We propose a novel algorithm to solve multi-robot motion planning (MRMP) rapidly, called Simultaneous Sampling-and-Search Planning (SSSP). Conventional MRMP studies mostly take the form of two-phase planning that constructs roadmaps and then finds inter-robot collision-free paths on those roadmaps. In contrast, SSSP simultaneously performs roadmap construction and collision-free pathfinding. This is realized by uniting techniques of single-robot sampling-based motion planning and search techniques of multi-agent pathfinding on discretized spaces. Doing so builds the small search space, leading to quick MRMP. SSSP ensures finding a solution eventually if exists. Our empirical evaluations in various scenarios demonstrate that SSSP significantly outperforms standard approaches to MRMP, i.e., solving more problem instances much faster. We also applied SSSP to planning for 32 ground robots in a dense situation.

翻译：我们提出了一种名为同步采样-搜索规划(SSSP)的新型算法，用于快速求解多机器人运动规划(MRMP)问题。传统MRMP研究大多采用两阶段规划形式，即先构建路标图再在这些路标图上寻找机器人间无碰撞路径。相比之下，SSSP同步执行路标图构建与无碰撞路径搜索。这一目标通过融合单机器人基于采样的运动规划技术与离散化空间中的多智能体路径搜索技术得以实现。该方法构建了较小的搜索空间，从而实现了快速MRMP。SSSP保证在存在解的情况下最终能求得解。我们在多种场景下的实验评估表明，SSSP显著优于标准MRMP方法，即能更快速地解决更多问题实例。我们还将SSSP应用于密集场景中32台地面机器人的规划任务。