https://github.com/ethanjameslew/rrt_merci

RRT Implementation used for GRAIC Racing Competition
https://github.com/ethanjameslew/rrt_merci

autonomous-racing autonomous-vehicles graic path-planning robotics-competition rrt rrt-star

Last synced: 12 months ago
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RRT Implementation used for GRAIC Racing Competition

• Host: GitHub
• URL: https://github.com/ethanjameslew/rrt_merci
• Owner: EthanJamesLew
• License: bsd-3-clause
• Created: 2022-06-01T03:58:54.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-06-15T18:02:31.000Z (almost 4 years ago)
• Last Synced: 2023-03-04T05:23:36.330Z (over 3 years ago)
• Topics: autonomous-racing, autonomous-vehicles, graic, path-planning, robotics-competition, rrt, rrt-star
• Language: Rust
• Homepage:
• Size: 17.2 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# MERCI RRT Path Planners

Implementations of RRT-based algorithms used in [GRAIC 2.0](https://popgri.github.io/Race/outreach/) Competition by team 

MERCI (MEthodological Racing Computer Intelligence). **MERCI placed 1st in the Head-to-Head category.**

![racing example](./doc/img/planner_demo.gif)

| Rapidly Exploring Random Trees (RRT)    | RRT*                                            |

|----------------------------------------|-------------------------------------------------|

| ![RRT Diagram](./doc/img/RRT_Tree.svg) | ![RRT Star Diagram](./doc/img/RRTStar_Tree.svg) |

## Overview

The RRT implementations are designed for 2D vehicle racing, containing

* Planners

    * Rapidly Exploring Random Trees (RRT)

    * RRT*

    * [TODO] Kinematic RRT

        * Dubins Path

    * [TODO] Dynamic RRT

        * LQR RRT?

* Obstacles

    * Circles

    * Rectangles 

    * Convex Polygons 

    * Parallel Curves 

* Smoothers

    * Random Path Smoothing (obstacle aware)

* Samplers

    * Uniform

    * [TODO] Sobol

## Benchmarks

We have a crude [benchmarks](./notebooks/rrt-benchmark.ipynb) notebook. We see that for RRT implementations, MerciPy achieves planning **10s to 100s of times faster than an existing python implementation**.

## References 

Algorithms were taken from their respective papers. Also, Pythonrobotics implementations were referenced.

### PythonRobotics

> Sakai, A., Ingram, D., Dinius, J., Chawla, K., Raffin, A., & Paques, A. (2018). Pythonrobotics: a python code collection of robotics algorithms. arXiv preprint arXiv:1808.10703.

### RRT

> LaValle, S. M., & Kuffner Jr, J. J. (2001). Randomized kinodynamic planning. The international journal of robotics research, 20(5), 378-400.

### RRT*

> Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The international journal of robotics research, 30(7), 846-894.

## Python Bindings

See the [mercipy](./mercipy) folder.