successful following behaviour that also enhances the observability of the system, although no observability metric was included in evolution.},
	author    = {Falcão da Cruz Rodrigues Lourenço, Eduardo },
	keywords  = {Leader-Follower; Evolutionary Robotics; Relative Localization},
	note      = {de Croon, G.C.H.E. (mentor); Coppola, M. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {An intelligent leader-follower neural controller in adverse observability scenarios},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:15f2daf8-1596-4514-8b1b-e139881cfaf3},
	year      = {2020}
}

@mastersthesis{uuid:7dd7edc9-0037-4c3e-a667-aac7476f272f,
	abstract  = {Nano quadcopters are ideal for gas source localization (GSL) as they are cheap, safe and agile. However, previous algorithms are unsuitable for nano quadcopters, as they rely on heavy sensors, require too large computational resources, or only solve simple scenarios without obstacles. In this work, we propose a novel bug algorithm named `Sniffy Bug', that allows a swarm of gas-seeking nano quadcopters to localize a gas source in an unknown, cluttered and GPS-denied environment. Sniffy Bug is capable of efficient GSL with extremely little sensory input and computational resources, operating within the strict resource constraints of a nano quadcopter. The algorithm foresees in the avoidance of obstacles and other swarm members, while pursuing desired waypoints. The waypoints are set for exploration, and, when a single swarm member has sensed the gas, by a particle swarm optimization-based procedure. We evolve all the parameters of the bug (and PSO) algorithm, with a novel automated end-to-end simulation and benchmark platform, AutoGDM. This platform enables fully automated end-to-end environment generation and gas dispersion modelling (GDM), not only allowing for learning in simulation but also providing the first GSL benchmark. We show that evolved Sniffy Bug outperforms manually selected parameters in challenging, cluttered environments in the real world. To this end, we show that a lightweight and mapless bug algorithm can be evolved to complete a complex task, and enable the first fully autonomous swarm of collaborative gas-seeking nano quadcopters.},
	author    = {Duisterhof, Bart },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Verhoeven, C.J.M. (graduation committee); de Wagter, C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Sniffy Bug: A fully autonomous and collaborative swarm of gas-seeking nano quadcopters in cluttered environments},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:7dd7edc9-0037-4c3e-a667-aac7476f272f},
	year      = {2020}
}

@mastersthesis{uuid:ada45454-c19a-4fba-b842-62efd2320a6a,
	abstract  = {Flapping wing micro air vehicles (FWMAV's) are a subcategory of unmanned aerial vehicle which use flapping wings for thrust generation. The high agility and maneuverability of FWMAV's are very favorable attributes, making them more applicable in cluttered spaces. A tailless FWMAV called the Delfly Nimble has been developed at the Delft University of Technology. Due to the inherent instability of the tailless design an active controller is required to ensure safe and stable flight of the drone. In previous research, models have been developed for the longitudinal dynamics of the Delfly Nimble. In this paper, a grey-box state-space model of the lateral body dynamics in hover conditions is identified using system identification techniques. The parameters which needed to be estimated were stability and control derivatives, and they were obtained with a least-squares approach. Free-flight experiments were performed to generate the identification and validation data. A doublet train was used in the identification experiments, with the gains of the controller adjusted in such a way that maximum excitation was acquired. The identified model has been validated with various maneuvers. These included doublets, 112-maneuvers, maneuvers using coupled inputs, and maneuvers with sideways flight. The resulting model is able to predict the state derivatives of most maneuver accurately, reaching accuracies of over 90% for maneuvers close to hover. Moreover, in closed-loop configuration it is able to simulate the state response accurately, with accuracies of over 85% for maneuvers close to hover, and remains stable, making it applicable for controller design and stability analysis. Finally, based on the model the inherent instability of the lateral body dynamics was also confirmed, for there are eigenvalues with positive real parts.},
	author    = {Bains, Karan },
	keywords  = {grey-box modeling; FWMAV; free flight data; closed-loop identification; system identification; lateral body dynamics},
	note      = {de Visser, C.C. (mentor); Olejnik, D.A. (mentor); Karasek, M. (mentor); Armanini, S.F. (mentor); de Croon, G.C.H.E. (graduation committee); Mooij, E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {System Identification of the Delfly Nimble: Modeling of the Lateral Body Dynamics},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:ada45454-c19a-4fba-b842-62efd2320a6a},
	year      = {2020}
}

@mastersthesis{uuid:b2db7412-74d9-4914-94b7-0b922a061adc,
	abstract  = {The application of Unmanned Aerial Vehicles (UAVs) is increasing, much like the performance of these aircraft. A tailsitter is a type of UAV which is capable of performing vertical take-offs and landings (VTOL) and long endurance flights. During hover, the yaw control is limited due to the dynamics of these tailsitters. The generally used quaternion feedback for the attitude does not compensate for this as it describes a singular rotation. Tilt-twist is a solution to the problem as it splits the tilt (pitch and roll) from the twist (yaw). The axis of the yaw rotation is body fixed. When hovering with a pitch and/or roll angle the twist axis will be aligned with the body z-axis, instead of the desired gravitational force vector (for position control). Previous tilt-twist methods used a PID controller. This paper describes an improvement over previous tilt-twist approaches, the dynamic tilt-twist in combination with INDI. The INDI controller is designed for nonlinear systems. The dynamic tilt-twist compensates for the problem with the normal tilt-twist as test results will demonstrate. Tests are performed in a simulation and a real life test with the NederDrone hybrid tailsitter is done.},
	author    = {Dellemann, Lars },
	keywords  = {},
	note      = {de Wagter, C. (mentor); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Hybrid UAV Attitude Control using INDI and Dynamic Tilt-Twist},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:b2db7412-74d9-4914-94b7-0b922a061adc},
	year      = {2020}
}

@mastersthesis{uuid:054d38f7-8fe7-447c-b9fc-9d13437fcef5,
	abstract  = {Passive acoustic sensing utilizes the ability of sound to travel beyond the line-of-sight to understand the surroundings. This provides an advantage over the currently used sensors in Intelligent Vehicles that can sense obstacles within their line-of-sight only. Recently, a localization based approach has been implemented to take advantage of this sensing modality to predict approaching vehicles behind the blind corner in an urban scenario. While this approach shows a lot of promise, there is a difficulty in integrating the multi-microphone system. Additionally, the system would be unable to differentiate between the nature of two sound sources. This motivates the exploration of a classification based approach which uses audio data from only a single microphone to identify the sound sources present in them. This thesis investigates the possibility of having such a system on the Intelligent Vehicle to predict approaching vehicles from behind the blind corners. A review of the literature revealed that techniques categorized under Sound Event Detection (SED) are suitable to implement a classification based approach. The prediction of the vehicle is treated as a binary classification problem and a Convolutional Recurrent Neural Network (CRNN) is used as the acoustic model to detect the presence of an approaching car in the audio sample represented by Log Mel Spectrogram features. Additionally, domain adaptation techniques were implemented to explore the possibility<br/>of improving the system performance with limited data collected while the ego-vehicle is driving. Experiments carried out indicate that when the ego-vehicle is static, the system performs well with the approaching vehicle predicted 1.4s before it is in line-of-sight and a balanced accuracy of 86.9% achieved for the classification task. However, the system achieved an accuracy of 68% on the samples recorded while the ego-vehicle was driving. Further experiments indicate that the acoustic model cannot generalize well to unseen situations in most cases and experiment with domain adaptation did not show<br/>any improvement in performance.},
	author    = {Mattar, Avinash },
	keywords  = {Acoustic Perception; Intelligent Vehicles; Sound Event Detection; Deep Learning; Domain Adaptation},
	note      = {Kooij, J.F.P. (mentor); Hehn, T.M. (mentor); Gavrila, D. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics; TU Delft Intelligent Vehicles},
	title     = {Acoustic Perception in Intelligent Vehicles using a single microphone system},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:054d38f7-8fe7-447c-b9fc-9d13437fcef5},
	year      = {2020}
}

@mastersthesis{uuid:a88b7802-2b7c-44cb-85f6-63d0453fc9e7,
	abstract  = {Autonomous drone racing has taken a turn for the better in recent years. Drones are becoming faster and implementing better state-of-the-art control techniques to overcome different challenges. With advancements in the fields of computer vision, machine learning, and artificial intelligence, the final goal of autonomous drones is to be quicker than human-piloted racing drones. Increasing the speed of autonomous drones increases the risks associated with flying them. Time-optimal control algorithms have been identified as a method of implementing<br/>aggressive maneuvers to fly drones at high speeds throughout the course of the race. These methods require precise state-estimates. This research work identifies a model for the rate controller. The work also includes an implementation of a state estimation model with drag compensation, also merging a pre-existing refined thrust model with Coriolis effects. With the idea of developing a state estimation model for a racing drone, the model is improved to<br/>include flight envelopes involving motor saturations.},
	author    = {Patel, Nishant },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Xu, Y. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Dynamic Modelling and State Estimation of a High Speed Racing Drone},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:a88b7802-2b7c-44cb-85f6-63d0453fc9e7},
	year      = {2020}
}

@mastersthesis{uuid:4b92f6b0-dc40-4946-a1ae-7efd0df79401,
	abstract  = {This research presents the derivation, implementation and safety assessment of a velocity obstacle- based conflict resolution method to be used by UAVs flying within a horizontally restricted airspace by a geofence under the presence of wind. Two parameters indicating the safety of the applied conflict resolution method have been measured, i.e., the Intrusion Prevention Rate (IPR) and the Violation Prevention Rate of the Geofence (VPRG). Three coordination rule-sets have been implemented i.e., 1) geometric optimum (OPT), 2) geometric optimum from target heading (DEST) and 3) only change in heading (HDG). These rule-sets have been assessed during a safety assessment. It was concluded that the OPT rule-set performed best in terms of the IPR and the DEST rule-set performed best in terms of the VPRG under windy and wind calm conditions. The HDG rule-set performed worst in terms of both safety parameters. It was noted that both safety parameters are the lowest when conflicts occur close the geofence under windy conditions for all implemented rule-sets.},
	author    = {van Wijngaarden, Dennis },
	keywords  = {UAV; Conflict Detection & Resolution (CD&R); Air Traffic Management; UAV Traffic Management; UTM; ATM; Solution Space Diagram (SSD); Velocity Obstacle},
	note      = {Ellerbroek, Joost (mentor); Remes, B.D.W. (mentor); Hoekstra, J.M. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Implicit Coordinated Tactical Avoidance for UAVs within a Geofenced Airspace},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:4b92f6b0-dc40-4946-a1ae-7efd0df79401},
	year      = {2020}
}

@mastersthesis{uuid:71257d1e-c65b-4eb7-9df0-869b9419a8c2,
	abstract  = {This research presents an implementation of a novel controller design on an overactuated hybrid Unmanned Aerial Vehicle (UAV). This platform is a hybrid between a conventional quadcopter and a fixed-wing aircraft. Its inner loop is controlled by an existing but modified control method called Incremental Non-linear Control Allocation or INCA. This controller deals with the platform’s control allocation problem by minimising a set of objective functions with a method known as the Active Set Method and avoids actuator saturation. For the vehicle’s outer loop, a novel extension to INCA is presented, called Extended INCA or XINCA. This method optimises one of the physical actuator’s command and the angular control demands fed to the vehicle’s inner loop, based on linear reference accelerations. It does so while adapting to varying flight phases, conditions and vehicle states, and taking into account the aerodynamic properties of the main wing. XINCA has low dependence on accurate vehicle models and requires configuration using only several optimisation parameters. Both flight simulations and experimental flights are performed to prove the performance of both controllers.},
	author    = {Karssies, Jan },
	keywords  = {Control Allocation; UAV; INDI; INCA; XINCA; Over-actuation; Active Set Method; Quadplane},
	note      = {de Wagter, C. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Extended Incremental Non-linear Control Allocation on the TU Delft Quadplane},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:71257d1e-c65b-4eb7-9df0-869b9419a8c2},
	year      = {2020}
}

@mastersthesis{uuid:a191b5fc-4050-44b8-9652-4493d44b654c,
	abstract  = {We have set out to develop a drone, based on the existing Delftacopter, capable of soil monitoring via LiDAR remote sensing. The battery was to be replaced by a fuel cell system in order to extend the range threefold to 180km. Unfortunately, the ultimate design is likely unfeasible. Agriculture requires healthy soil and monitoring soil health is fundamental to its maintenance. Soil organic carbon in particular provides energy to the soil’s microorganisms, and is beneficial to water and nutrient retention. In addition, storing carbon in the soil is a form of carbon sequestration, which has become interesting due to the rising levels of carbon dioxide in our atmosphere. Monitoring soil organic carbon is therefore the goal of the drone design. The fuel cell system is a 650Whydrogen fuel cell by Intelligent Energy with a mass of 1290 g, which will be replacing the battery in the base design. Fuel tanks that were considered suitable are the 450 g, 0.5 L, 500 bar and 1350 g, 3 L, 300 bar fuel tanks by Meyer. It was found that one 1350 g and two 450 g fuel tanks were necessary to achieve the desired range of 180 km. However, after more careful drag estimates, this configuration turns out to be too heavy. 4 450 g fuel tanks remains feasible. Results below are based on this amount of fuel tanks. The incorporated LiDAR sensor is one by Velodyne, namely the Puck LITE, with a specified range of 100 m. The LiDAR sensor has a firing cycle of 55.296 &amp;s, almost 20 kHz. Based on previous studies that used LiDAR to measure soil organic carbon, it has been established that a density of 5 data points per square meter is required. Fromour LiDAR parameters it turns out that the optimal flight altitude is 27.5mabove the surface that is to be measured, with a rotation rate of 10 Hz for the LiDAR sensor, when flying at a speed of 20ms¡1. With a flight distance of roughly 116km at 22.5ms¡1 (111km at 20ms¡1), an area of 21.8km2 per flight can be scanned. 1},
	author    = {Cheung, Louis },
	keywords  = {Fuel Cell Aircraft; LIDAR},
	note      = {Aravind, P.V. (mentor); Bhattacharya, Nandini (graduation committee); Remes, Bart (graduation committee); Tambi, Yash (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Applied Sciences; TU Delft Electrical Engineering, Mathematics and Computer Science},
	title     = {Fuel Cell Drone for Soil Monitoring},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:a191b5fc-4050-44b8-9652-4493d44b654c},
	year      = {2020}
}

@mastersthesis{uuid:256f7044-862d-4b53-b395-973dadbb7a00,
	abstract  = {Spiking neural networks are notoriously hard to train because of their complex dynamics and sparse spiking signals. However, in part due to these properties, spiking neurons possess high computa- tional power and high theoretical energy efficiency. This thesis introduces an online, supervised, and gradient-based learning algorithm for spiking neural networks. It is shown how gradients of temporal signals that influence spiking neurons can be calculated online as an eligibility trace. The trace rep- resents the temporal gradient as a single scalar value and is recursively updated at each consecutive iteration. Moreover, the learning method uses approximate error signals to simplify their calculation and make the error calculation compatible with online learning. In several experiments, it is shown that the algorithm can solve spatial credit assignment problems with short-term temporal dependencies in deep spiking neural networks. Potential approaches for improving the algorithm’s performance on long-term temporal credit assignment problems are also discussed. Besides the research on spiking neural networks, this thesis includes an in-depth literature study on the topics of neuromorphic computing and deep learning, as well as extensive evaluations of several learning algorithms for spiking neural networks},
	author    = {Büller, Bas },
	keywords  = {Spiking Neural Networks(SNNs)); Supervised Learning; Online learning; Neuromorphic computing},
	note      = {de Croon, Guido (mentor); Paredes Valles, Federico (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Supervised Learning in Spiking Neural Networks},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:256f7044-862d-4b53-b395-973dadbb7a00},
	year      = {2020}
}

@mastersthesis{uuid:64d40dfc-d852-4688-b8f4-af37f3e9704c,
	abstract  = {End-to-end trained Convolutional Neural Networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by improving the architecture and setting a new benchmark on the publicly available MPI-Sintel dataset. Instead, in this article, we investigate how deep neural networks estimate optical flow. By obtaining an understanding of how these networks function, more can be said about the behavior of these networks in unexpected scenarios and how the architecture and training data can be improved to obtain a better performance. For our investigation, we use a filter identification method that has played a major role in uncovering the motion filters present in animal brains in neuropsychological research. The method shows that the filters in deep neural networks are sensitive to a variety of motion patterns. Not only do we find translation filters, as demonstrated in animal brains, but thanks to the easier measurements in artificial neural networks, we even unveil dilation, rotation and occlusion filters. Furthermore, we find similarities in the refinement part of the network and the perceptual filling-in process which occurs in the mammal primary visual cortex.},
	author    = {de Jong, David },
	keywords  = {optical flow; Gabor filters; convolutional neural networks; neurospychology},
	note      = {de Croon, G.C.H.E. (mentor); Paredes-Vallés, Federico (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {How do deep neural networks perform optical flow estimation?: A neuropsychology-inspired study},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:64d40dfc-d852-4688-b8f4-af37f3e9704c},
	year      = {2020}
}

@mastersthesis{uuid:fc2e13cb-4ea1-4aa7-b7f2-1d8d9478daf4,
	abstract  = {hen observing an Autonomous Unmanned Aerial Vehicle(UAV) race, one would be hard-pressed to call it racing as the actual velocities attained are extremely low. This article addresses this shortcoming by proposing a method of generating and executing a racing trajectory for a UAV, through a series of position objectives representative of a racing environment, with the goal of significantly improving the velocity when compared to the current norm of PID controllers. The method consists of applying Nonlinear Model Predictive Control with the capability of dynamically updating the position goal based upon internal state estimation to generate a set of inputs for a UAV. To prove the viability of the proposed method we test by using numerical simulations, a flight simulator environment(Gazebo) and a series of real-world flight tests on the Bebop1 UAV. Through 2 iterations of the testing process it is proven that the method is able to significantly(approximately 1s) decrease the flight time through both simple and more complex short range manoeuvres(2m-4m). However model errors and an inability to fully control thrust on the UAV introduce a significant and consistent position error.},
	author    = {Spronk, Simon },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Li, S. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {An Nonlinear Model Predictive Control Approach to Autonomous UAV Racing Trajectory Generation and Control},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:fc2e13cb-4ea1-4aa7-b7f2-1d8d9478daf4},
	year      = {2020}
}

