🤖 AI Summary
This work addresses the large-scale multi-robot motion planning problem in high-density, narrow-passage environments—scaling to ~100 robots, a tenfold increase over state-of-the-art (SOTA) methods. We propose a structure-guided hypergraph coordination planning framework. Our key contributions are: (1) the first integration of structure-guidance into the Decomposed and Structured Hypergraph (DaSH) state-space framework, enabling support for unstructured environments and kinematic constraints; (2) a coordination-necessity-driven dynamic grouping strategy that enables conflict-aware, on-demand coordination; and (3) structure-aware spatial pruning and efficient conflict resolution mechanisms. Evaluated in complex narrow-passage scenarios, our method achieves a 10× speedup in planning time and reduces collision incidence by 42%, significantly outperforming existing SOTA approaches.
📝 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.
