Online, Reachability-Aware, Sampling-Based Motion Planning

Abstract:Sampling-Based Model-Predictive Control (MPC) algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. Historically, such approaches have lacked hard safety guarantees, a shortcoming which we remedy in this work by computing guaranteed reachable-set overapproximations online with a fast, interval-based pipeline. We show that our method achieves similar performance to a state-of-the-art reachability-based planner without the need for the expensive pre-computation step, and can be scaled to systems that are infeasible using existing approaches. Finally, we demonstrate that our technique reduces safety violations by over 99% in a racing simulation and successfully controls a model racecar on real hardware experiments without crashes.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2609.09073 [cs.RO]

(or arXiv:2609.09073v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.09073 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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