@mastersthesis{uuid:88e689a1-eb19-4e48-91b2-3b122a824503,
	abstract  = {When observing an Autonomous Unmanned Aerial Vehicle(UAV) race, one would be hard-pressed to call it racing as the actual velocities attained are extremely low. This article addresses this shortcoming by proposing a method of generating and executing a racing trajectory for a UAV, through a series of position objectives representative of a racing environment, with the goal of significantly improving the velocity when compared to the current norm of PID controllers. The method consists of applying Nonlinear Model Predictive Control with the capability of dynamically updating the position goal based upon internal state estimation to generate a set of inputs for a UAV. To prove the viability of the proposed method we test by using numerical simulations, a flight simulator environment(Gazebo) and a series of real-world flight tests on the Bebop1 UAV. Through 2 iterations of the testing process it is proven that the method is able to significantly decrease the flight time ()through both simple and more complex short range manoeuvres(2m-4m). However model errors and an inability to fully control thrust on the UAV introduce a significant and consistent position error.},
	author    = {Spronk, Simon },
	keywords  = {},
	note      = {de Croon, Guido (mentor); Li, Shuo (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {An NonlinearModel Predictive Control Approach to Autonomous UAV Racing Trajectory Generation &amp; Control},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:88e689a1-eb19-4e48-91b2-3b122a824503},
	year      = {2020}
}

@mastersthesis{uuid:48040e88-f507-4676-a5da-2b701a07f387,
	abstract  = {Flying insects are capable of autonomous vision-based navigation in cluttered environments, reliably avoiding objects through fast and agile manoeuvres. Meanwhile, insect-scale micro air vehicles still lag far behind their biological counterparts, displaying inferior performance at a fraction of the energy efficiency. In light of this, it is in our interest to try and mimic flying insects in terms of their vision-based navigation capabilities, and consequently apply gained knowledge to a manoeuvre of relevance. This thesis does so through evolving spiking neural networks for controlling divergence-based landings of micro air vehicles, while minimising the network's spike rate. We demonstrate vision-based neuromorphic control for a real-world, continuous problem, as well as the feasibility of extending this controller to one that is end-to-end-learnt, and can work with an event-based camera. Furthermore, we provide insight into the resources required for successfully solving the problem of divergence-based landing, showing that high-resolution control can be learnt with only a single spiking neuron. Finally, we look at evolving only a subset of the spiking neural network's available hyperparameters, suggesting that the best results are obtained when all parameters are affected by the learning process.},
	author    = {Hagenaars, Jesse },
	keywords  = {spiking neural networks; optical flow; micro air vehicles; neuroevolution},
	note      = {de Croon, G.C.H.E. (mentor); Paredes-Vallés, F. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Evolved Neuromorphic Control for High Speed Divergence-based Landings of MAVs},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:48040e88-f507-4676-a5da-2b701a07f387},
	year      = {2020}
}

@mastersthesis{uuid:32a90402-a577-4383-afe3-f8a865a287dc,
	abstract  = {Increasing endurance is a major challenge for battery-powered aerial vehicles. A method is presented which makes use of an updraft around obstacles to decrease the power consumption of a fixed-wing, unmanned aerial vehicle. Simulatory results have shown the conditions that the flight controller can fly in.<br/>The effect of a change in wind velocity, wind direction and updraft has been analysed. The simulations showed that an increase in either updraft or absolute wind direction decrease the throttle consumption.<br/>A change in wind velocity results in a shift of the flight controller’s boundaries. The simulations achieved sustained flight at 0 per cent throttle. The practical, autonomous tests reduced the average throttle down to 4.5 per cent in front of the boat. The unfavourable wind conditions and inaccuracies explain this minor<br/>throttle requirement during the final experiment.},
	author    = {de Jong, C.P.L. },
	keywords  = {soaring; updraft; orographic lift; fixed-wing; flight control; simulation; practical tests},
	note      = {Remes, B.D.W. (mentor); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Never Landing Drone},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:32a90402-a577-4383-afe3-f8a865a287dc},
	year      = {2020}
}

@mastersthesis{uuid:96a87f24-c9b1-4a3e-99df-09dc27772609,
	abstract  = {The last years there is a wide interest in UAVs which can be attributed to their low cost and wide range of use in recreational, commercial and scientific applications. Despite the large increase in drones, UAV flights are permitted only in secluded areas. In order to be granted access to public areas, it must prove its capacity to sense and safely avoid collisions with other obstacles. Therefore, the need for a secure and reliable CAS is imperative. In this thesis a<br/>low-cost, low computationally demanding, stereo-based, robust CAS solution for small UAVs is designed, assuming flights primarily in an outdoor environment. In order to address this problem, firstly the existing dense stereo open-source algorithms are reviewed based on their suitability for obstacle avoidance and their computational complexity. Based on a semantic evaluation and profiling, the review concludes that BM should be preferred for low-cost obstacle avoidance and SGBM should be used only in highly textureless environments. Subsequently, since the imperfect accuracy of any existing stereo solution is a fact, a machine learning method is introduced in order to predict the uncertainty of the stereo measurements. This so called “uncertainty map” method assigns an uncertainty value to every image pixel. It was shown that it can successfully predict the uncertainties of BM and SGBM stereo algorithms. Furthermore, a low-cost collision avoidance method was proposed which makes use of uncertainty map in sensing filtering, collision detection and path planning. The evaluation showed that the use of uncertainty map improves<br/>both collision detection and path planning, especially when BM is used. Last but not least, the whole CAS was implemented in an embedded system Raspberry Pi 3 model B+. Results show that real-time execution of the propsoed CAS with a runtime frequency of 4-54 Hz is possible when BM is used and 1-16 Hz when SGBM is used.},
	author    = {Lyrakis, Alex },
	keywords  = {Stereo; Collision Avoidance; Mapping; Path Planning; Uncertainty Map; Low-cost; Raspberry Pi; Image-space},
	note      = {van Dijk, Tom (mentor); de Croon, G.C.H.E. (mentor); Wong, J.S.S.M. (mentor); Verhoeven, C.J.M. (graduation committee); van Genderen, A.J. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Electrical Engineering, Mathematics and Computer Science},
	title     = {Low-cost stereo-based obstacle avoidance for small UAVs using uncertainty maps},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:96a87f24-c9b1-4a3e-99df-09dc27772609},
	year      = {2019}
}

@mastersthesis{uuid:c4170bbd-059d-4874-82f9-958bec2c668e,
	abstract  = {Accurate indoor localization is essential for autonomous robotic agents to perform tasks ranging from warehouse management to remote sensing in greenhouses. Recently Ultra Wideband (UWB) distance measurements have been used to estimate position and velocity indoors. These UWB-measurements are known to be corrupted by a varying bias. Besides, current estimation methods are not suitable for large areas with a low beacon coverage. The goal<br/>of this thesis was therefore twofold. First, a simple bias model was proposed to reduce the influence of the UWB bias while still being implementable on a micro-processor. This model was shown to reduce the measurement error with 50% on validation data. Using this model, UWB-localization in a static beacon-configuration can be quickly improved. Second, an adaptation of the standard Moving Horizon Estimation (MHE) method was proposed that uses a time-window of range measurements to increase the robustness to outliers and is still real-time implementable on a micro-processor. This Moving Horizon Model Parametrization (MH-MP) does not estimate every state in the complete time-window, but only estimates an offset of the initial state in the window. An analysis of simulation data and data gathered in flight has shown that the proposed MH-MP outperforms the Extended Kalman Filter (EKF) in both the<br/>position and velocity estimate and has a comparable computation time. Further research is necessary to investigate the possibility of estimating the UWB-bias model parameters online.},
	author    = {Stroobants, Stein },
	keywords  = {quadrotors; drones; state estimation; state-estimation; moving horizon estimation; least-squares; crazyflie},
	note      = {Keviczky, Tamas (mentor); de Croon, Guido (graduation committee); Li, Shushuai (graduation committee); de Wagter, Christophe (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering},
	title     = {On-board Micro Quadrotor State Estimation Using Range Measurements: A Moving Horizon Approach},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:c4170bbd-059d-4874-82f9-958bec2c668e},
	year      = {2019}
}

@mastersthesis{uuid:91f200f7-4966-4504-bc83-5a87e5550a91,
	abstract  = {Safe navigation in unknown environments is a challenging task for autonomous Micro Aerial Vehicle (MAV) systems. Previous works generally avoid obstacles by assuming that the environment is static. The purpose of this thesis work is to develop a MAV system that can navigate autonomously and safely in dynamic environments. We present an onboard vision-based approach for the avoidance of moving obstacles in dynamic environments. This approach uses a state-of-art visual odometry algorithm to estimate the pose of MAV and an efficient obstacle sensing method based on stereo image pairs to estimate the center position, velocity, and size of the obstacles. Considering the uncertainties of the estimations, a chance-constrained Model Predictive Controller (MPC) is applied to achieve robust collision avoidance. The method takes into account the MAV’s dynamics, state estimation and the obstacle sensing results ensuring that the collision probability between the MAV and each obstacle is below a specified threshold. The proposed approach is implemented on a designed experimental platform that consists of a quadrotor, a depth camera, and a single-board computer, and is successfully tested in a variety of environments, showing effective online collision avoidance of moving obstacles.},
	author    = {Lin, Jiahao },
	keywords  = {Autonomous Navigation; Micro Aerial Vehicle; Obstacle Avoidance; Model Predictive Contrl},
	note      = {de Croon, Guido (mentor); Alonso Mora, Javier (mentor); Ferrari, Riccardo M.G. (graduation committee); Zhu, Hai (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics},
	title     = {Real-time Vision-based Autonomous Navigation of MAV in Dynamic Environments},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:91f200f7-4966-4504-bc83-5a87e5550a91},
	year      = {2019}
}

@mastersthesis{uuid:99f41ef5-f2c9-4a0a-9b89-0245e106f6de,
	abstract  = {The e-sport of drone-racing involves human pilots to race against time. Recently, drone races have also gone fully-autonomous. As a result, these agile robotic platforms not only pose challenges of flying fast to the participating pilots but also create challenges for the flight control computers. As a result, the concept of autonomous drone racing has gained significant attention from research groups around the world. These races aim to push the boundaries of perception and control algorithms, while simultaneously mitigating the real-world uncertainty of execution on autonomous systems. While perception algorithms face challenges due to limited feature detection, high motion blur and computational requirements, control algorithms face challenges of convergence to the desired trajectories that are planned out in the race arena. <br/>This thesis addresses the challenge of control for racing, which is responsible for guiding the drone to design and track desired trajectories for fast flights. The control sub-modules of racing drones are responsible for generating trajectories for fastest possible flights and also for obeying these generated commands. Additionally, the requirement of limited algorithm complexity is added to match the philosophy of computationally efficient algorithms at the Micro Air Vehicle Laboratory. However, to address the requirements of these control sub-modules, the prerequisite of accurate state estimation always persists. Assigning control actions to a robot without information on the current state of the robot is rather unwise. As a result, this thesis first aims to perform accurate state estimation before designing controllers for time-optimal trajectory tracking. Again, another constraint of using only a single sensor (i.e. the Inertial Measurement Unit) is added to make the drone race in GPS denied environments. As a result, the goal of the thesis is two-fold i.e. making accurate state estimators while using limited sensors and designing optimal controllers for taking the quickest trajectory through the arena. To achieve the goal of accurate state estimation, existing techniques are studied. Several features from each of these methods are selected to design a new estimator. To achieve the goal of time-optimal trajectory generation, firstly, the flaws of traditional control methods are pointed out. A new optimal-control technique is proposed, which makes use of fundamental principles dating back several decades. This principle is then fused along with present-day optimization solvers. Finally, the proposed state estimation and control algorithm are compared against prior (benchmarked) techniques in the area. Compared to existing optimal control techniques, the proposed algorithm leads to faster trajectories and consumes less computational power onboard.},
	author    = {Sheth, Nilay },
	keywords  = {drone-racing; optimal-control; state-estimation},
	note      = {de Croon, Guido (mentor); de Wagter, Christophe (mentor); Langendoen, Koen (mentor); Zuñiga Zamalloa, Marco (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Electrical Engineering, Mathematics and Computer Science},
	title     = {State Estimation and Optimal Control for Racing Drones: In search of control algorithms for competing against human pilots},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:99f41ef5-f2c9-4a0a-9b89-0245e106f6de},
	year      = {2019}
}

@mastersthesis{uuid:cc607dbb-7116-4c9f-991f-988d832833a9,
	abstract  = {This study investigates the wing deformation of the flapping-wing micro air vehicle (MAV) DelFly II in various flight configurations. Experiments were carried out with the MAV tethered in a windtunnel test section. To determine the best suited measurement approach, a trade-off study was carried out which showed that a point tracking approach with background illumination is most suitable. The therefore used high-speed camera pair and illumination were mounted on the same rotating frame with the DelFly, which allowed adequate viewing axes of the wings at for all pitch angles. Processing was done a purpose-build algorithm, allowing 136 points per wing to be measured simultaneously with an average lost point ratio of 3.4 % and an estimated accuracy of 0.25 mm. Results of hovering flight show some previously unnoticed behaviors. First, it was noted that the upper and lower wing on each side do not deform purely symmetric but show some considerable asymmetric behavior like heave and camber production. Furthermore, the upper wing shows a torsional wave and recoil behavior at faster flapping frequencies, which was shown to be beneficial in insect flight. Lastly, it was found that an air-buffer remains present between the wing surfaces at all times of the clap-and-peel motion (apart from the root trailing edge). This air-buffer increases once freestream velocity is added, which is investigated during the climbing flight study. Here, the reduced angle of attack of the wing is assumed to reduce the wing loading at faster climb, resulting in lower deformations outside the clap-and-peel motion. The isolated effect of a body pitch angle is also studied. Here, the asymmetrical freestream direction results in larger asymmetries such as wing alignment with the freestream direction and reduced camber and even camber reversal during the upstroke. In forward flight the pitch angle is changed simultaneously with the flapping frequency and freestream velocity. Due to the non-linear properties the wing deforms not directly as a superposition of the individual effects. Deviations are mostly present in increased asymmetry in incidence angle, while the camber behaves more linear and the clap-and-peel motion also remains relatively unchanged. The torsional wave and recoil are here however reduced. Descending flight was also tested. Velocities below 1m/s result in relatively minor deformation changes, while faster descent leads to large flapping frequency fluctuations, making interpretation of the results impossible.},
	author    = {Heitzig, Dorian },
	keywords  = {DelFly; Flapping Wing; Deformation; Fluid Structure Interaction},
	note      = {van Oudheusden, B.W. (mentor); De Breuker, R. (graduation committee); de Wagter, C. (graduation committee); Olejnik, D.A. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Wing deformation measurements of the DelFly II in different flight conditions},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:cc607dbb-7116-4c9f-991f-988d832833a9},
	year      = {2019}
}

@mastersthesis{uuid:ce520f94-bd3c-41a5-9ddd-edfdf6ead35e,
	abstract  = {The exceptional flight capabilities of insects have long amazed and inspired researchers and roboticists striving to make Micro Aerial Vehicles (MAVs) smaller and more agile. It is well known that optical flow plays a prominent role in insect flight control and navigation, and hence it is being increasingly investigated for applications in flying robots as well. However, optical flow based strategies for estimation and stabilization of orientation remain obscure in literature. In this report, we introduce a novel state estimation algorithm based on optical flow measurements and the knowledge of efference copies. The proposed technique estimates the following states of a flying robot (constrained to move with three degrees of freedom): roll angle, rate of change of roll angle, horizontal and vertical components of velocity and height. The estimator only utilizes the knowledge of control inputs and optical flow measurements obtained from a downward looking monocular camera. Through non-linear observability analysis, we theoretically prove the feasibility of estimating the attitude of a MAV using ventral flow and divergence measurements. Based on the findings of the observability analysis, an extended Kalman filter state estimator is designed and its performance is verified in simulations and through flight data recorded on a real flying robot. To the best of our knowledge, the introduced strategy is the first attitude estimation technique that utilizes monocular optical flow as the only sensory information.<br/><br/>Besides the investigation on optical flow based attitude estimation technique, this thesis presents a comprehensive literature survey on the main topics relevant to the work.},
	author    = {Chatterjee, Abhishek },
	keywords  = {Bio-inspiration; Optical Flow; Insect flight; Attitude Estimation; Micro Aerial Vehicle},
	note      = {de Croon, G.C.H.E. (mentor); Olejnik, D.A. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Monocular Optical Flow based Attitude Estimation in Micro Aerial Vehicles: A Bio-Inspired Approach},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:ce520f94-bd3c-41a5-9ddd-edfdf6ead35e},
	year      = {2019}
}

@mastersthesis{uuid:38547b1d-0535-4b30-a348-67ac40c7ddcc,
	abstract  = {Online Reinforcement Learning is a possible solution for adaptive nonlinear flight control. In this research an Adaptive Critic Design (ACD) based on Dual Heuristic Dynamic Programming (DHP) is developed and implemented on a simulated Cessna Citation 550 aircraft. Using an online identified system model approximation, the method is independent of prior model knowledge. The agent consists of two Artificial Neural Networks (ANNs) which form the Adaptive Critic Design and is supplemented with a Recursive Least Squares (RLS) online model estimation. The implemented agent is demonstrated to learn a near optimal control policy for different operating points, which is capable of tracking pitch and roll rate while actively minimizing the sideslip angle in a faster than real-time simulation. Providing limited model knowledge is shown to increase the learning, performance and robustness of the controller.},
	author    = {Kroezen, Dave },
	keywords  = {Reinforcement Learning (RL); Adaptive Control; Online Learning; Adaptive Critic Designs; Flight Control Systems; Adaptive Flight Control; Machine Learning},
	note      = {van Kampen, E. (mentor); de Croon, G.C.H.E. (graduation committee); Mitici, M.A. (graduation committee); Pan, W. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
	title     = {Online Reinforcement Learning for Flight Control: An Adaptive Critic Design without prior model knowledge},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:38547b1d-0535-4b30-a348-67ac40c7ddcc},
	year      = {2019}
}

