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.
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-plannerThe 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
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Clone this repository & create a new conda environment, e.g.,
conda create -n commonroad-py38 python=3.8 -
Go to cloned root directory and install the package:
- Install the package via poetry:
poetry install - Install the package via pip:
pip install .
- Install the package via poetry:
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.
[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