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 trajectory planner from PyPi, please run:

pip install commonroad-reactive-planner

Requirements

The software is developed and tested on recent versions of Linux. The required python dependencies are listed in requirements.txt.

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-py37 python=3.7

2. Install the package:

   • Install the package via pip: pip install .
   • Or install the dependencies with pip install -r requirements.txt and add the root folder to the python path of your interpreter

How to run

Main example script run_planner.py: The example script shows how to run the planner on an exemplary CommonRoad scenario. Therein, the following steps are included:

• creating a planner configuration
• instantiating the reactive planner
• running the planner in planning cycles with a fixed re-planning frequency

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