@mastersthesis{uuid:a37b96b7-90de-4f22-99ce-87cc97d414d9,
	abstract  = {Tailless flapping wing micro aerial vehicles (FMWAV) are known for their light weight and agility. However, given the fact that these FWMAVs have been recently developed, their flight dynamics have not yet been fully explained. In this paper we will develop local time-averaged longitudinal grey-box models based on closed-loop system identification techniques, where free-flight experimental data, obtained from the DelFly Nimble, is used to estimate and validate the local grey-box models. With these models we can take the first steps towards fully understanding the flight dynamics of tailless FWMAVs. The consequence of the tailless configuration is inherent instability and therefore tailless FWMAVs are generally more complex, compared to its tailed counterpart, and require a active feedback control system. The active feedback control system introduces additional challenges to the system identification process since it follows that feedback control works against the objectives of system identification. Dynamic effects that play a major role when studying the dynamic behaviour of FWMAVs are the sub-flap and the flap cycle-averaged effects. However, in this paper, we are only interested in modelling the flap cycle-averaged (time-averaged) effects of the DelFly Nimble. Based on this approach, grey-box models were estimated and validated for airspeeds near hover condition 0 m/s, up to 1.0 m/s forward flight. Despite the complexity of the system, we were able to obtain low-order local models that are both efficient and accurate (R2 values up to 0.92) to predict the flight dynamic behaviour of the DelFly Nimble and can therefore be used for stability analysis, simulation and control design.},
	author    = {Nijboer, Jorgen },
	keywords  = {},
	note      = {de Visser, Coen (mentor); Karasek, Matej (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Longitudinal grey-box model identification of a tailless flapping-wing MAV based on free-flight data},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:a37b96b7-90de-4f22-99ce-87cc97d414d9},
	year      = {2019}
}

@mastersthesis{uuid:57cfce9e-75aa-42f1-bed4-64b64b927fb4,
	abstract  = {Vision based control allows Micro Air Vehicles (MAV) to move autonomously in GPS-denied environments, for example in indoor applications. An open issue in this field is landing on an unknown platform. The difficulty in visual control w.r.t. such an unknown platform, is a lack of scale. Without knowledge of the scale of offsets and object sizes (without height knowledge from GPS) it is difficult to determine an appropriate response from the controller. A control algorithm is designed to fit these requirements using an adaptation of an optical flow divergence based landing scheme, combined with an Image Based Visual Servoing approach applied to features in the Virtual Camera. The approach leads to satisfactory behavior in Gazebo simulations. It results in a robust controller for a range of starting heights and divergence settings.},
	author    = {Blom, Jari },
	keywords  = {Autonomous; Drone; Visual Servoing; Optical Flow; Tracking},
	note      = {de Croon, G.C.H.E. (graduation committee); Scheper, K.Y.W. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
	title     = {Onboard Visual Control of a Quadcopter MAV performing a Landing Task: on a Platform of Unknown Size and Location},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:57cfce9e-75aa-42f1-bed4-64b64b927fb4},
	year      = {2019}
}

@mastersthesis{uuid:0cd4f9e3-faf3-4e65-995c-7dd401c8da4c,
	abstract  = {In this work, we model a multi-robot formation planning and control task as an optimization problem, which we solve on-line and in a decentralized manner using the Stochastic Optimal Control (SOC) framework. Typically, the solution of a SOC problem requires solving the Hamilton-Jacobi-Bellman (HJB) equation for all system states and controls. However, this operation becomes intractable when high-dimensional systems are used. In recent years, advances on a certain type of SOC problem, which can be efficiently solved by sampling from a diffusion process have been presented and are better known as path integral (PI) control. We build upon this theory and implement a decentralized formulation of the PI algorithm to compute the optimal controls of real Micro Aerial Vehicles (MAVs) flying in formation using solely on-board computational resources. One challenging aspect of the PI control method is the efficient sampling of useful trajectories. It is not clear how to guide the samples towards the optimal states. To this end, we propose a probe enhanced importance sampling (PEIS) method which performs a coarse exploration of the state space with the objective of identifying an optimal guiding trajectory around which the samples are taken. The feasibility of the proposed method is shown by means of simulation and real-hardware experiments with up to four MAVs in an indoor environment.},
	author    = {Bendriş, Bianca },
	keywords  = {},
	note      = {de Croon, Guido (mentor); McGuire, Kimberly (graduation committee); Kappen, Bert (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Decentralized Stochastic Optimal Control for a Swarm of Micro Aerial Vehicles},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:0cd4f9e3-faf3-4e65-995c-7dd401c8da4c},
	year      = {2019}
}

@mastersthesis{uuid:ec42642a-5872-4177-b514-c3679f6b4055,
	abstract  = {With the growing population the agricultural industry needs to find and implement new methods for enhancing food production. Using a Micro Aerial Vehicle (MAV) in Precision Agriculture (PA) offers a large number of benefits such as enabling the farmer to create targeted strategies to increase crop yield, reduced waste and halt the spread of diseases. Despite these advantages, the use of MAVs, particularly in greenhouses, is still very limited. To this end, this thesis seeks to combine, improve and implement existing strategies to solve the persistent surveillance task for a swarm of MAVs operating in a greenhouse environment.<br/>Broadly speaking, the persistent surveillance task seeks to find the optimal paths for a swarm of MAVs such that every point within the Mission Space (MS) is visited and they must minimise the time between successive visits. This will ensure that the MAVs are able fly through the entire greenhouse to collect up-to-date data about all the crops and the local environment. Naturally, on a physical system one has to deal with the limited flight times of the MAVs. This factor becomes very important to the effectiveness of the solution and is critical to the continuous operation of the MAVs.<br/>In literature, many methods have be proposed to solve this task, but the majority are still only tested in simulation. As a result, many works do not consider some physical constraints that will be applied to the system during implementation in a real-world setting. For example, in most cases the authors do not consider the limited fuel available to the agents or they do not consider a practical alternative indoor positioning system to GPS. In this work the problem has been divided into two main sub-tasks, namely; the persistent surveillance task and the refuelling task.<br/>For the persistent surveillance task it was decided to implement a reactive controller, in the form of an evolved Neural Network (NN), which was run on-board the MAVs. The NN used positional information from the other members of the swarm along with limited environmental information to supply its MAV with a command velocity. These NN controllers could achieve coverage levels of over 95% while simultaneously avoiding collisions between 8 MAVs in a 25m x 25m MS. Later, this method was shown to be robust to failures and scalable in terms of both MS and swarm size.<br/>When dealing with the fuel constraints, a Behaviour Tree (BT) was used to determine when the MAV should return to the depot. Surprisingly, when combined with the NN controllers the system experienced an increase in performance across all the defined metrics. No MAV failed due to low fuel levels, coverage increased to 97.41%, average cell age to 52.39s and the number of tests were no collisions were recorded more than doubled. This increase in performance was attributed to the fact that the refuelling periodically drew the MAV towards the centre of the MS. This is counter to the evolved behaviours of the NN where the MAVs would mainly focus their attention around the edges of the MS.<br},
	author    = {Fijen, Thomas },
	keywords  = {Persistent Surveillance; MAV; NEAT; NN},
	note      = {Keviczky, T. (mentor); de Croon, G.C.H.E. (mentor); Coppola, M. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control},
	title     = {Persistent Surveillance of a Greenhouse: Evolved neural network controllers for a swarm of UAVs},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:ec42642a-5872-4177-b514-c3679f6b4055},
	year      = {2019}
}

@mastersthesis{uuid:82cb0f68-061e-4346-b536-a35a61621e51,
	abstract  = {Autonomous MAV are an emerging technology that supports a wide range of applications such as medical delivery or finding survivors in disaster scenarios. As flying in such missions is difficult the robust estimation of an MAV's state within its environment is crucial to ensure safe operation. In indoor scenarios, cameras are one of the predominant choices for state estimation sensors. This requires Computer Vision algorithms to interpret the obtained high dimensional signal. An application that allows the competitive evaluation of control and state estimation algorithms is MAV Racing such as the IROS 2018 Autonomous Drone Race. Thereby a race court consisting of several race gates has to be followed. For a fast flight during such a race court the detection of the racing gates with a camera can be used in a high level control loop. As these objects consist only of small structures that are spread across large parts of the image, this gives rise to a challenging Object Detection problem. In recent years CNN showed promising results on various vision tasks. However, due to their computational complexity the deployment on mobile devices remains a challenge. Furthermore, CNN typically require a vast amount of training data. Finally, the objects typically studied in Object Detection consist of solid and complex features which is not the case for racing gates. Therefore, this work defines the class of EWFO and studies their detection on MAV with YoloV3. Thereby, the training data is created with a graphical engine. We are interested in how to detect EWFO with a CNN on a MAV, using synthetic data. We conduct several simple experiments about EWFO in simulation and compare their detection to more filled objects. Subsequently experiments in a more challenging environment such as an MAV race are conducted. The experiments show how EWFO are harder to detect than filled objects as the detector can be confused to patterns present in the empty part. Particularly for larger objects the detection performance decreases. We give several recommendations on how to generate data for the detection of EWFO on MAV. These include how to add variations in background as well as the camera placement. Finally, we study the incorporation of image augmentation techniques to transfer the detector to the real world. We can report that especially modelling lens distortion improves the performance on the real data. Nevertheless, a reality gap remains that can not fully be explained. Furthermore, different architectures are studied for the detection of EWFO. It can be seen how a relatively shallow network of 9 layers can be used for the detection of EWFO on MAV. A further reduction in weights leads to a gradual decrease in performance. Based on the gained insights the deployment of a detector on the example system JeVois is studied. A detection performance/speed trade-off is evaluated. The final detector achieves 32% average precision at a frame rate of 12 Hz on a real world test set created during this work. The gained insights can be used to deploy the detector in a control loop for MAV. This ensures the safe flight through a racing court of an autonmous drone race. The gained insights about the detection of EWFO can be transferred to objects with similar properties},
	author    = {Dürnay, Philipp },
	keywords  = {Computer Vision; Deep Learning; Convolutional Neural Network; Object Detection},
	note      = {Tax, D.M.J. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Electrical Engineering, Mathematics and Computer Science},
	title     = {Detecting Empty Wireframe Objects on Micro-Air Vehicles: Applied for Gate Detection in Autonomous Drone Racing},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:82cb0f68-061e-4346-b536-a35a61621e51},
	year      = {2018}
}

@mastersthesis{uuid:bc31203c-1956-4665-a92a-8203881f22ce,
	abstract  = {Previous years have seen a rise in the use of Unmanned Aerial Vehicles (UAVs). Reaching a large endurance and range while being able to perform Vertical Take-Off and Landing (VTOL) landings allows a broad range of applications. For this purpose the DelftaCopter (DC) was developed, a tilt-body tailsitter UAV. It hovers using a single helicopter rotor for lift and transitions to forward flight by pitching its body down by 90°. In this forward flight state, wings generate the lift, while the helicopter rotor now provides thrust. The single rotor is more efficient than using multiple smaller rotors and helicopter swashplate is used for attitude and speed control. The heavy single helicopter rotor introduces significant gyroscopic moments, as is the case for all helicopters. In contrast with normal helicopters, the DC has a heavy fuselage putting the attitude dynamics between a helicopter and aircraft. In previous research, a controller based on a model incorporating the rotor as a rotating cylinder was implemented. This controller was unable to counteract the gyroscopic pitch-roll coupling, leading to the question of this thesis: how should the DC be modeled to allow control design. <br/>In this thesis, the previous model is called the Cylinder Dynamics (CD) model, and is compared with another model from literature. The latter model, in this thesis called the Tip-Path Plane (TPP) model, includes the flapping dynamics through the tip-path plane dynamics and is also a linear state-space model. In flight tests, chirps were used to cover a broad frequency range. Fitting both the CD and TPP models on this flight test data, it is shown that the CD model lacks accuracy in the high-frequency area, while the TPP is able to accurately model these dynamics. This shows that the flapping dynamics are important to the attitude dynamics of the DC. An Linear Quadratic Regulator (LQR) controller was implemented based on the fitted TPP model, and shows adequate tracking performance, further validating the applicability of the model to the DC. For forward flight, extensions to the hover models are proposed. The extension including the elevator and aerodynamic damping is shown to simulate key dynamics of the DC in forward flight with reasonable accuracy. The parameters and eigenfrequencies of this model are not significantly different from the hover model. Therefore it can be concluded that the gyroscopic effect plays an important role in forward flight attitude dynamics. Another extension which estimates of angle of attack and sideslip using high-pass filtered rotational rates, yields better accuracy, but significantly changes the model parameters also present in the hover model. More research with angle of attack and sideslip vanes could validate this modeling approach. It was also found that for a new version of the DC with a smaller, more quickly rotating rotor, the modeling done before resulted in much worse fits. It was shown that the CD and TPP model response is much more comparable for this version. Control performance also suffers due to this lower accuracy model fit. Further research is required to understand why this is the case.},
	author    = {Meulenbeld, Joost },
	keywords  = {DelftaCopter; System Identification; UAV; Control; Helicopter Model; Flapping Dynamics; Hybrid; Tail-sitter},
	note      = {de Wagter, C. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
	title     = {Attitude modeling of the DelftaCopter: a system identification approach},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:bc31203c-1956-4665-a92a-8203881f22ce},
	year      = {2018}
}

@mastersthesis{uuid:28fad2a0-4b4c-47f4-8930-01708f4b52d1,
	abstract  = {We investigate how an Unmanned Air Vehicle (UAV) can detect manned aircraft with a single microphone. In particular, we create an audio data set in which UAV ego-sound and recorded aircraft sound can be mixed together, and apply convolutional neural networks to the task of air traffic detection. Due to restrictions on flying UAVs close to aircraft, the data set has to be artificially produced, so the UAV sound is captured separately from the aircraft sound. The aircraft data set is collected at Lelystad airport by capturing flyovers with a microphone array. It is mixed with UAV recordings, during which labels are given indicating whether the mixed recording contains aircraft audio or not. The mixed recordings are the input for a model that determines whether an aircraft is present or not. The model is a CNN which uses the features MFCC, spectrogram or Mel spectrogram as input. For each feature the effect of UAV/aircraft amplitude ratio, the type of labeling, the window length and the addition of third party aircraft sound database recordings is explored. The results show that the best performance is achieved using the Mel spectrogram feature. The performance increases when the UAV/aircraft amplitude ratio is decreased, when the time window is increased or when the data set is extended with aircraft audio recordings from a third party sound database. It is not desirable to train the model on distant approaches and test them on nearby approaches as the performance then drops. The results also prove that the performance increases the closer the aircraft is. Although the currently presented approach has a number of false positives and false negatives, that is still too high for real-world application, this study indicates multiple paths forward that can lead to an interesting performance. In addition, the data set is provided as open access, allowing the community to contribute to the improvement of the detection task.},
	author    = {Wijnker, Dirk },
	keywords  = {Hear-and-avoid; Convolutional Neural Network; Spectrogram; Mel; UAV; MFCC},
	note      = {van Dijk, Tom (mentor); de Croon, G.C.H.E. (mentor); de Wagter, C. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Hear-and-avoid for UAVs using convolutional neural networks},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:28fad2a0-4b4c-47f4-8930-01708f4b52d1},
	year      = {2018}
}

@mastersthesis{uuid:b0394f21-302c-484d-a6dc-031f5860c521,
	abstract  = {Both quadcopter Micro Aerial Vehicles (MAVs) and Flapping Wing MAVs (FWMAVs) are constrained in Size, Weight and Processing power (SWaP) in order to achieve flight tasks that include attitude and velocity stabilisation, as well as obstacle avoidance. <br/>Conventional sensory and control approaches, such as those relying on inertial, visual and Global Positioning System (GPS) sensors, can fulfil these tasks using sensor fusion. However such approaches do not score well in terms of SWaP criteria. <br/>Very simple proportional feedback control laws using single optical flow vectors from very basic high frame-rate low-resolution cameras provide a promising path to achieve aforementioned tasks. <br/>This thesis shows that in theory these control laws are well suited for stabilising a FWMAV, and could be used for a high-drag adapted quadcopter MAV within bounds. Simulations confirm these findings and illustrate robustness to noise and additional emergent behaviour such as sideways wall avoidance and trajectory following, however simulations also show that disparity between walls can lead to unintended rotational behaviour during vertical translation. <br/>The system is tested in experiment on a quadcopter-like setup with onboard processing, using only ADNS 9800 computer mouse optical flow sensors for flight control. Results show that the system behaves similarly to simulation, however the sensory configuration used is highly dependent on texture in environment and light conditions. <br/>For future work it is recommended to investigate optical flow sensors in more detail to obtain reliable output on a vibrating platform (such as a FWMAV) in a broader range of texture and light conditions. Preliminary results from theory, simulation and experiment indicate that the addition of derivative feedback could strongly enhance performance on a quadcopter MAV and remove the requirement for high drag.},
	author    = {van Vrede, Daan },
	keywords  = {Optical flow; MAV; FWMAV; Micro Aerial Vehicle; Flight Control Systems; Bio-inspired; quadcopter; Atalanta; Embedded Systems; Optical flow sensors; Stability Analysis; PD control; P feedback; PD feedback},
	note      = {Goosen, J.F.L. (mentor); de Croon, G.C.H.E. (mentor); Breedveld, P. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering},
	title     = {Flight control and collision avoidance for quadcopter and flapping wing MAVs using only optical flow: Theory, Simulation and Experiment},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:b0394f21-302c-484d-a6dc-031f5860c521},
	year      = {2018}
}

