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We propose a novel algorithm to solve multi-robot motion planning (MRMP)rapidly, called Simultaneous Sampling-and-Search Planning (SSSP). ConventionalMRMP studies mostly take the form of two-phase planning that constructsroadmaps and then finds inter-robot collision-free paths on those roadmaps. Incontrast, SSSP simultaneously performs roadmap construction and collision-freepathfinding. This is realized by uniting techniques of single-robotsampling-based motion planning and search techniques of multi-agent pathfindingon discretized spaces. Doing so builds the small search space, leading to quickMRMP. SSSP ensures finding a solution eventually if exists. Our empiricalevaluations in various scenarios demonstrate that SSSP significantlyoutperforms standard approaches to MRMP, i.e., solving more problem instancesmuch faster. We also applied SSSP to planning for 32 ground robots in a densesituation.

Quick Multi-Robot Motion Planning by Combining Sampling and Search