Reactive Planner

This project generates solutions to trajectory planning problems given in the CommonRoad 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:

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.

For the development IDE we suggest 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