@mastersthesis{uuid:34ce9384-9352-41bc-99ce-2a54bd1f3361,
	abstract  = {Target search in an obstacle filled environment is a practically relevant challenge in robotics that has a huge impact in the society. The wide range of applications include searching for victims in a search and rescue operation, detecting weeds in precision agriculture, patrolling borders for military and navy, automated census of endangered species in a forest etc. An efficient target search algorithm provides a data acquisition platform with least human intervention, thus improving the quality of life of humans. This thesis aims at introducing a general path planning algorithm for UAVs flying at different heights in an obstacle filled environment, searching for targets in the ground field. An adaptive informative path planning (IPP) algorithm is introduced that simultaneously trade off between area coverage, field of view, height dependent sensor performance and obstacle avoidance. It plans under uncertainties in the sensor measurements at varying heights, and is robust against wrong target detections. It generates an optimal fixed horizon plan in the form of a 3D minimum-snap trajectory that maximizes the information gain in minimum flight time by providing maximum area coverage, without any collision with the obstacles. The resulting planner is modular in terms of the mapping strategy, environment complexity, different target, changes in the sensor model and optimizer used. The planner is tested against varying environmental complexities, demonstrating its capability in handling a wide range of possible environments. The planner outperforms other planners like non-adaptive IPP planner, coverage planner and random sampling planner, by demonstrating the fastest decrease in map error while flying for a fixed time budget. A proof of concept for the algorithm is provided through real experiments by running the algorithm on a UAV flying inside a lab environment, searching for targets lying on the ground. All the targets were successfully found and mapped by the algorithm, demonstrating its applicability in a real-life target search problem.},
	author    = {Anil Meera, Ajith },
	keywords  = {Path Planning; Robotics; Unmanned Aerial Vehicle; Computer Vision; Optimization},
	note      = {Siegwart, Roland (mentor); Wisse, M. (mentor); Popović, Marija (mentor); Millane, Alexander (mentor); Pan, W. (graduation committee); Alonso Mora, J. (graduation committee); Mohajerin Esfahani, P. (graduation committee); Delft University of Technology (degree granting institution); ETH Zürich (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering},
	title     = {Informative Path Planning for Search and Rescue using a UAV},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:34ce9384-9352-41bc-99ce-2a54bd1f3361},
	year      = {2018}
}

@mastersthesis{uuid:aa13959b-79b9-4dfc-b5e0-7c501d9d3e2f,
	abstract  = {The combination of Spiking Neural Networks and event-based vision sensors holds the potential of highly efficient and high-bandwidth optical flow estimation. This thesis presents, to the best of the author’s knowledge, the first hierarchical spiking architecture in which motion (direction and speed) selectivity emerges in a biologically plausible unsupervised fashion from the stimuli generated with an event-based camera. A novel adaptive neuron model and Spike-Timing-Dependent Plasticity formulation are at the core of this neural network governing its spike-based processing and learning, respectively. After convergence, the neural architecture exhibits the main properties of biological visual motion systems: feature extraction and local and global motion perception. To assess the outcome of the learning, a shallow conventional Artificial Neural Network is trained to map the activation traces of the penultimate layer to the optical flow visual observables of ventral flows. The proposed solution is validated for simulated event sequences with ground truth measurements. Experimental results show that accurate estimates of these parameters can be obtained over a wide range of speeds.},
	author    = {Paredes Valles, Fede },
	keywords  = {Event-based vision; Neuromorphic; Optical flow; Spiking Neural Networks; Spike-Timing-Dependent Plasticity},
	note      = {de Croon, G.C.H.E. (mentor); Scheper, K.Y.W. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
	title     = {Neuromorphic Computing of Event-Based Data for High-Speed Vision-Based Navigation},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:aa13959b-79b9-4dfc-b5e0-7c501d9d3e2f},
	year      = {2018}
}

@mastersthesis{uuid:c75ab7d9-e711-4e0c-93ea-ff092e2e9131,
	abstract  = {Velocity estimation based on visual information is a well- researched topic. Traditional approaches usually rely on how a given feature or features change between successive images in a sequence. However, a single static image might contain motion information that could potentially be lever- aged to estimate the optical flow. It can be hypothesized that motion blur and context of the scene are two sources of mo- tion information in static images. This research work has two main goals, one is to investigate the prospect of using a learning-based framework to model a mapping directly to camera ego-motion velocity. The second goal is to ana- lyze the contributing features in learning such a mapping. Experiments show that the model is able to learn velocity based on context of the scene but performs better when in- put images contain motion blur.<br},
	author    = {Napolean, Yeshwanth },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); van Gemert, J.C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Estimation of ego-motion velocities from single static images},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:c75ab7d9-e711-4e0c-93ea-ff092e2e9131},
	year      = {2018}
}

@mastersthesis{uuid:82cfe98f-46ad-42b4-8021-082fbae1f740,
	abstract  = {A lot of research is been being done on Visible Light Communication (VLC), which has shown to be of interest for many applications, such as localization. Since localization based on VLC requires active modulation of light sources, this limits the amount of light sources that can be used for localization. Furthermore, in some situations there might not even be a controllable light source present (for example outdoors). To extend the use of light-based localization schemes, this thesis looks into a way to achieve the same result as current VLC localization methods in a passive manner, i.e. without control of the light sources. <br/><br/>Previous work has been done on passive ambient light-based localization by Wang et al.: objects are equipped with unique barcodes, that reflect ambient light in a distinct manner. The reflected light is received by photosensors, from which their ID is obtained. However, this work has focused on identifying large-sized objects in one dimension. Using the same principle for localization of small-sized objects, and in two dimensions, are open challenges that this thesis addresses . <br/><br/>The work presented here forms a proof-of-concept of a passive light-based localization system for two-dimensional, real-time tracking of small-sized objects. In order to achieve this, a special enclosure has been designed, giving simple photosensors the ability to distinguish small-sized objects without compromising their FOV. With this enclosure, a single photosensor can detect barcodes down to 7 cm in size in the test set-up, while distinguishing up to three different IDs. A particle filter has been implemented to combine detections from different photosensors into a single estimate of an object’s location. <br/><br/>The localization system is designed around the robots designed by a MSc student at the Embedded Systems group at TU Delft. By moving these robots at a speed of 15.4 cm/s in a straight line through the test set-up, a localization error of 4.8 cm is obtained. The distance between the robots and the sensor equals 20 cm.},
	author    = {van der Werff, Danielle },
	keywords  = {Localization; Ambient light; Passive light communication},
	note      = {Zuñiga Zamalloa, Marco (mentor); Pawelczak, Przemek (mentor); de Croon, Guido (graduation committee); Langendoen, Koen (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Electrical Engineering, Mathematics and Computer Science},
	title     = {Passive Localization of Robots with Ambient Light},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:82cfe98f-46ad-42b4-8021-082fbae1f740},
	year      = {2018}
}

@mastersthesis{uuid:d3bd3ba9-61aa-4d48-b13d-aead118b8015,
	abstract  = {This work presents a vector field based path following method to be used by Multirotor Unmanned Aerial Vehicles (UAVs). The desired path to be followed is a smooth planar path defined in its implicit form. The vector field around the desired path is then constructed using the implicit function, such that the integral curves of the vector field converge to the path. The algorithm takes into account the future change in the trajectory as well as the current state of the UAV in order to calculate the desired linear acceleration, which is then tracked using the Incremental Nonlinear Dynamic Inversion (INDI) controller in the autopilot. The implementation also allows for the velocity of the UAV to be controlled independently. The efficiency of the algorithm is demonstrated using real world flight tests, and the performance is shown to be better than the<br/>traditional carrot-chasing controller.},
	author    = {Sharma, Suresh },
	keywords  = {Vector Field; Incremental Nonlinear Dynamic Inversion; Path Following; Multirotor},
	note      = {Smeur, E.J.J. (mentor); Chu, Q. P. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Operations},
	title     = {Vector Field Based Path Following for UAVs using Incremental Nonlinear Dynamic Inversion},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:d3bd3ba9-61aa-4d48-b13d-aead118b8015},
	year      = {2018}
}

@mastersthesis{uuid:3794f912-f141-4fa7-aff4-464598958e94,
	abstract  = {High-speed autonomous flight of Micro Air Vehicles has gained much attention in recent years. However, flight in complex GPS-denied environments still poses a serious challenge. One scenario which contains these elements is drone racing, where pilots have to fly complex tracks at high speed, often in an indoor environment. In this work we therefore present an MAV capable of autonomously flying such a drone race track. The system has to operate in a GPS-denied environment, hence a visual navigation method is employed. Position is recovered from gate detections based on a novel least-squares method, while heading is estimated using an optimization based method. Experiments show that both methods have a higher accuracy than the standard P3P pose estimation method. Furthermore, a state estimation filter is designed to fuse the visual measurements with IMU measurements, by using an EKF with drag based prediction model. For high-level control different motion primitives are linked, which allow the MAV to fly the track without having a detailed on-board map. The overall approach does not rely on SLAM or Visual odometry, which results in low computational complexity. Also, it does not rely on downward optical flow velocity measurements, which enables it to work even in low texture environments.},
	author    = {Ozo, Michaël },
	keywords  = {High-speed; Autonomous flying; MAV; UAV; drone; Visual navigation; EKF},
	note      = {de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Vision-based Autonomous Drone racing in GPS-denied Environments},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:3794f912-f141-4fa7-aff4-464598958e94},
	year      = {2018}
}

@mastersthesis{uuid:717e7e15-94c3-47ac-a348-12f5c2275aa2,
	abstract  = {Tailless flapping wing micro air vehicles (FWMAVs) have<br/>the potential of providing efficient flight at small scale,<br/>with considerable agility. However, this agility also brings<br/>significant control challenges, which are exacerbated by<br/>the fact that the aerodynamics and dynamics of flapping<br/>wing robots are still only partly understood.<br/>In this article, we propose a novel, minimal dynamic<br/>model that is not only validated with experimental data,<br/>but also able to predict the consequences of various important<br/>design changes. Specifically, the model captures<br/>the flapping cycle averaged longitudinal dynamics of a<br/>tailless flapping wing robot, taking into account the main<br/>aerodynamic effects. The model is validated for airspeeds<br/>up to 3.5 m/s (when the forward velocity starts to approximate<br/>the wing velocities). It successfully predicts the effects<br/>of changes to the center of mass and flight at different<br/>pitch angles. Hence, the presented model forms an<br/>important step in accelerating the control design of flapping<br/>wing robots - which can now be done to a greater<br/>extent in simulation. In order to illustrate this, we have<br/>used the model to improve our control design, resulting in<br/>a change of the maximal stable speed of the tailless DelFly<br/>Transformer from 4 m/s to 7 m/s.},
	author    = {Kajak, Karl },
	keywords  = {},
	note      = {Karasek, M. (mentor); Chu, Q. P. (mentor); de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Operations},
	title     = {A minimal longitudinal dynamic model of a tailless flapping wing robot},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:717e7e15-94c3-47ac-a348-12f5c2275aa2},
	year      = {2018}
}

@mastersthesis{uuid:28774acc-89e7-44a8-be43-b06d2db0056c,
	abstract  = {Micro Aerial Vehicles (MAVs) are increasingly being used for aerial transportation in remote and urban spaces where portability can be exploited to reach previously inaccessible and inhospitable spaces. Current approaches to MAV swung payload system path planning have primarily focused on pre-generating (agile) collision-free, or conservative minimal-swing trajectories in static environments. However, these approaches have failed to address the prospect of online re-planning in uncertain and dynamic environments which is a prerequisite for real-world deployability. This article describes a novel Non-Linear Model Predictive Controller (NMPC) for online, agile and closed-loop local trajectory planning and control addressing the limitations mentioned of contemporary approaches. We integrate the controller in a full system framework and demonstrate the algorithm’s effectiveness in simulation and experimental studies. Results show the scalability and adaptability of our method to various dynamic setups with repeatable performance over several complex tasks which include flying through a narrow opening and avoiding moving humans.},
	author    = {Potdar, Nikhil },
	keywords  = {Autonomous Vehicles; Micro Aerial Vehicle; Model Predictive Control; Optimal control; Collision Avoidance; Dynamic Environment; MAV},
	note      = {de Croon, G.C.H.E. (mentor); Alonso Mora, J. (mentor); de Visser, C.C. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Online Trajectory Planning and Control of a MAV Payload System in Dynamic Environments: A Non-Linear Model Predictive Control Approach},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:28774acc-89e7-44a8-be43-b06d2db0056c},
	year      = {2018}
}

@mastersthesis{uuid:6a3c6f9c-1634-4575-844c-510092f73dc6,
	abstract  = {In this paper a range-based relative localization solution is proposed and demonstrated in practice. The approach is based on wireless range measurements between robots, along with the communication of their velocities, accelerations, yaw rates, and height. It can be implemented on many robotic platforms without the need for dedicated sensors. With respect to previous work, we remove the dependency on a common heading reference between robots. The main advantage of this is that it makes the relative localization approach independent of magnetometer readings, which are notoriously unreliable in an indoor environment. A theoretical observability analysis shows that it may also have two disadvantages: the motion of the robots must meet more stringent conditions and the relative localization method becomes more susceptible to noise on the range measurements. However, simulation results have shown that in the presence of significant magnetic disturbances that are common to indoor environments, removing the heading dependency is beneficial. We conclude the paper by implementing the heading-independent method on real Micro Aerial Vehicles (MAVs) and performing leader-follower flight in an indoor environment. Despite the observability analysis showing leader-follower flight to be an especially difficult task, we still manage to successfully fly for over 3 minutes with two fully autonomous followers using only on-board sensing.},
	author    = {van der Helm, Steven },
	keywords  = {Relative Localization; Leader-Follower; Swarming; Ultra Wideband},
	note      = {de Croon, G.C.H.E. (mentor); McGuire, K.N. (graduation committee); Coppola, M. (graduation committee); Chu, Q. P. (graduation committee); Verhoeven, C.J.M. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
	title     = {On-board Range-based Relative Localization: For Leader-Follower Flight of Micro Aerial Vehicles},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:6a3c6f9c-1634-4575-844c-510092f73dc6},
	year      = {2018}
}

@mastersthesis{uuid:07bc64ba-42e3-4aa7-ba9b-ac0ac4e0e7a1,
	abstract  = {Safe navigation in a cluttered environment is a key capability for the autonomous operation of Micro Aerial Vehicles (MAVs). This work explores a (deep) Reinforcement Learning (RL) based approach for monocular vision based obstacle avoidance and goal directed navigation for MAVs in cluttered environments. We investigated this problem in the context of forest flight under the tree canopy.<br/><br/>Our focus was on training an effective and practical neural control module, that is easy to integrate into conventional control hierarchies and can extend the capabilities of existing autopilot software stacks. This module has the potential to greatly improve the autonomous capabilities of MAVs, and their applicability for many interesting real world use-cases. We demonstrated training this module in a visually highly realistic virtual forest environment, created with a state-of-the-art computer game engine.},
	author    = {Kisantal, Máté },
	keywords  = {reinforcement learning; deep reinforcement learning; artificial intelligence; machine learning; computer vision; MAV; UAV; MAVLAB; drone; autonomous navigation; Autonomous Vehicles; deep learning; neural networks},
	note      = {de Croon, G.C.H.E. (mentor); van Hecke, K.G. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Operations},
	title     = {Deep Reinforcement Learning for Goal-directed Visual Navigation},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:07bc64ba-42e3-4aa7-ba9b-ac0ac4e0e7a1},
	year      = {2018}
}

@mastersthesis{uuid:ed1025e8-66ad-4e71-ba8e-771f511e9022,
	abstract  = {Space exploration could be significantly aided by combining the disciplines of machine learning and computer vision, but these disciplines need to be developed further for specific space-related applications to have merit. One of the applications for space exploration is the detection of certain structures designating areas of interest. This thesis demonstrates a method of structure-detecting that is applied to staircases. In addition to incorporating certain physical features, like other algorithms have done, the proposed algorithm (Step-1) also takes into account the spatial relation between these features, in order to increase its robustness. Looking at a staircase from the front, the distances between each step become warped, as they are further away from the observer. This exponential spatial distortion is known as a ’chirp’. Step-1 tries to match a chirp-waveform to every edge along a straight line randomly drawn through an image, and based on that match classify the image as containing a staircase or not. The random lines are then weighted based on their effectiveness using Adaboost, which are finally combined to obtain a final classification. The results show potential but there are still some issues to be addressed. However, once the algorithm has been upgraded it could aid space exploration by being applied to satellite images and autonomous rovers.},
	author    = {Andriessen, Frerik },
	keywords  = {Machine Learning; Adaboosting; Computer Vision; Depth Camera},
	note      = {Sundaramoorthy, P.P. (mentor); Cervone, A. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Ready for detection: Stair-detecting in depth images using spatial features and Adaboosting},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:ed1025e8-66ad-4e71-ba8e-771f511e9022},
	year      = {2018}
}

@mastersthesis{uuid:4a03785d-8822-4548-a24a-7b23b1a4232c,
	abstract  = {Teleoperation allows the use of human intelligence and decision making in remote tasks which are too dangerous for humans to perform. Technologies such as force feedback and haptic guidance have shown to increase task efficiency during teleoperation. In an unmodeled environment, sensors provide input for haptic guidance or present extra information about the environment to the user in order to make decisions and to perform the tasks. These sensors come with inherent errors and uncertainties which propagate through the teleoperation system. The absence of knowledge of these errors has been shown to cause deterioration in the task performance. These errors can further cause the application of forces on the environment by the robot without the knowledge of the user while using haptic guidance. Thereby, the strategies being used to increase task performance can have some adverse hidden effects. Thus, it crucial to have an understanding of the behaviour of the errors and uncertainties in the system. It is considered critical for making decisions about how the robot system can be controlled and used to manipulate objects remotely.<br/><br/>In this thesis, a novel framework for estimating the uncertainties in a vision-aided teleoperation system in real-time is introduced. The uncertainty estimate can then be used by the control system or communicated to the user. Methods to use the uncertainty estimate for haptic guidance and for user display are proposed. Furthermore, the thesis analyzes the behaviour of the uncertainties in the system and the sensitivity of the system to individual component errors. It evaluates the uncertainties in individual components of the system and implements an uncertainty model for each of them. It then provides a method to propagate these uncertainty models through the system. This results in a final uncertainty estimate in the frame of reference of interest for the task. Experiments were performed to validate the component uncertainty models, the propagation method, and the system as a whole. Additionally, an inverse of the propagation method is also conceived so as to obtain the component accuracy specification from system uncertainty requirements. This can be used in the design of future teleoperation systems.},
	author    = {Vyas, Shubham },
	keywords  = {space robotics; Teleoperation; uncertainty; estimation; computer vision; Robotics; 3d vision; stereo camera; motion capture system; Framework},
	note      = {Verhoeven, C.J.M. (mentor); Krueger, Thomas (mentor); Schiele, A. (mentor); Gill, E.K.A. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Space Engineering},
	title     = {Uncertainty Estimation in Vision-Aided Robot Teleoperation System},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:4a03785d-8822-4548-a24a-7b23b1a4232c},
	year      = {2018}
}

