Multi-robot motion planning (MRMP) is the problem of finding collision-free paths for a set of robots in a continuous state space. The difficulty of MRMP increases with the number of robots and is exacerbated in environments with narrow passages that robots must pass through, like warehouse aisles where coordination between robots is required. In single-robot settings, topology-guided motion planning methods have shown improved performance in these constricted environments. In this work, we extend an existing topology-guided single-robot motion planning method to the multi-robot domain to leverage the improved efficiency provided by topological guidance. We demonstrate our method's ability to efficiently plan paths in complex environments with many narrow passages, scaling to robot teams of size up to 25 times larger than existing methods in this class of problems. By leveraging knowledge of the topology of the environment, we also find higher-quality solutions than other methods.

翻译：多机器人运动规划（MRMP）是在连续状态空间中为多个机器人寻找无碰撞路径的问题。随着机器人数量增加，该问题的难度随之上升，并且在机器人必须穿过的狭窄通道环境中（例如需要机器人之间协调的仓库通道）会进一步加剧。在单机器人场景下，拓扑引导的运动规划方法在这些受限环境中展现出更优的性能。本研究将现有的拓扑引导单机器人运动规划方法扩展至多机器人领域，以利用拓扑引导带来的效率提升。我们证明了该方法能在具有大量狭窄通道的复杂环境中高效规划路径，在此类问题中可扩展至比现有方法大25倍的机器人团队规模。通过利用环境拓扑知识，我们还发现了比其它方法更高质量的解决方案。