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：我们提出一种新型算法，称为同步采样搜索规划（SSSP），用于快速求解多机器人运动规划问题。传统MRMP研究多采用两阶段规划范式：先构建路径图，再在该图上寻找机器人间无碰撞路径。与此不同，SSSP同步执行路径图构建与无碰撞路径搜索。该算法通过融合单机器人基于采样的运动规划技术以及离散化空间中的多智能体路径搜索技术实现这一目标。这种方法能够构建较小的搜索空间，从而实现快速MRMP。若解存在，SSSP确保最终能够找到可行解。在多种场景下的实证评估表明，SSSP的性能显著优于标准MRMP方法，能以更快的速度解决更多问题实例。我们还将SSSP应用于32台地面机器人在密集环境下的规划任务。