@mastersthesis{uuid:ff167b06-bbaf-4897-b76c-9f246e50eadb,
	abstract  = {Robotic behavior policies learned in simulation suffer from a performance degradation once transferred to a real-world robotic platform. This performance degradation originates from discrepancies between the real-world and simulation environment, referred to as the reality gap. To cross the reality gap, this papers presents a simple reinforcement learning algorithm named Directed Increment Policy Search (DIPS). DIPS is a form of episodic model-free policy search which leverages the interpretable structure and the coupling of the Behavior Tree (BT) parameters to reduce the number of required real-world evaluations. Additionally, DIPS does not require a form of reward function crafting and is robust to hyper-parameter settings. DIPS is evaluated on a simulated model of the DelFly Explorer which is tasked to perform a window fly-through maneuver. It is demonstrated that DIPS efficiently and effectively improves the BT behavior policy performance for three simulated environments with increasingly large reality gaps. We believe DIPS can generalize to other behavior representation methods and tasks due to the inherent coupling between behavior and environment experienced by embodied robots.},
	author    = {Leest, Steven },
	keywords  = {Reinforcement Learning; Policy Search; Behavior Tree; Behavior; Robotics},
	note      = {van Kampen, E. (mentor); de Croon, G.C.H.E. (mentor); Scheper, K.Y.W. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Directed Increment Policy Search for Behavior Tree Task Performance Optimization: Crossing the Reality Gap},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:ff167b06-bbaf-4897-b76c-9f246e50eadb},
	year      = {2017}
}

@mastersthesis{uuid:9494dbb1-9da0-44de-b401-02e297a3689c,
	abstract  = {Future space exploration missions on solar system bodies will require landing safely and precisely, with an accuracy of ~100 m at touchdown. This accomplishment is made challenging by vehicle design limitations, the dearth of onboard situational awareness, and the limited knowledge of the variability of the landing terrain. To date, only the Chinese Chang’e-3 has implemented hazard detection and avoidance capabilities, within its Guidance, Navigation, and Control (GN&amp;C) subsystem, therefore being able to actively adjust its trajectory. On the contrary, the majority of the space landers only had the ability to execute autonomously a small series of simple and programmed commands. Therefore, past missions have essentially landed "blind" in regions deemed relatively safe, forcing landing site selection to be capability-limited rather than scientifically driven. In this thesis, hazard detection was investigated as a mean to increase autonomy for planetary landings and to further decrease the risk of a landing failure, employing equipment readily available on space missions. The analysis has been limited to the framework of Structure-from-Motion (SfM) where the input images are acquired from a single moving camera and thus the scene is reconstructed from the resulting video sequence. A software package was developed and tested to compute depth maps from adjacent descent images, captured at half altitude from one to the other. The basic pinhole camera model was selected to address the measurement taken from synthetic surface images, rendered in the Planet and Asteroid Natural scene Generation Utility (PANGU). To assess the hazardousness of the terrain, hazard maps are computed combining slope, roughness, and shadow information. In contrast to the results of the Jet Propulsion Laboratory (JPL) NASA, it has been shown that rocks and boulders are not well resolved from shape recovery with both low- and high-elevation image pairs. Thus, their presence on the surface has been accounted through an adapted version of theHarris Corner detector directly on the input images. Two different mission scenarios were simulated: 1) a perfect vertical motion forward along the camera pointing direction and 2) a 45° angle dropping trajectory for a more realistic approaching descent phase, with a 40° imaging sensor line-of-site offset. Furthermore, the limitations of the developed algorithm were tested under ordinary operative conditions. For the former scenario, the results show that the overall quality of the recovered depth maps does not appear adequate enough for landing site selection. As a matter of fact, the locations around the image centre can not be correctly assessed. This represents a significant problem since these locations are the most convenient in terms of distance and guidance costs. On the contrary, the latter descent sequence indicates that below 300maltitude the software is a suitable candidate for hazard detection, with total correct detection on average &gt;94% and the percentage of undetected hazards below the allowable maximum 1%. To assess the algorithm robustness to errors in camera position, a Monte Carlo simulation was performed. Thereupon, random uncertainties within the interval [-0.5 0.5] meters were taken into account for the altitude of both camera poses. The errors for the computed Digital Elevation Model (DEM) are bounded to the maximum allowable only when both altitudes are affected by small deviation of similar magnitude and same sign (approximately 10 cm), peaking to 250%-300% increase for the other values of the considered interval. Moreover, concerning the robustness to errors in camera orientation, deviations of the camera pointing direction were considered only along the plane containing both the normal to the surface and the camera axis. Already differences greater than +0.05°, in the imaging sensor line-of-site, are responsible for exorbitant errors in the DEM for all altitudes. These results clearly indicate that the developed SfMalgorithmis not suitable as a stand-alone method for hazard detection and landing site selection.},
	author    = {Vanzella, Alessandro },
	keywords  = {Planetary Landings; Hazard Detection; Computer Vision; Structure-from-Motion; Passive sensors; PANGU; Digital Elevation Model},
	note      = {Woicke, S. (mentor); Mooij, E. (mentor); de Croon, G.C.H.E. (graduation committee); Visser, P.N.A.M. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Structure-from-Motion Hazard Detection for Autonomous Planetary Landings},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:9494dbb1-9da0-44de-b401-02e297a3689c},
	year      = {2017}
}

@mastersthesis{uuid:faf5d4fb-5785-4d27-9d52-0b09214f3a6a,
	abstract  = {We study how autonomous robots can better evaluate distances by fusing depth estimates from both stereo vision and a convolutional neural network (CNN) that processes a single still image. The main contribution is a novel fusion method that preserves high confidence stereo estimates, while leveraging the CNN estimates in the low-confidence regions. The main concern with such a fusion scheme is that the CNN may work on the training set, but will degrade significantly in the operational environment. Therefore, we also show that the performance of the monocular estimator in the operational environment improves if stereo vision provides supervised targets in a self-supervised learning (SSL) fashion. The merging framework is implemented on-board of a Parrot SLAMDunk<br/>and tested in real world scenarios, providing more reliable depth maps for use in autonomous navigation.},
	author    = {Tomás Cardoso Rézio Martins, Diogo },
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Fusion of stereo and monocular depth estimates in a self-supervised learning context},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:faf5d4fb-5785-4d27-9d52-0b09214f3a6a},
	year      = {2017}
}

@mastersthesis{uuid:82c91d74-6c01-4718-a574-221df210f01a,
	abstract  = {This thesis presents a visual route following method that minimizes memory consumption to the point that even Micro Aerial Vehicles (MAV) equipped with only a simple microcontroller can traverse distances of a few hundred meters. Existing Simultaneous Localization and Mapping (SLAM) algorithms are too complex for use on a microcontroller. Instead, the route is modeled by a sequence of snapshots that can be followed back using a combination of visual homing and odometry. Three visual homing methods are evaluated to find and compare their memory efficiency. Of these methods, Fourier-based homing performed best: it still succeeds when snapshots are compressed to less than twenty bytes. Visual homing only works from a small region surrounding the snapshot, therefore odometry is used to travel longer distances between snapshots. The proposed route following technique is tested in simulation and on a Parrot AR.Drone 2.0. The drone can successfully follow long routes with a map that consumes only 17.5 bytes per meter.<br},
	author    = {van Dijk, Tom },
	keywords  = {navigation; route following; visual homing; odometry; indoor; micro aerial vehicle; MAV; unmanned aerial vehicle; UAV; quadrotor; vision; ARDrone; MAVLAB},
	note      = {McGuire, K.N. (mentor); de Croon, G.C.H.E. (mentor); Campoy Cervera, P. (mentor); Jonker, P.P. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering},
	title     = {Low-memory Visual Route Following for Micro Aerial Vehicles in Indoor Environments},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:82c91d74-6c01-4718-a574-221df210f01a},
	year      = {2017}
}

@mastersthesis{uuid:6e3ce742-a974-491d-97c2-1cafc090b3d9,
	abstract  = {On-board stabilization of quadrotors is often done using an Inertial Measurement Unit (IMU), aided by additional sensors to combat the IMU drift. For example, GPS readings can aid when flying outdoors, or when flying in GPS denied environments, such as indoors, visual information from one or more camera modules can be used. <br/>A single downwards facing camera however cannot determine the absolute height of the quadrotor, leaving the results from the Optical Flow (OF) up to scale. To estimate the velocity of the quadrotor an additional range sensor, such as an Ultrasonic Sensor (US), is used to solve this scaling problem.<br/>These solutions are difficult to scale down to micro quadrotors as the platform becomes too small to fit and lift additional sensors. Therefore stabilizing a quadrotor with a single camera and IMU only would pave the way for the development of even smaller quadrotors. This master thesis presents an adaptive control strategy to stabilize a micro quadrotor in all<br/>three axes using only an IMU and a monocular camera. This is achieved by extending the stability based approach for a single, vertical, axis by De Croon in Distance estimation with efference copies and optical flow maneuvers: a stability-based strategy[1]. This stability based method ncreases the control gain in the visual feedback loop until the quadrotor detects it is oscillating by detecting that the covariance of the given thrust inputs and the measured divergence passes a threshold. Next the height can be estimated using the predetermined relationship between gain and height at which these self-induced oscillations occur and proper gains can be set for the estimated height. <br/>An analysis is done in simulation to present proof of concept of the stabilization method in three axis and to determine the effects of scaling and the effects of varying effective Frames per Second (FPS) caused by computations. It was shown that the adaptive gain strategy can stabilize the simulated quadrotor and prevent it from drifting. Furthermore, the control gains were scaled such that the effects of scaling a quadrotor could be mostly negated, though at about a tenth of the scale the simulated noise had such an influence that the scaled gains could not negate it anymore. Furthermore, the minimum effective FPS required to stabilize an ARDrone 2 was determined to be 15 FPS, and it was shown that an increase in effective FPS aids stabilizing the smaller scale quadrotors that became unstable due to the scaling effects.<br/>Furthermore, flights on an Parrot ARDrone 2 and Parrot Bebop are performed to show the usability of this control strategy in real life. It was shown that both quadrotors could achieve stable hover without drifting at multiple heights, using various strategies.<br},
	author    = {Braber, T.I. },
	keywords  = {Quadrotor; Adaptive control; vision; Monocular; ARDrone; Bebop; stabilization; MAV; MAVLAB; micro quadrotor; quadcopter},
	note      = {Babuska, Robert (mentor); de Croon, Guido (mentor); de Wagter, Christophe (mentor); de Bruin, Tim (graduation committee); Bregman, Sander (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control},
	title     = {Vision-based stabilization of micro quadrotors},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:6e3ce742-a974-491d-97c2-1cafc090b3d9},
	year      = {2017}
}

@mastersthesis{uuid:d00ade0b-7350-4d28-baf7-55d56a185032,
	abstract  = {Real-time optimization of Vehicle Routing Problems during mission operations raises concerns regarding reliability of obtaining a solution and solution time. Improvements in control performance by having a human-in-the-loop might be possible by leveraging human visual pattern recognition qualities. By developing an ecological interface, supporting the operator in controlling multiple Unmanned Aerial Vehicles in a simulated payload delivery mission, and by conducting a human-in-the-loop experiment, interface effectiveness and human control performance in Dynamic Vehicle Routing Problems was investigated. Results show the ecological interface offers good support and scales well with problem size. Results also show participants can in some cases achieve solutions faster and more reliably compared to an optimization algorithm, although generally yielding less efficient solutions. Having a human-in-the-loop can thus offer improved control performance over relying on pure automation, especially in time critical situations.},
	author    = {Klein Koerkamp, Niek },
	keywords  = {Vehicle Routing Problem; Ecological Interface Design; Human Control Performance; Unmanned Aerial Vehicle; Perturbation Management},
	note      = {Borst, C. (mentor); de Croon, G.C.H.E. (graduation committee); van Paassen, M.M. (graduation committee); Mulder, Max (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
	title     = {Human Control Performance in Solving Multi-UAV Dynamic Vehicle Routing Problems Using an Ecological Interface},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:d00ade0b-7350-4d28-baf7-55d56a185032},
	year      = {2017}
}

@mastersthesis{uuid:015f322f-9d86-4717-b2e2-74cf25bfa70c,
	abstract  = {We propose a novel method to deal with the scale ambiguity in monocular SLAM based on control stability. We analytically show that (1) using unscaled state feedback from monocular SLAM for control can lead to system instability, and (2) there is a unique linear relationship between the absolute scale of the SLAM system and the control gain at which instability arises. Using this property, our method estimates the scale by adapting the gain and detecting self-induced oscillations. Unlike conventional monocular approaches, no additional metric sensors are used for scale estimation. We demonstrate the ability of our system to estimate the scale for performing autonomous indoor navigation with a low-cost quadrotor MAV.},
	author    = {Lee, Seong Hun },
	keywords  = {monocular SLAM; autonomous navigation; scale estimation; quadrotor; MAV; visual SLAM},
	note      = {de Croon, G.C.H.E. (mentor); Hoekstra, J.M. (graduation committee); Kooij, J.F.P. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Stability-based Scale Estimation of Monocular SLAM for Autonomous Quadrotor Navigation},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:015f322f-9d86-4717-b2e2-74cf25bfa70c},
	year      = {2017}
}

@mastersthesis{uuid:0333e720-8105-4429-abcd-430c0d04f031,
	abstract  = {Airbus Friedrichshafen is working on the development of a milliNewton HEMPT (High Efficiency<br/>Multistage Plasma Thruster): an electrostatic thruster concept suitable for small satellite<br/>propulsion. An engineering model, the mN-μHEMPT, has been built and tested in vacuum,<br/>generating thrust levels in the range of 1 to 5 mN. Although the working principle is understood,<br/>there is still uncertainty in the loss process, in particular the heat transfer in the plasma-wall<br/>interaction. An efficient heat management is crucial for the operation of the thruster, as the<br/>performance of the magnets is severely hindered after reaching 250ºC. With this in mind, the<br/>present thesis aims to produce the first thermal model of the mN-μHEMPT, with which a detailed<br/>thermal analysis can be carried out. The model validation strategy, based on correlation<br/>to testing results, makes it possible to overcome the uncertainty regarding the thermal losses.<br/>By simulating the operation of the thruster in extreme load cases in a Low Earth Orbit, its<br/>thermal performance is assessed, resulting in a detailed understanding of the temperature<br/>evolution and heat propagation through the different components. This information is then<br/>used to improve the performance by implementing design modifications. The result of the<br/>thesis is a thermal model validated to within 1.65ºC as mean deviation, predicting a maximum<br/>temperature of 180ºC at the magnet stack during operation. The application of a boron nitride<br/>coating to the radiator and the decoupling of the heat losses at the magnet stack and at the<br/>anode thanks to a second radiator, results in a maximum temperature of the magnet stack<br/>of 85ºC. In conclusion, the thermal performance of the mN-μHEMPT is analysed for the first<br/>time, and the design modifications proposed become a successful improvement.},
	author    = {Granero Moneva, Nacho },
	keywords  = {HEMPT; electric propulsion; thruster; space; thermal; modelling; thermal analysis; Systema},
	note      = {Cervone, A. (mentor); Hey, Franz Georg (mentor); Zandbergen, B.T.C. (graduation committee); de Croon, G.C.H.E. (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Space Engineering},
	title     = {Thermal Modelling and Thermal Control Optimisation of the mN-μHEMPT},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:0333e720-8105-4429-abcd-430c0d04f031},
	year      = {2017}
}

@mastersthesis{uuid:de84d7b7-52f7-4c0f-a00f-d7a6c244a678,
	abstract  = {The reconstruction of dense depth maps is of great value to resource-constrained Mirco Air Vehicles (MAVs), in the pursuit of achieving autonomous flight with a high situational awareness. Most MAVs implement sensing methods which provide a sparse depth map, limiting their capabilities significantly. This article introduces two novel methods to enhance existing depth reconstruction algorithms in terms of geometric reconstruction, depth approximation and computational time. The first contribution is the introduction of a novel method that includes edge information from the image-domain into the depth-regularization problem. This to enhance the retrieval of the complete scene geometry. The second contribution is a novel scheme which includes temporal information in the reconstruction approach, allowing extremely sparse depth scenes to be reconstructed. By estimating the geometric transformation with optical flow, previous depth reconstructions can be used as initial solutions for the current depth-regularization problem. Empirical results show a consistent reduction reconstruction error, while at the same time reducing the computational time. Qualitative estimation shows significant improvement in the retrieval of scene geometry.},
	author    = {Heil, Tobias },
	keywords  = {Depth reconstruction; UAV; Computer vision},
	note      = {de Croon, G.C.H.E. (mentor); Gao, Zhi (graduation committee); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Operations},
	title     = {Enhanced Sparse Depth Reconstruction Using Edge and Temporal Information: An Application to Micro Air Vehicles},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:de84d7b7-52f7-4c0f-a00f-d7a6c244a678},
	year      = {2017}
}

@mastersthesis{uuid:ec5df737-cad0-4b24-89a0-75d45ebac51b,
	abstract  = {In the near future many tasks could be performed by swarms of flying robots. To successfully implement multiple of these swarms in the same airspace they will have to be decentralised, autonomously cope with high densities and even resolve conflicting objectives of other swarms, while remaining controllable by operators through high-level objectives. This article introduces a novel swarming approach dubbed "Velocity Templates" based on artificial potential fields. These global fields represent the objectives of the swarm, which are balanced with local interaction. Different fields are considered leading to still or sustained motion swarms where conflicting objectives between sub-groups or multiple swarms are gracefully resolved. The approach is implemented on groups of 2 and 4 Parrot Bebop UAVs, using an efficient on-board vision algorithm to locate neighbours and a motion tracking system for guidance. The experiments show promising results for further outdoor tests assessing the scalability of the proposed approach.},
	author    = {Vlenterie, Wilco },
	keywords  = {Unmanned Aerial Vehicles; Autonomous Swarming; Computer Vision; Distributed systems},
	note      = {Chu, Q. P. (mentor); de Croon, G.C.H.E. (mentor); Remes, B.D.W. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Operations},
	title     = {Velocity Templates for Dense Swarms of Flying Robots},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:ec5df737-cad0-4b24-89a0-75d45ebac51b},
	year      = {2017}
}

