Scalable Multi-Robot Motion Planning Using Guidance-Informed Hypergraphs (2311.10176v3)

Abstract: In this work, we propose a method for multiple mobile robot motion planning that efficiently plans for robot teams up to an order of magnitude larger than existing state-of-the-art methods in congested settings with narrow passages in the environment. We achieve this improvement in scalability by adapting the state-of-the-art Decomposable State Space Hypergraph (DaSH) planning framework to expand the set of problems it can support to include those without a highly structured planning space and those with kinodynamic constraints. We accomplish this by exploiting guidance about a problem's structure to limit exploration of the planning space and through modifying DaSH's conflict resolution scheme. This guidance captures when coordination between robots is necessary, allowing us to decompose the intractably large multi-robot search space while limiting risk of inter-robot conflicts by composing relevant robot groups together while planning.