Abstract

Abstract: Sampling-based motion planning is a key tool for several autonomous systems ranging from autonomous driving to service and intralogistic robotics. Over the past decades, several algorithms, extend functions and post-smoothing techniques have been introduced for such systems. Choosing the best combination of such components for an autonomous system's application is a tedious exercise, even for expert users. With the aim of helping researchers and practitioners in efficiently solving this issue, we have recently presented 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. In this workshop paper, we complement our previous publication, by providing several examples on how to use it, together with the details on the framework architecture and components.

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