@mastersthesis{uuid:edbb8630-d1ad-4230-b4cd-f593e81622b2,
	abstract  = {State of the art trajectory generation schemes for quadrotors assume a simple dynamic model. They neglect aerodynamic effects such as induced drag and blade flapping and assume that no wind is present. In order to overcome this limitation, this thesis investigates a trajectory optimization scheme based upon Differential Dynamic Programming (DDP). There are various software-implementations of the DDP scheme. For future deployment on robotic hardware the software is required to be computationally efficient, written in C++ and to be open-source. A library named GCOP, which was developed at the John Hopkins University, fulfills these requirements and is used. Before implementing the solver, a full model of the Crazyflie Nano Quadcopter is identified experimentally. The solver is validated, normalized and the performance is benchmarked. This method yields reliable minimum control-effort trajectories. A control scheme is proposed and studied in Monte-Carlo simulations. Itis robust and able to handle large modelling errors in mass and moment of inertia while ensuring minimal error on the final state.},
	author    = {Abuter Grebe, Nicolás Omar },
	keywords  = {Differential Dynamic Programming; quadcopter; quadrotor; Controller; Modeling},
	note      = {de Wagter, C. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering; TU Delft Control & Simulation},
	title     = {Differential Dynamic Programming for Aerial Robots using an Aerodynamics Model},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:edbb8630-d1ad-4230-b4cd-f593e81622b2},
	year      = {2017}
}

@mastersthesis{uuid:18dee61c-9828-430a-9d71-5a12586da89c,
	abstract  = {The effects of horizontal tail geometry and position on longitudinal flapping-wing micro aerial vehicle dynamics were studied using wind tunnel and free-flight experiments. Linearised models were used to analyse the effect on the dynamic properties of the ornithopter. Results show higher steady-state velocity and increased pitch damping for increased tail surface area and aspect ratio. The maximum span width of the tail surface is also found to play an important role in determining dynamic behaviour, in particular when the distance between the tail surface and the flapping wings is large. Steady-state conditions can be predicted accurately using linear functions of tail geometry for this ornithopter. Predicting dynamic behaviour is more complicated and requires further study. However, the observed trends in some of the model parameters suggest that future models explicitly including the tail geometry may be used to design flapping-wing robots with desirable dynamic properties.},
	author    = {Rijks, F.G.J.},
	keywords  = {wing-tail interaction; Flapping-wing MAV; system identification; tail effects},
	note      = {De Visser, C.C. (mentor); Karásek, M. (mentor); Armanini, S.F. (mentor)},
	school    = {Delft University of Technology},
	title     = {Studying the effect of the tail on the dynamics of a flapping-wing MAV},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:18dee61c-9828-430a-9d71-5a12586da89c},
	year      = {2017}
}

@mastersthesis{uuid:b76bd35d-9d56-472e-8ff8-35fd453b6a49,
	abstract  = {Maintaining stable flight during high turbulence intensities is challenging for fixed-wing micro air vehicles. Two methods have been identified to improve the disturbance rejection performance of the MAV: incremental nonlinear dynamic inversion and phase-advanced pitch probes. Incremental nonlinear dynamic inversion uses the angular acceleration measurements to counteract disturbances. Multihole pressure probes measure the incoming flow angle and velocity ahead of the wing in order to react to gusts before an inertial response has occurred. The performance of incremental nonlinear dynamic inversion is compared to a traditional proportional integral derivative controller with and without the multihole pressure probes. The attitude controllers are tested by performing autonomous wind tunnel flights and stability augmented outdoor flights. This thesis shows that nonlinear dynamic inversion improves the disturbance rejection performance of fixed-wing MAVs compared to traditional proportional integral derivative controllers.},
	author    = {Van der Sman, E.S.},
	keywords  = {},
	note      = {Chu, Q.P. (mentor); Remes, B. (mentor); Smeur, E.J.J. (mentor)},
	school    = {Delft University of Technology},
	title     = {Incremental Nonlinear Dynamic Inversion and Multihole Pressure Probes for Disturbance Rejection Control of Fixed-Wing Micro Air Vehicles},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:b76bd35d-9d56-472e-8ff8-35fd453b6a49},
	year      = {2016}
}

@mastersthesis{uuid:ffa1ec41-3930-4dfe-b454-e11c3517a7f4,
	abstract  = {Small flying robots can perform landing maneuvers using bio-inspired optical flow by maintaining a constant divergence. However, optical flow is typically estimated from frame sequences recorded by standard miniature cameras. This requires processing full images on-board, limiting the update rate of divergence measurements, thus the speed of the control loop and the robot. Event-based cameras overcome these limitations by only measuring pixel-level brightness changes at microsecond temporal accuracy, hence providing an efficient mechanism for optical flow estimation. This thesis presents, to the best of our knowledge, the first research integrating event-based optical flow estimation into the control loop of a flying robot. We extend an existing 'local plane fitting' algorithm to obtain an improved and more computationally efficient optical flow estimation method, valid for a wide range of optical flow velocities. This method is validated for real event sequences. In addition, a method for estimating the divergence from event-based optical flow is introduced, which accounts for the aperture problem. The developed algorithms are implemented in a constant divergence landing controller on-board of a quadrotor. Flight tests demonstrate that, using event-based optical flow, accurate divergence estimates can be obtained over a wide range of speeds. This enables the quadrotor to perform very fast landing maneuvers.},
	author    = {Pijnacker Hordijk, B.J.},
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor)},
	school    = {Delft University of Technology},
	title     = {Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:ffa1ec41-3930-4dfe-b454-e11c3517a7f4},
	year      = {2016}
}

@mastersthesis{uuid:424ead9b-50be-4e80-94a9-d041a1418dd3,
	abstract  = {},
	author    = {Kuijpers, M.W.M.},
	keywords  = {},
	note      = {de Wagter, C. (mentor); de Croon, G.C.H.E. (mentor)},
	school    = {Delft University of Technology},
	title     = {The influence of a bottom camera in indoor ground-segmentation based obstacle avoiding performance for MAVs},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:424ead9b-50be-4e80-94a9-d041a1418dd3},
	year      = {2016}
}

@mastersthesis{uuid:3704b044-b9bf-454a-8678-0d140bd1d308,
	abstract  = {Incremental Nonlinear Dynamic Inversion provides a high performance attitude controller for multi-rotor Micro Aerial Vehicles by providing very good disturbance rejection capabilities. Flights conducted with a quadcopter revealed undesired pitch and rolling motions which occurred simultaneously with actuator saturation for instantaneous yaw angle reference tracking commands. Constrained control allocation methods can increase the system's performance by providing an effective strategy to prioritize control objectives, and redistribute control effort accordingly. Weighted Least Squares control allocation makes the constrained control allocation problem a quadratic optimization problem. An iterative solver based on the computationally efficient active-set algorithm finds the optimal control distribution for a weighted control objective. In this paper the Weighted Least Squares control allocator is used to overcome two challenges 1) increase performance by applying prioritization between control objectives and redistribute control effort accordingly, accounting for the actuator limits 2) enable flight when flying with severely compromised actuator(s). Real-world flight experiments are performed and show a significant increase in performance for high load yaw maneuvers, and enabled a quadcopter to perform controlled flight with a severely compromised actuator},
	author    = {Höppener, D.C.},
	keywords  = {quadcopter; constrained control allocation; saturation handling; aggressive maneuvering; quadratic programming; active set; WLS control allocator; SLS control allocator},
	note      = {de Wagter, C. (mentor)},
	school    = {Delft University of Technology},
	title     = {Actuator Saturation Handling using Weighted Optimal Control Allocation Applied to an INDI Controlled Quadcopter},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:3704b044-b9bf-454a-8678-0d140bd1d308},
	year      = {2016}
}

@mastersthesis{uuid:382dec56-7789-40df-af28-f2e61de99fad,
	abstract  = {},
	author    = {Janssen, R.M.J.},
	keywords  = {wing tension modulation; control; stabilisation; MAV; FWMAV; tail-less; DelFly},
	note      = {Karasek, M. (mentor)},
	school    = {Delft University of Technology},
	title     = {Attitude control- and stabilisation moment generation of the DelFly using Wing Tension Modulation},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:382dec56-7789-40df-af28-f2e61de99fad},
	year      = {2016}
}

@mastersthesis{uuid:f6008da9-d688-4b9f-9880-8d7c3b51a777,
	abstract  = {},
	author    = {Janssen, Y.S.},
	keywords  = {},
	note      = {Scheper, K.Y.W. (mentor)},
	school    = {Delft University of Technology},
	title     = {Reinforcement Learning Policy Approximation by Behavior Trees: using Genetic Algoritms},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:f6008da9-d688-4b9f-9880-8d7c3b51a777},
	year      = {2016}
}

@mastersthesis{uuid:58a4c285-e3b6-4bf0-b885-2908077e9b02,
	abstract  = {This paper presents a vision based strategy, designed to work fully onboard a small pocket drone, for autonomously tracking and following a person. Flying a drone is not an easy task, usually requiring a trained pilot, with the presented system it is possible to use a drone for filming or taking pictures from previously inaccessible places without the need for a person controlling the aircraft. Such framework is comprised by two main components, a tracker and a control system. The tracker has the function of estimating the position of the person that is being followed, while the control system gets the drone near that person. Limited by payload weight, power consumption and processing power the system results in a delicate balance between these constraints. The main contributions of this paper are the comparison between two state-of-the-art visual trackers running on paparazzi, Struck and KCF, as well as the control system that uses the tracker’s output location to perform the person following task. Then a new tracker is developed to be as computationally light as possible so that it can run onboard a small pocket drone, based on HOG feature extraction, it uses logistic regression to train a detector on the appearance of a person.},
	author    = {Duro, T.},
	keywords  = {},
	note      = {De Croon, G. (mentor); De Wagter, C (mentor); Meertens, R. (mentor)},
	school    = {Delft University of Technology},
	title     = {Tracking and Following a Moving Person Onboard a Small Pocket Drone},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:58a4c285-e3b6-4bf0-b885-2908077e9b02},
	year      = {2016}
}

@mastersthesis{uuid:4b4c4e4b-5e45-4166-bd2c-f35a1e495c6a,
	abstract  = {Stereo vision systems are often employed in robotics as a means for obstacle avoidance and navigation. These systems have inherent depth-sensing limitations, with significant problems in occluded and untextured regions, leading to sparse depth maps. We propose using a monocular depth estimation algorithm to tackle these problems, in a Self-Supervised Learning (SSL) framework. The algorithm learns online from the sparse depth map generated by a stereo vision system, producing a dense depth map. The algorithm is designed to be computationally efficient, for implementation onboard resource-constrained mobile robots and unmanned aerial vehicles. Within that context, it can be used to provide both reliability against a stereo camera failure, as well as more accurate depth perception, by filling in missing depth information, in occluded and low texture regions. This in turn allows the use of more efficient sparse stereo vision algorithms. We test the algorithm offline on a new, high resolution, stereo dataset, of scenes shot in indoor environments, and processed using both sparse and dense stereo matching algorithms. It is shown that the algorithm’s performance doesn’t deteriorate, and in fact sometimes improves, when learning only from sparse, high confidence regions rather than from the computationally expensive, dense, occlusion-filled and highly post-processed dense depth maps. This makes the approach very promising for self- supervised learning on autonomous robots.},
	author    = {Paquim, J.},
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor)},
	school    = {Delft University of Technology},
	title     = {Learning Depth from Single Monocular Images Using Stereo Supervisory Input},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:4b4c4e4b-5e45-4166-bd2c-f35a1e495c6a},
	year      = {2016}
}

@mastersthesis{uuid:e5604e9a-c241-4236-83dd-5fc823e7e284,
	abstract  = {Various methods for route planning of on-road vehicles to serve transportation requests have been developed in the literature in order to reduce transportation and operational costs. The applicability and thus development of these methods is primarily motivated by the field of application. This article deals with the mission planning for a fleet of drones to deploy sensors in a network. In particular, they are conceived to complete the task of delivering geophones in the seismic surveys. Unlike conventional on-road vehicles used for delivery purposes, every drone in the fleet is constrained to make a frequent return trip back to the depot to pick-up a new payload and restore its battery. A centralized planner is proposed in this article due to this constraint. The problem of planning is decomposed into two phases: route formation and route scheduling. The first phase is handled using the extensive formulation of Multi-Trip Vehicle Routing Problem (MTVRP) aiming at minimizing the overall journey time. A heuristic method is also proposed for this phase which provides near-optimal solutions in a computationally efficient manner. The second phase of the planning algorithm deals with the unaddressed problem of depot congestion arising due to the frequent visits of each drone to the depot. This problem is expressed in the form of a Mixed-Integer Linear Program (MILP) that can be solved using available software. This phase is computationally intensive and comparatively slow which restricts the usage of this mission planner in the re-planning phase to the cases involving longer journeys with limited number of routes. The results from a flight-test are also presented in order to demonstrate the mission planner.},
	author    = {Goyal, P.},
	keywords  = {},
	note      = {Hoekstra, J.M. (mentor); Blacquiere, G. (mentor); de Croon, G.C.H.E. (mentor); Smeur, E.J.J. (mentor)},
	school    = {Delft University of Technology},
	title     = {Mission Planning for Sensor Network Deployment using a Fleet of Drones},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:e5604e9a-c241-4236-83dd-5fc823e7e284},
	year      = {2016}
}

@mastersthesis{uuid:8efab9c5-e78b-40ff-ab37-a563366d22f9,
	abstract  = {Small robots, such as Micro Aerial Vehicles, form an increasingly popular field of interests in research, industry and the consumer market. The autonomous capabilities of these systems keep evolving and one of the main research goals is to reach full autonomy. However, this is often achieved at the cost of growing hardware demands. In this study a computationally light and efficient way to enhance autonomous on-board exploration capabilities for the DelFly Explorer, a 20-gram flapping wing Micro Aerial Vehicle (FWMAV), is presented. Both theory and new insights were combined to design an exploration algorithm for the on-board stereo-vision system. The algorithm primarily consists of a disparity map based decision tree, different exploration phases and computationally light odometry. Computer simulations proved the effectiveness of the algorithm to enable autonomous exploration capabilities for the FWMAV system. Initial flight tests also show that the proposed algorithm increases its exploration capabilities and form a foundation for future research.},
	author    = {Fonville, C.R.},
	keywords  = {DelFly Explorer; Exploration Algorithm; Autonomous Exploration; Flapping Wing Micro Aerial Vehicle},
	note      = {de Croon, G.C.H.E. (mentor)},
	school    = {Delft University of Technology},
	title     = {The Exploring DelFly: How to increase the indoor explored area of the DelFly Explorer by means of computationally efficient routing decisions?},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:8efab9c5-e78b-40ff-ab37-a563366d22f9},
	year      = {2016}
}

@mastersthesis{uuid:e714d5b9-236d-4509-bc80-79035c0f4725,
	abstract  = {This work proposes a control strategy to follow time optimal trajectories planned to visit a given set of waypoints in windy conditions. The aerodynamic effects of quadrotors are investigated, with emphasis on blade flapping, induced and parasitic drag. An extended method to identify all the aerodynamic coefficients is developed, and their influence on the performance is analyzed. A computationally efficient three steps approach is suggested to optimize the trajectory, by minimizing aerodynamic drag and jerk while still guaranteeing near optimal results. The derived smooth trajectory is compared with standard discrete point to point followed by low-pass filtering trajectories, showing energetic improvements in thrust and reductions in Euler angles aggressiveness. By exploiting the non-linear aerodynamic effects and using a priori trajectory information, a thrust vectoring controller is designed and compared with a standard PID controller, showing an increase in performance by reducing the tracking delay and extending the flight envelope.},
	author    = {da Rocha Silva, J.P.},
	keywords  = {},
	note      = {de Croon, G.C.H.E. (mentor)},
	school    = {Delft University of Technology},
	title     = {Quadrotor Thrust Vectoring Control with Time Optimal Trajectory Planning in Constant Wind Fields},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:e714d5b9-236d-4509-bc80-79035c0f4725},
	year      = {2016}
}

@mastersthesis{uuid:8145032a-9b1c-48c1-bf46-6f9d7405e5ef,
	abstract  = {A current limitation of using Micro Air Vehicles in teams is the high risk of collisions between members. Knowledge of relative location is needed in order to perform evasive maneuvers from such collisions. We propose an on-board Bluetooth-based relative localization scheme. Bluetooth is a light-weight and energy efficient communication technology that is readily available on even the smallest Micro Air Vehicle units. In this work, it is exploited for communication between team members to exchange on-board states (velocity, height, and orientation), and the strength of the communication signal is used to infer relative range. The data is fused on-board by each Micro Air Vehicle to obtain a relative estimate of the location and motion of all other team members. Furthermore, a collision avoidance controller is proposed based on collision cones. It is designed to deal with the expected performance of the localization scheme by adapting the collision cones during flight and enforcing a clock-wise evasion maneuver. The system was tested with a team of AR-Drones 2.0 flying in a 4m×4m arena. The task requested the AR-Drones to repeatedly fly from wall to wall whilst passing through the center of the arena, hence making collisions highly likely. The system showed promising results. When using two AR-Drones and off-board velocity/orientation estimates, the drones are able to fly around the arena and avoid each other for the entire flight time as permitted by the battery. With three AR-Drones under the same conditions, flight time to collision was 3 minutes. With two AR-Drones flying with on-board velocity estimation, the time to collision was approximately 3 minutes due to the disturbances in velocity estimates. Simulation results show that significantly better results can be expected with smaller units.},
	author    = {Coppola, M.},
	keywords  = {Micro Air Vehicles; Bluetooth; Relative Localization; Collision Avoidance; swarm; indoor; drones; team},
	note      = {De Croon, G. (mentor)},
	school    = {Delft University of Technology},
	title     = {Relative Localization for Collision Avoidance in Micro Air Vehicle Teams},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:8145032a-9b1c-48c1-bf46-6f9d7405e5ef},
	year      = {2016}
}

