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Adding in roscon talk links to readmes #4821

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Jan 3, 2025
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Adding in roscon talk links to readmes #4821

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3 changes: 0 additions & 3 deletions README.md
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Expand Up @@ -39,9 +39,6 @@ Please thank our amazing sponsors for their generous support of Nav2 on behalf o

### [Stereolabs](https://www.stereolabs.com/) produces the high-quality ZED stereo cameras with a complete vision pipeline from neural depth to SLAM, 3D object tracking, AI and more.

### Confidential is just happy to support Nav2's mission!


## Citation

If you use the navigation framework, an algorithm from this repository, or ideas from it
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5 changes: 5 additions & 0 deletions nav2_docking/README.md
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Expand Up @@ -17,6 +17,11 @@ This is split into 4 packages

Click on the image above to see an extended video of docking in action.

Want to learn more? Checkout the ROSCon 2024 talk on Docking by clicking on the image below!

[![IMAGE ALT TEXT](https://github.com/user-attachments/assets/468bb49c-87de-4c9e-83a8-ad6f14bbd6d3)](https://vimeo.com/1024971348)


## Architecture

The Docking Framework has 5 main components:
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4 changes: 4 additions & 0 deletions nav2_mppi_controller/README.md
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Expand Up @@ -12,6 +12,10 @@ This controller is measured to run at 50+ Hz on a modest Intel processor (4th ge

It works currently with Differential, Omnidirectional, and Ackermann robots.

Want to learn more? Checkout the ROSCon 2023 talk on the MPPI Controller by clicking on the image below!

[![IMAGE ALT TEXT](https://github.com/user-attachments/assets/4e091c5d-9687-457c-a7fb-d0e0689fbaea)](https://vimeo.com/879001391)

## MPPI Description

The MPPI algorithm is an MPC variant that finds a control velocity for the robot using an iterative approach. Using the previous time step's best control solution and the robot's current state, a set of randomly sampled perturbations from a Gaussian distribution are applied. These noised controls are forward simulated to generate a set of trajectories within the robot's motion model.
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4 changes: 4 additions & 0 deletions nav2_smac_planner/README.md
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Expand Up @@ -19,6 +19,10 @@ We have users reporting using this on:

See its [Configuration Guide Page](https://docs.nav2.org/configuration/packages/configuring-smac-planner.html) for additional parameter descriptions.

Want to learn more? Checkout the ROSCon 2022 talk on the Smac Planner by clicking on the image below!

[![IMAGE ALT TEXT](https://github.com/user-attachments/assets/95717450-2ff8-4576-ae38-88e7f266e8c6)](https://vimeo.com/showcase/9954564/video/767157646)

## Introduction

The `nav2_smac_planner` package contains an optimized templated A* search algorithm used to create multiple A\*-based planners for multiple types of robot platforms. It was built by [Steve Macenski](https://www.linkedin.com/in/steve-macenski-41a985101/) while at [Samsung Research](https://www.sra.samsung.com/). We support **circular** differential-drive and omni-directional drive robots using the `SmacPlanner2D` planner which implements a cost-aware A\* planner. We support **legged, cars, car-like, and ackermann vehicles** using the `SmacPlannerHybrid` plugin which implements a Hybrid-A\* planner. We support **non-circular, arbitrary shaped, any model vehicles** using the `SmacPlannerLattice` plugin which implements a State Lattice planner. It contains control sets and generators for ackermann, legged, differential drive and omnidirectional vehicles, but you may provide your own for another robot type or to have different planning behaviors. The last two plugins are also useful for curvature constrained or kinematically feasible planning, like when planning robot at high speeds to make sure they don't flip over or otherwise skid out of control. It is also applicable to non-round robots (such as large rectangular or arbitrary shaped robots of differential/omnidirectional drivetrains) that need pose-based collision checking.
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