Military Drones Simulation

A ROS 2 Jazzy + Gazebo Harmonic based multi-drone surveillance simulation. The system implements a swarm of three modified X3 quadcopters equipped with cameras and onboard object detection using YOLOv11.

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✨ Features

• Three surveillance drones (X3_1, X3_2, X3_3) modeled in Gazebo.
• Custom onboard cameras for live video feeds.
• YOLOv11 TFLite-based object recognition via object_recognizer.py.
• Centralized swarm planner for multi-drone coordination.
• GUI (drone_gui.py) to monitor and interact with drones.
• ROS 2 ↔ Gazebo bridge for control and sensor integration.
• Predefined environment objects (car, horse, man, zebra, etc.).

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📂 Repository Structure

military_drones/
├── LICENSE                         # License file
├── military_drones_bringup          # Launch and configs
├── military_drones_control          # Control nodes, GUI, object recognition
│   └── resources                    # YOLO model + dataset configs
├── military_drones_description      # Drone and environment models
├── military_drones_gazebo           # Gazebo plugins and worlds
└── README.md                        # Project documentation

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🚀 Installation

Tested on Ubuntu 24.04 (Noble Numbat) with ROS 2 Jazzy on Gazebo Harmonic.

# Clone the repository
cd ~/ros_ws/src
git clone github.com/Manohara-Ai/Military_Drones military_drones

# Install dependencies
sudo apt update
rosdep install --from-paths src --ignore-src -r -y

# Build workspace
cd ~/ros_ws
colcon build
source install/setup.bash

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▶️ Running the Simulation

Launch the full system:

ros2 launch military_drones_bringup military_drones.launch.py

This will:

• Spawn the 3 X3 quadcopters in Gazebo.
• Start swarm control nodes (central_planner, flight_controller).
• Run YOLOv11 object recognition (object_recognizer).
• Open the GUI and RViz visualization.
• Establish ROS 2 ↔ Gazebo bridges.

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📦 Packages

military_drones_bringup

• Launch files & configs.
• RViz visualization setup.

military_drones_control

• central_planner.py → swarm mission coordination.
• flight_controller.py → per-drone control.
• object_recognizer.py → YOLOv11 inference.
• drone_gui.py → live monitoring GUI.
• resources/ → contains YOLO model & dataset labels.

military_drones_description

• Models of drones & environment objects.
• Textures, meshes, SDF configs.

military_drones_gazebo

• Gazebo world (world.sdf).
• Plugins for system simulation.

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📌 Future Work

• Enhance multi-drone autonomy with reinforcement learning.
• Implement SLAM-based navigation.
• Add real-time communication between drones.
• Improve GUI with mission planning tools.

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👨‍💻 Authors

Developed by Manohara B M & Vibhashree Vasuki.

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📜 License

This project is licensed under the MIT License. See LICENSE for details.