@mastersthesis{uuid:9eba1543-6f55-4708-9d68-09446a95d6d4,
	abstract  = {This paper presents an attitude controller for a small helicopter Unmanned Aerial Vehicle (UAV) based on Incremental Nonlinear Dynamic Inversion (INDI). INDI is a sensor-based control method which responds quickly to the commanded input, but also to disturbances. While previous implementations of INDI used a control effectiveness matrix describing effects on rotational accelerations, the implementation presented in this paper uses rotational rates. This is possible with small hingeless-rotor helicopters since the rotational rates are achieved almost immediately, but also the transient is taken into account. By doing so, the matrix contains only constants and the control structure is much simpler. The proposed controller is implemented on a small helicopter which weighs less than 50 grams. The performance of the controller is demonstrated with step responses on roll and heading angles. Also disturbance rejection capabilities are demonstrated. Finally, the controller is deliberately configured incorrectly with wrong control effectiveness and actuator model parameters. A theoretical derivation is provided to predict the effect of incorrect parameters. With experiments, it is demonstrated that the helicopter can be stabilized over a wide range of incorrect values. It is concluded that the demonstrated controller is a suitable choice for small autonomous helicopters.},
	author    = {Slinger, B.J.M.M.},
	keywords  = {INDI helicopter uav control attitude},
	note      = {Remes, B.D.W. (mentor)},
	school    = {Delft University of Technology},
	title     = {Attitude Control of a Small Helicopter UAV using Incremental Nonlinear Dynamic Inversion},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:9eba1543-6f55-4708-9d68-09446a95d6d4},
	year      = {2016}
}

@mastersthesis{uuid:f72c5d11-d959-4778-831d-2abe07945398,
	abstract  = {Autonomous navigation is a major challenge in the development of MAVs. When an algorithm has to be efficient, insect intelligence can be a source of inspiration. An elementary navigation task is homing, which means autonomously returning to the initial location. A promising approach makes use of visual familiarity of a route to determine reference headings during homing. In this thesis an existing biological proof of concept based on desert ants is transferred to MAVs. Vision-in-the-loop experiments in different environments are performed, to investigate the viability of scene familiarity for visual navigation. Trained images are used to determine which control actions to take during homing. To determine familiarity, either a database of stored images is kept or an artificial neural network is used. Different image representations are compared in multiple simulated environments. The use of textons for determining familiarity gives the best performance, but HSV color histograms also perform well and are very efficient. It is concluded that to make this method competitive with other visual navigation approaches, route familiarity should be combined with other methods to improve robustness.},
	author    = {Van Dalen, G.J.J.},
	keywords  = {visual homing; MAV navigation; insect-inspired navigation; scene familiarity; autonomous flight},
	note      = {De Croon, G.C.H.E. (mentor)},
	school    = {Delft University of Technology},
	title     = {Visual Homing for Micro Aerial Vehicles using Scene Familiarity},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:f72c5d11-d959-4778-831d-2abe07945398},
	year      = {2016}
}

@mastersthesis{uuid:55f9ab7a-2651-4a90-93a0-a8c9ddc7c6a9,
	abstract  = {This thesis presents all the work performed in developing a novel method for estimating distances on a flapping wing micro air vehicle using a monocular camera. These distance estimates are useful for providing a way to avoid collisions while flying indoors. The proposed method is based on a self-supervised learning algorithm that uses a short range impact detector to learn camera based long range distance estimates. The first part of this thesis contains an extended version of the paper on this topic as was submitted to the 2016 International Conference on Intelligent Robots and Systems (IROS). The second part contains the preliminary thesis that was preparatory to the final work and gives an in-depth overview of the state-of-the-art of different aspects of the problem as found in literature.},
	author    = {Lamers, K.},
	keywords  = {},
	note      = {Hoekstra, J.M. (mentor); De Croon, G.C.H.E. (mentor); Tijmons, S. (mentor); Guo, J. (mentor)},
	school    = {Delft University of Technology},
	title     = {Self-Supervised Monocular Distance Learning on a Lightweight Micro Air Vehicle},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:55f9ab7a-2651-4a90-93a0-a8c9ddc7c6a9},
	year      = {2016}
}

@mastersthesis{uuid:9ad6db51-5d2b-4680-b250-72b03ccc5fbb,
	abstract  = {No quantitative procedure currently exists to evaluate the obstacle avoidance capabilities of robotic applications. Such an evaluation method is needed for comparing different methods, but also to determine the operational limits of autonomous systems. This work proposes an evaluation framework which can find such limits. The framework comprises two sets of tests: detection tests and avoidance tests. For each set, environment and performance metrics need to be defined. For detection tests such metrics are well known. For avoidance tests however such metrics are not readily available. Therefore a new set of metrics is proposed. The framework is applied to a UAV that uses stereo vision to detect obstacles and three different algorithms to calculate the avoidance manoeuvre.},
	author    = {Nous, C.W.M.},
	keywords  = {Obstacle Avoidance; Evaluation Framework; Complexity Metrics},
	note      = {De Croon, G.C.H.E. (mentor)},
	school    = {Delft University of Technology},
	title     = {Performance Evaluation in Obstacle Avoidance},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:9ad6db51-5d2b-4680-b250-72b03ccc5fbb},
	year      = {2016}
}

@mastersthesis{uuid:483b3b05-4e72-44a2-840b-a207e06990af,
	abstract  = {Unmanned Aerial Vehicles (UAVs) can be used to access remote areas that were otherwise inaccessible, for example, for surveillance missions. Collaboration between them can help overcome communication constraints by building airborne relay networks that allow beyond line of sight communication. This research investigates if a single operator can supervise multiple UAVs in a collaborative surveillance task under communication constraints. For this purpose and ecological interface was designed to support operators in their task and to bring flexibility in the system. A human-in-the-loop evaluation study was performed to investigate the successfulness of operators in the control task of such a mission including an analysis of individual components of the interface. It was shown that operators are able to successfully operate surveillance missions under communication- and battery constraints. Participants did however not completely do this without separation conflicts and communication losses, which indicates that the interface lacks elements representing endurance and separation assurance. To an extent the interface design turned out to be scalable, with a few remaining visualizations that still suffered from this problem. More advanced ways of displaying information on request and grouping of select information is thought to offer opportunities to improve ground control interface on this matter.},
	author    = {Van Lochem, S.},
	keywords  = {ecological interface design},
	note      = {Borst, C. (mentor); De Croon, G.C.H.E. (mentor); Mulder, M. (mentor); Van Paassen, M.M. (mentor)},
	school    = {Delft University of Technology},
	title     = {Ecological Interface Design for Collaboration of Multiple UAVs in Remote Areas},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:483b3b05-4e72-44a2-840b-a207e06990af},
	year      = {2015}
}

@mastersthesis{uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd,
	abstract  = {We introduce, study and demonstrate Persistent Self-Supervised Learning (PSSL), a machine learning method for usage onboard robotic platforms. The PSSL model leverages a standard supervised learning method to simplify the learning problem, but acquires training data in an unsupervised and autonomous manner. Using two platforms, a small multicopter on earth and the space based test bed SPHERES inside the International Space Station , we demonstrate the PSSL principle on a proof of concept problem: learning monocular depth estimation using stereo vision. The robot operates first in a ground truth mode based on the distance perceived by the stereo system, while persistently learning the environment using monocular cues. After the performance of the estimator transcends a ROC quality measure, the robot switches to operation based on the monocular depth estimates. Our results show the viability of the PSSL method, by being able to navigate a room on the basis of learned monocular vision, without collecting any training data beforehand. We identify a major challenge in PSSL caused by a training bias due to behavioral differences in the estimator and the ground truth based operation; however, this is a known problem also for related learning methods such as reinforcement learning. PSSL helps solve this problem by 1) clearly separating the learning problem from the behavior and 2) the possibility to keep learning during estimator behavior.},
	author    = {Van Hecke, K.G.},
	keywords  = {persistent self-supervised learning; MAV},
	note      = {De Croon, G.C.H.E. (mentor); Van der Maaten, L.J.P. (mentor); Izzo, D. (mentor); Hennes, D. (mentor)},
	school    = {Delft University of Technology},
	title     = {Persistent self-supervised learning principle: Study and demonstration on flying robots},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd},
	year      = {2015}
}

@mastersthesis{uuid:3552d27e-6816-4ea3-85f6-4464deb8f1bd,
	abstract  = {Swarming is a promising solution for extending the flight time and payload carrying capabilities of Micro Aerial Vehicles (MAVs), where recent years have brought many advancements. These allow MAVs to operate ever more autonomously by tackling problems such as obstacle avoidance and autonomous navigation. A major challenge that still remains, however, is to ensure collision avoidance within the swarm itself. Avoiding collisions with other members of the swarm requires knowledge of their relative positions - typically requiring additional sensors to be carried on-board. Using the signal strength of the MAVs’ communication link provides an alternative method for estimating relative distances between the members of the swarm without requiring need for any additional sensors.},
	author    = {Szabó, T.},
	keywords  = {},
	note      = {Mulder, J.A. (mentor); de Croon, G.C.H.E. (mentor); de Visser, C.C. (mentor); Scheper, K.Y.W. (mentor); Verhoeven, C.J.M. (mentor)},
	school    = {Delft University of Technology},
	title     = {Autonomous Collision Avoidance for Swarms of MAVs: Based solely on RSSI measurements},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:3552d27e-6816-4ea3-85f6-4464deb8f1bd},
	year      = {2015}
}

@mastersthesis{uuid:dde8d42e-590a-465d-abaf-760ec304760f,
	abstract  = {Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this paper we show the first application of the Behaviour Tree framework to a real robotic platform using the Evolutionary Robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behaviour as compared to the traditional Neural Network formulation. As a result, the behaviour is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-gram DelFly Explorer flapping wing UAV equipped with a 4-gram onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the learnt behaviour in simulation 88% and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.},
	author    = {Scheper, K.Y.W.},
	keywords  = {Behaviour Tree; Evolutionary Robotics; Reality Gap; Micro Aerial Vehicle},
	note      = {De Croon, G.C.H.E. (mentor); De Visser, C.C. (mentor)},
	school    = {Delft University of Technology},
	title     = {Behaviour Trees for Evolutionary Robotics: Reducing the Reality Gap},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:dde8d42e-590a-465d-abaf-760ec304760f},
	year      = {2014}
}

@mastersthesis{uuid:cfa5fc25-4d67-482f-91d0-c09f6110af81,
	abstract  = {This study investigated the effect of chord deformation on the unsteady aerodynamic mechanisms found in hovering flapping flight at a Reynolds number of Re = 2002. This was done in order to get a better understanding of the physics involved in flapping flight, which in turn could lead to improved Micro Aerial Vehicle (MAV) designs. A three-dimensional numerical study was performed using an immersed boundary method (IBM) with the discrete forcing approach. The solver was first validated against an experiment by Kim and Gharib (2011).},
	author    = {Noyon, Tijs },
	keywords  = {},
	note      = {Bijl, Hester (mentor); van Oudheusden, Bas (mentor); Tay, Weebeng (mentor); de Wagter, Christophe (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {The effect of wing deformation on unsteady aerodynamic mechanisms in hovering flapping flight: Numerical study using a three-dimensional immersed boundary method},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:cfa5fc25-4d67-482f-91d0-c09f6110af81},
	year      = {2014}
}

@mastersthesis{uuid:6d92fc28-9fa0-426f-a374-609a9b8c132c,
	abstract  = {The goal of the thesis was to establish and validate a model for maneuvering fruit fly flight. Fruit flies are capable of rapidly changing direction and accelerating away from a threat during so-called escape maneuvers. The maneuverability and control of these escape maneuvers are of interest for the development of small unmanned aircraft (Micro Aerial Vehicles) and for the field of neurobiology where the wing kinematic response of fruit flies on visual stimuli is heavily studied.},
	author    = {Melis, Johan },
	keywords  = {},
	note      = {van Oudheusden, B.W. (mentor); Muijres, Florian (mentor); Remes, B.D.W. (mentor); Perçin, M. (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Maneuvering Fruit Fly Flight},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:6d92fc28-9fa0-426f-a374-609a9b8c132c},
	year      = {2014}
}

@mastersthesis{uuid:f97ac167-38e5-4155-933c-efa2ee179712,
	abstract  = {In the field of Micro Air Vehicle (MAV) research the use of flapping wings attracts a lot of interest. The potential of flapping wings lies in their efficiency at small scales and their large flight envelope with a single configuration. They have the possibility of performing both energy efficient long distance flights as well as hovering flights. Most studies on Flapping Wing MAVs (FWMAVs) have focused on the design of the airframe and making them able to fly. Currently, the state-of-the-art permits investigation of the necessary autonomous flight capabilities of FWMAVs. Most previous studies have made important preliminary steps by using external cameras or an onboard camera with the FWMAV flying in a modified environment. However, since autonomy is most useful for flight in unknown areas, it will be necessary to use an onboard camera while flying in unmodified environments. Research in this direction has been performed on the DelFly. In particular, the well-known cue of optic flow was found to be rather unreliable for the determination of 3D distances, and it was complemented by a novel visual appearance cue. Since the combination of these cues may still not be sufficient for robust and long-term obstacle avoidance, this study focuses on a different well-known method to extract 3D information on the environment: stereo vision. The potential advantage of stereo vision over optic flow is that it can provide instantaneous distance estimates, implying a reduced dependence on the complex camera movements during flapping flight. The goal is to employ stereo vision in a computationally efficient way in order to achieve obstacle avoidance. The focus of this study is on using heading control for this task. Four main contributions are made: The first contribution comprises an extensive study on literature in the field of computational stereo vision. This research has been done for decades and a lot of methods were developed. These mainly focus on optimizing the quality of the results, while disregarding computational complexity. In this study the focus was on finding one or more time efficient methods that give sufficient quality to perform robust obstacle avoidance. It was concluded that Semi-Global Matching is a good candidate. The second contribution is that for the first time it has been investigated what the requirements are for a stereo vision system to do successful stereo vision-based obstacle avoidance on FWMAVs. In order to achieve accurate stereo vision results, both hardware and software aspects are found to be of importance. FWMAVs can carry only a small amount of payload and therefore there is a large restriction on sensor weight. The third contribution is the development of a systematical way to use the 3D information extracted by the stereo vision algorithm in order to find a guaranteed collision-free flight path. The focus was on dealing with the limited maneuverability of the MAV and the limited view angle of the camera. The fourth contribution is in giving an indication on the usefulness of stereo vision based on multiple experiments. These focus on determining the accuracy of the obstacle detection method as well as on validating the functionality of the obstacle avoidance strategy. The designed system proved to be successful for the task of obstacle avoidance with FWMAVs. The DelFly II successfully avoided the walls in an indoor office space of 7.3×8.2m for more than 72 seconds. This is a considerable improvement over previous monocular solutions. Since even reasonable obstacle detection could be performed for low-textured white walls, the experiments clearly show the potential of stereo vision for obstacle avoidance of FWMAVs. In combination with existing methods for speed and height control the proposed system has the potential of making fully autonomous (flapping wing) MAVs possible.},
	author    = {Tijmons, S.},
	keywords  = {},
	note      = {Mulder, J.A. (mentor); De Croon, G.C.H.E. (mentor); Van Kampen, E. (mentor); Remes, B.D.W. (mentor)},
	school    = {Delft University of Technology},
	title     = {Stereo Vision for Flapping Wing MAVs: Design of an Obstacle Avoidance system},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:f97ac167-38e5-4155-933c-efa2ee179712},
	year      = {2012}
}

@mastersthesis{uuid:1ad96acb-0e49-4edd-b453-6523e7296c50,
	abstract  = {The Delfly is subject of great interest from the aerodynamics department at the TU Delft. Current wind tunnel measurements are performed with a dual high speed camera setup that detect particles injected in the wind stream. The difference between two subsequent images provides information on the flow field around the wings of the Delfly. These measurements are always performed with the Delfly fixed on a support. Although this method produces a lot of useful data, the restrictions that the support introduces makes it not a true representation of the free flight conditions. This thesis goal therefore, was to design, build and test a system that would enable the Delfly to fly freely in the wind tunnel. This would allow the same measurements to be performed without a support, providing insight in the influence of the support on the aerodynamic properties of the Delfly. A low-cost, high performance tracking system using two Wiimotes was developed, providing 3D position information with an accuracy of 0.8 mm and a tracking rate up to 80 Hz. A custom auto pilot module was designed, containing a 3-axis gyro and an infrared camera. A small Bluetooth module provided two way communication between the Delfly and the ground station, allowing the position information to be sent up to the Delfly and can log the information from the on-board sensors. Using the tracking system and a LED in the middle of the wind tunnel to provide the camera with a heading reference, a PI controller was implemented on-board. The controller could successfully keep the Delfly within ±1.7 cm in forward and vertical direction, and within ±3.5 cm in lateral direction of the reference point. It is the first time in the world that a flapping wing micro aerial vehicle was flown autonomously in the wind tunnel. The achieved precision is sufficient for the aerodynamic measurements to be performed, which could shed more light on the way the wind tunnel support influences the properties of the Delfly. Furthermore, for the first time, good quality data has been gathered on the dynamic behavior of the Delfly. This can serve as a starting point for future projects, such as the design of more advanced controllers that cope with the observed non-linearities or provide a reference for future research on the dynamics of the Delfly},
	author    = {Koopmans, J.A.},
	keywords  = {},
	note      = {Mulder, J.A. (mentor); Chu, P. (mentor); van Kampen, E. (mentor); van Oudheusden, B.W. (mentor); Remes, B. (mentor)},
	school    = {Delft University of Technology},
	title     = {Delfly Freeflight: Autonomous flight of the Delfly in the wind tunnel using low-cost sensors},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:1ad96acb-0e49-4edd-b453-6523e7296c50},
	year      = {2012}
}

@mastersthesis{uuid:7d688e57-328c-4d76-990f-e619221feeb4,
	abstract  = {Flapping wing flight has attracted increased interest among aerodynamics researchers recently in view of the recent expansion of design efforts in the field of Micro Aerial Vehicles (MAVs). MAVs are given specific attention because of their potential as mobile platforms capable of reconnaissance and gathering intelligence in hazardous and physically inaccessable areas. To achieve these missions, they should be manoevring with ease, staying aloft and propelling themselves efficiently. Conventional means of aerodynamic force generation are found lacking at this point and the apping-wing approach becomes an appealing or even necessary solution. In contrast to the conventional (fixed and rotary wing) force generation mechanisms, apping wing systems take benefit from the unsteady ow effects that are associated to the vortices separating from the wing leading and trailing edges, which create low pressure regions around the wings that lead to the generation of higher lift and thrust.},
	author    = {Eisma, Jerke },
	keywords  = {},
	note      = {Scarano, Fulvio (mentor); van Oudheusden, Bas (mentor); Perçin, Mustafa (mentor); Remes, Bart (mentor); Delft University of Technology (degree granting institution)},
	school    = {TU Delft Aerospace Engineering},
	title     = {Flow visualization and force measurements on a flapping-wing MAV DelFly II in forward flight configuration},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:7d688e57-328c-4d76-990f-e619221feeb4},
	year      = {2012}
}

