Eric Heiden, Luigi Palmieri, Leonard Bruns, Leonard, Kai O. Arras, Gaurav S. Sukhatme, and Sven Koenig. Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots. IEEE Robotics and Automation Letters. 2021 Mar 24;6(3):4536-4543.

Abstract

Planning smooth and energy-efficient paths for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics. Over the past decades, several sampling-based motion-planning algorithms, extend functions and post-smoothing algorithms have been introduced for such motion-planning systems. Choosing the best combination of components for an application is a tedious exercise, even for expert users. We therefore present Bench-MR, the first open-source motion-planning benchmarking framework designed for sampling-based motion planning for nonholonomic, wheeled mobile robots. Unlike related software suites, Bench-MR is an easy-to-use and comprehensive benchmarking framework that provides a large variety of sampling-based motion-planning algorithms, extend functions, collision checkers, post-smoothing algorithms and optimization criteria. It aids practitioners and researchers in designing, testing, and evaluating motion-planning systems, and comparing them against the state of the art on complex navigation scenarios through many performance metrics. Through several experiments, we demonstrate how Bench-MR can be used to gain extensive insights from the benchmarking results it generates.

@ARTICLE{HeidenPalmieriRAL2021,
author={Heiden, Eric and Palmieri, Luigi and Bruns, Leonard and Arras, Kai O. and Sukhatme, Gaurav S. and Koenig, Sven},
journal={IEEE Robotics and Automation Letters}, 
title={Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots}, 
year={2021},
volume={6},
number={3},
pages={4536-4543},
doi={10.1109/LRA.2021.3068913}}