https://github.com/commonroad/commonroad-reactive-planner

A sampling-based motion planner for autonomous vehicles using quintic polynomials in a Frenet frame
https://github.com/commonroad/commonroad-reactive-planner

Last synced: about 2 months ago
JSON representation

A sampling-based motion planner for autonomous vehicles using quintic polynomials in a Frenet frame

• Host: GitHub
• URL: https://github.com/commonroad/commonroad-reactive-planner
• Owner: CommonRoad
• License: bsd-3-clause
• Created: 2024-03-21T10:11:02.000Z (10 months ago)
• Default Branch: master
• Last Pushed: 2024-04-23T13:45:38.000Z (9 months ago)
• Last Synced: 2024-04-23T14:34:13.361Z (9 months ago)
• Language: Jupyter Notebook
• Size: 0 Bytes
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Reactive Planner

This project generates solutions to trajectory planning problems given in the [CommonRoad](https://commonroad.in.tum.de/) scenario format.

The trajectories are generated using the sampling-based approach in [1][2]. This approach plans motions by sampling a 

discrete set of trajectories, represented as quintic polynomials in a Frenet frame and selecting an optimal trajectory according

to a given cost function.



## Getting Started

These instructions should help you to install the trajectory planner and use it for development and testing purposes.

To install the package from PyPi, please run:

```shell

pip install commonroad-reactive-planner

```

### Requirements

The software is  written in Python 3.8 and tested on Ubuntu 18.04-22.04. The required python dependencies are listed in `pyproject.toml`.

For the python installation, we suggest the usage of [Anaconda](http://www.anaconda.com/download/#download).

For the development IDE we suggest [PyCharm](http://www.jetbrains.com/pycharm/)

### Installation from Source

1. Clone this repository & create a new conda environment, e.g.,  `conda create -n commonroad-py38 python=3.8`

2. Go to cloned root directory and install the package:

    * Install the package via poetry: `poetry install`

    * Install the package via pip: `pip install .`

### How to run

**Main example script** `run_planner.py`: 

The example script shows how to run the planner on an exemplary CommonRoad scenario with the following steps:

* creating a planner configuration

* instantiating the reactive planner

* running the planner in a cyclic replanning loop with a fixed replanning fequency

In addition we also provide an interactive Jupyter notebook tutorial in the `tutorial/` folder.

## Literature

[1] Werling M., et al. *Optimal trajectory generation for dynamic street scenarios in a frenet frame*. In: IEEE International Conference on Robotics and Automation, Anchorage, Alaska, 987–993.

[2] Werling M., et al. *Optimal trajectories for time-critical street scenarios using discretized terminal manifolds* In:

The International Journal of Robotics Research, 2012