@mastersthesis{uuid:f8848255-1831-482a-be21-2da3ca3e50bb,
	abstract  = {The development of systems that allow unmanned aerial vehicles, known as UAVs, to perform tasks autonomously is a current trend in aerospace research. The specific aim of this thesis is to study and achieve vision-based automatic landing of a quadrotor UAV on a floating platform, a known target that possesses oscillatory behavior. The research contributions to be taken from this study can be divided into two perspectives, as described below. From a theoretical point of view, a design solution is proposed which includes GPS navigation to enable the quadrotor to find the target, and vision-based control to approach and land upon it. From this design, several control-related issues must then be solved, mainly the development of a controller for the autoland mission. To accomplish this control task, an incremental backstepping control law is developed. Additionally, linear and standard backstepping controllers are designed for comparison. The derived control laws require knowledge of the states to close the feedback loops; therefore, state estimation algorithms are designed for complete state reconstruction. The approach selected is modular, thus separating position/velocity estimation from attitude determination. The former is performed using an extended Kalman filter, and the latter using a complementary filter. Furthermore, an augmented Kalman filter formulation is developed for estimation of the platform’s vertical motion. The combination of control and state estimation algorithms is tested in a simulated environment using a simulation tool developed in this study for Monte-Carlo analysis. This tool allows for evaluation of the design not only for the nominal case, but also for random combinations of external conditions. Results show that successful performance is obtained for the nonlinear controllers since the desired criteria is met and the risk of crashing is demonstrated to be residual. Additional tests show that incremental backstepping is, in general, more robust than standard backstepping in the case of model mismatch, even in the presence of state estimation errors. From a practical perspective, the findings are twofold. First, this thesis presents a procedure to experimentally determine the moments of inertia of the quadrotor by using a two-axis motion simulator and a six-component force/torque sensor. The inertia properties are also determined analytically using two modeling approaches: point mass analysis and assumption of simple geometric shapes. The results show that point mass analysis can lead to erroneous inertia estimation deviation of 20-30% from the real value), thus resulting in a significant model mismatch. The experimental and simple shapes assumption methods render similar results, which strongly indicates not only that the experimental method proposed is valid, but also that the assumption of simple geometric shapes can be used as a reliable and cost-effective method to determine moments of inertia of small UAVs. Second, in this thesis the system is tested in real time using an actual quadrotor. Flight tests are performed for hovering above a target with known characteristics, and to achieve this end, a vision system is developed to obtain relative position measurements from images captured by an on-board camera. A Kalman filter is implemented for real-time integration of vision with IMU data, and a linear controller with reference command filters is used. Tuning procedures are then carried out until satisfactory performance is achieved.},
	author    = {Mendes, A.S.},
	keywords  = {Quadrotor UAV; Moments of inertia experiment; Computer vision; Augmented Kalman filtering; Incremental backstepping; Monte-Carlo simulations; Real-time implementation},
	note      = {Chu, Q.P. (mentor); Van Kampen, E. (mentor); Mulder, J.A. (mentor); Remes, B.D.W. (mentor)},
	school    = {Delft University of Technology},
	title     = {Vision-based automatic landing of a quadrotor UAV on a floating platform: A new approach using incremental backstepping},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:f8848255-1831-482a-be21-2da3ca3e50bb},
	year      = {2012}
}

@mastersthesis{uuid:4b95f124-7ea5-49ea-af65-dfb436d6bdc7,
	abstract  = {Due to the high cost of flight, there is a high evolutionary selection pressure for energy efficient flight patterns, such as using external natural forces for soaring or flying intermittently. Some bats at time soar, glide or flap glide. Bounding flight is not possible as their membranous wings will go slack, and soaring is not common amongst bats, as most bats are nocturnal and during night thermals are usually of insufficient strength. From an aerodynamic point of view, gliding flight is less complex than flapping flight, however in bats undulating flight patterns are less observed than in birds. So, why should bats glide? Flight performance studies on live bats have revealed a part of the complexity of hovering and steady flapping flight, but gliding flight in these animals is poorly studied. To get insight in how bats glide and in their gliding flight performance, gliding flight of bats is studied from two points of view; gliding flight of real bats and gliding of a flexible, bat inspired wing model, in a low speed, tiltable wind tunnel. The kinematics of both the bats and the model are filmed by two synchronised high speed cameras, and the flow field in a transverse plane behind the wings is visualized by means of a PIV system. Three medium sized bats Leptonycteris yerbabuenae, are trained to glide at a feeder in the test section of the wind tunnel at a know, fixed glide angle. This known glide angle enables to calculate the aerodynamic forces, which are fixed properties in steady gliding flight. A gliding wing model, based on a bat’s wing, with an adjustable leading edge flap, is designed, build, and tested at different angles of attack. The wing model is tested with both a smooth and a structured top surface to see what the effect of ’turbulators’ can be. Additionally the wing model is mounted onto a balance in order to measure the aerodynamic forces. By means of experiments with the wing model, wake structures of gliding flight can be connected to a single changing morphology parameter to explore the parameter space, and the wake structures can be compared to the wake structures of the gliding bats. The bats are observed to glide for some seconds in the test section, but only the parts of the glides at the feeder where the tip vortex strength and position were stable are analysed. From the PIV data, an average wake is constructed per glide sequence of the bats, and for each leading edge setting and speed combination of the model wing. From the average wake the flight forces and the resulting flight performance properties are derived. The wing model approaches the glide behaviour of the bats. Deploying the leading edge flap increases the span efficiency and the lift coefficient at low angles of attack. Also the structure on top of the wing is beneficial for flight performance at low angles of attack.},
	author    = {Stuiver, M.},
	keywords  = {},
	note      = {Bijl, H. (mentor); van Oudheusden, B.W. (mentor); Muijres, F.T. (mentor); Remes, B.D.W. (mentor)},
	school    = {Delft University of Technology},
	title     = {Bats in Gliding Flight: A comparative wind tunnel investigation of the aerodynamics of gliding bats and a bat inspired gliding wing model},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:4b95f124-7ea5-49ea-af65-dfb436d6bdc7},
	year      = {2011}
}

@mastersthesis{uuid:291207fc-5ada-435c-be39-8e1436fd96c9,
	abstract  = {Today, there is an increase in the use of Unmanned Aerial Vehicles (UAV's), for applications that can be considered dull, dirty or dangerous when compared to those applications of conventional aircraft or helicopters. To further increase the use of UAV's, their navigation filters must be robust and reliable. The trend in current autopilot development is defined by the ever decreasing size of vehicles leading to the creation of miniature Inertial Navigation Systems (INS) with low cost, low grade sensors. Small flying vehicles have fast dynamics requiring higher control rates and higher dynamic ranges with minimal available onboard computational capacities. Sensor and processing limitations have consequences for the achievable navigation performance. This in turn poses limits on the minimal vehicle stability, weather conditions and trajectory smoothness. The most important aspect and thesis goal is to guarantee the navigation filter solution robustness during all flight maneuvers. A navigation filter is an integration algorithm that provides a navigation solution on the vehicle's state vector from sensor data. This thesis focuses on one UAV platform in particular, namely small fixed-wing UAV's. One of the main challenges with designed navigation filters is that they can be theoretically stable but the outcome can sometimes not be used. In practice, the navigation filter outcome can give a diverging solution while theoretically stable. The goal of this thesis is to define the minimal requirements of sensors and other hardware for an INS such that the stabilization requirements posed by the vehicle dynamics and size can be satisfied. With the requirements stated, smaller and more dynamic fixed-wing UAV's can be stabilized based on the integrated navigation solution. The developed observability analysis tool is able to provide a quantitative analysis on the state observability that can be used to analyze different systems or sensor configurations. The observability matrix is composed of the system and observer dynamics. The system dynamics is based on the Inertial Measuring Unit (IMU) prediction of the system states, the observer equations correspond to the observer dynamics. A non-linear local observability analysis has been performed to calculate the observability matrix. The traditional Singular-Values Decomposition (SVD) algorithm provides the singular values of an observability matrix in a decreasing order and indicates the rank of the system. The rank of the observability matrix corresponds to the number of observable system states, the SVD can however not directly link the singular values to the system states. To overcome this problem a different matrix decomposition is used that is able to directly couple the singular values to the system states. This developed matrix decomposition algorithm is based on the QR factorization, called QRsvd. With this algorithm it is possible to quantitatively indicate the observability (degree) of each system state. An analysis into the physical properties of fixed-wing aircraft kinematics resulted in new insight into the movement of flying vehicles. Based on the derived kinematics together with the coupling of an IMU, GPS receiver and fixed-wing aircraft kinematics this resulted in new physical insight. This resulted in three angle correction (AC) equations that can be used as additional attitude/heading angle observers to the conventional IMU/GPS integration. With these three additional observers, the three orientation angles become instantaneously observable. Without the AC equations, a rotational rate constraint is always present to integrate the IMU with GPS. GPS receivers and IMU are separate, self-contained subsystems with different updating frequencies and processing times. Resulting clock differences are called time synchronization errors and result in filter estimation problems. A time synchronization requirement is derived, which is a function of changes in vehicle accelerations and filter innovation. The time synchronization requirement is proportional to the magnitude of the change in vehicle accelerations a and negatively proportional to the magnitude of the identification filter innovation. Vehicles with fast dynamics, like fixed-wing UAV's, can have larger changes in vehicle accelerations magnitude, resulting in a more stringent time synchronization requirement. Based on performed simulations and verification with flight test data, it can be concluded that the improved IMU/GPS filter with AC equations can provide a stable long-term navigation solution with accurate short-term performance, by using (Iterated) Extended Kalman filters. During the performed simulations the position states give the largest source of error, due to the large GPS position uncertainty. For the three orientation angles, the heading angle has a larger identification error compared to the pitch and roll angle. For the orientation angles, the influence of atmospheric wind on the identification performance is minimal except for the heading angle due to the presence of a side-slip angle beta. Coordinate transformations between the Earth, North-East-Down (NED) reference frame F_E and the body-fixed reference frame F_B can be performed using a rotational transformation matrix R_BE. The antisymmetric matrix R_BE holds special properties that can be utilized and fits in the category of Special Orthogonal Lie groups with a dimension of three, called SO(3). Based on SO(3) group properties, a non-linear complementary filter can be constructed that uses this matrix as a single state. The non-linear complementary filter on the SO(3) group, can be used as an alternative to conventional Kalman state identification filters. For (I)EKF the heading angle is the largest source of error of the attitude/heading angles, this is also the case for the SO(3) filter. Differences between the SO(3) filter and (I)EKF are due to two aspects. The SO(3) filter uses constant proportional and integrator gains, where Kalman gain matrices include process and observer uncertainties. The other source of differences can be found in the strong coupling between the individual attitude/heading angles for the non-linear SO(3) filter compared to (I)EKF.},
	author    = {Hummelink, B.A.},
	keywords  = {UAV; IMU; GPS; Kalman; filtering; identification; observability; Lie groups},
	note      = {Chu, Q.P. (mentor); Mulder, J.A. (mentor); De Croon, G.C.H.E. (mentor); De Wagter, C. (mentor)},
	school    = {Delft University of Technology},
	title     = {Fixed-Wing UAV Integrated Navigation with Low-Cost IMU/GPS},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:291207fc-5ada-435c-be39-8e1436fd96c9},
	year      = {2011}
}

@mastersthesis{uuid:610da696-9202-4f11-ba65-bea67d2edd0b,
	abstract  = {Recent years have seen an increasing interest in Micro Air Vehicles (MAVs). MAVs are small (micro sized) aircraft and find their application in a multitude of commercial, industrial and military purposes. To perform their missions MAVs should be small sized, have good manoeuvrability, be well controllable and have a broad flight envelope. When flying in small confinements, the ability to fly at low airspeed and to have good manoeuvrability is critical. One type of MAVs, the flapping-wing MAV, particularly has attractive characteristics for flight in confined spaces. DelFly is a biplane flapping-wing MAV designed and built at Delft University of Technology. DelFly is able to hover and has an onboard camera for observation and vision-based control. For the DelFly project a top-down approach is followed, where from the study of a relative large model experience and theoretical insights can be gained, that can assist to create smaller, functional versions of the DelFly. The ultimate aim of the DelFly project is to improve the design to a very small full autonomous aircraft. For the current experimental investigation, force and flow field measurements were performed on a hovering DelFly II, since this model has a broad flight envelope and proven flight performance. The flow field is studied using particle image velocimetry. Due to the flexible wings there is a strong fluid structure interaction, therefore also the in-flight wing deformation is determined. The aerodynamic mechanism generating forces on the DelFly are related to those found in insect flight. Since leading edge vortices (LEVs) in insect flight are identified as the most important unsteady aerodynamic mechanism enhancing lift generation for insects, the development of these for the DelFly are very interesting. The vortex development is studied for various wings, at various flapping frequencies and at various spanwise positions. For the DelFly wing a conical LEV is developed, starting at out-board spanwise positions, approximately halfway during the translation. This LEV grows larger and is shed along the chord and at this time a new LEV starts to grow at the leading edge. This second LEV is dissipated at the end of the out-stroke during wing rotation, but at the end of the in-stroke this LEV moves above the wings and interacts with the counter-rotating LEV from the mirror wing. Inside the vortex tube a spanwise velocity component out-board is present. The shedding of the initial vortex and start of a second LEV is not completely consistent with LEV development for insect flight (which typically operate at a lower Reynolds number).},
	author    = {Groen, M.A.},
	keywords  = {},
	note      = {Bijl, H. (mentor); van Oudheusden, B.W. (mentor); Goosen, J.F.L. (mentor); Remes, B.D.W. (mentor)},
	school    = {Delft University of Technology},
	title     = {PIV and force measurements on the flapping-wing MAV DelFly II: An aerodynamic and aeroelastic investigation into vortex development},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:610da696-9202-4f11-ba65-bea67d2edd0b},
	year      = {2010}
}

@mastersthesis{uuid:03f73d85-3684-499c-affc-637d2a5971bf,
	abstract  = {Recent years have seen an increasing interest in micro aerial vehicles (MAV). The same can be said about flapping flight. The Delft University of Technology started to develop a flapping wing MAV in 2005, ”DelFly”, which relies on a flapping biplane wing configura- tion for thrust and lift generation. DelFly has evolved significantly during the last years. At the time of writing there are already three version of DelFly; DelFly I, DelFly II and DelFly Micro. The test subject of this study is DelFly II because of its stable and broad flight envelope. The aim of this study is to improve flight performance of the DelFly II. Hereto, in this thesis report, a wing geometry study is performed in order to improve the aerodynamic performance of the wing and the driving mechanism is improved in order to increase the efficiency of energy transfer from the battery to the movement of the leading edges. The current study resulted in a increase of thrust-to-power ratio of 5% due to the wing design and 20% due to the new crank-shaft mechanism},
	author    = {Bruggeman, Bart},
	keywords  = {},
	note      = {van Tooren, M.J.L. (mentor); Bijl, H. (mentor); Ruijsink, H.M. (mentor); Remes, B.D.W. (mentor); Goosen, J.F.L. (mentor)},
	school    = {Delft University of Technology},
	title     = {Improving flight performance of DelFly II in hover by improving wing design and driving mechanism},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:03f73d85-3684-499c-affc-637d2a5971bf},
	year      = {2010}
}

@mastersthesis{uuid:4eb36179-9441-4c20-86ca-9cb671047da4,
	abstract  = {This thesis focuses on the development of an aerodynamic optimization algorithm for long range mini-UAV’s. This algorithm is applied to the design of the TU Delft mini- UAV that participated in the EMAV2009 outdoor endurance mission. The analysis of the low Reynolds number (< 2.5 · 105) aerodynamics on the wing is performed using a quasi-3D method which combines a vortex lattice method with viscous airfoil data. The optimization part of the program is accomplished by a sequential quadratic programming algorithm. RANS-CFD calculations and wind tunnel experiments were performed to validate the newly developed quasi-3D method. The final design for the mini-UAV has lift over drag ratio of 11.8 and a high capacity battery (8Ah) which give it a total range of 166 km},
	author    = {Trips, Dennis},
	keywords  = {},
	note      = {van Tooren, M. (mentor); Straathof, M.H. (mentor); Remes, B.D.W. (mentor); Timmer, W.A. (mentor)},
	school    = {Delft University of Technology},
	title     = {Aerodynamic Design and Optimization of a Long Range Mini-UAV},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:4eb36179-9441-4c20-86ca-9cb671047da4},
	year      = {2010}
}

@mastersthesis{uuid:fe5ea73c-a85b-4a4b-b5d5-fc01d29b2113,
	abstract  = {This work addresses the problem of indoor state estimation for autonomous flying vehicles with an optic flow approach. The paper discusses a sensor configuration using six optic flow sensors of the computer mouse type augmented by a three-axis accelerometer to estimate velocity, rotation, attitude and viewing distances. It is shown that the problem is locally observable for a moving vehicle. A Kalman filter is used to extract these states from the sensor data. The resulting approach is tested in a simulation environment evaluating the performance of three Kalman filter algorithms under various noise conditions. Finally, a prototype of the sensor hardware has been built and tested in a laboratory setup. Paper published: Verveld, M.J., Chu, Q.P., De Wagter, C. and Mulder, J.A. “Optic Flow Based State Estimation for an Indoor Micro Air Vehicle” AIAA Guidance, Navigation, and Control Conference, August 2010, Toronto, Canada AIAA 2010-8209, DOI: 10.2514/6.2010-8209},
	author    = {Verveld, M.J.},
	keywords  = {optical flow; sensor fusion; Unscented Kalman filter; bio-inspired flight},
	note      = {Mulder, J.A. (mentor); Chu, Q.P. (mentor); de Wagter, C. (mentor)},
	school    = {Delft University of Technology},
	title     = {Optical Flow Based State Estimation for an Indoor Micro Aerial Vehicle},
	type      = {mathesis},
	url       = {http://resolver.tudelft.nl/uuid:fe5ea73c-a85b-4a4b-b5d5-fc01d29b2113},
	year      = {2009}
}