We present a multi-robot motion planning algorithm that efficiently finds paths for robot teams up to ten times larger than existing methods in congested settings with narrow passages in the environment. Narrow passages represent a source of difficulty for sampling-based motion planning algorithms. This problem is exacerbated for multi-robot systems where the planner must also avoid inter-robot collisions within these congested spaces, requiring coordination. Topological guidance, which leverages information about the robot's environment, has been shown to improve performance for mobile robot motion planning in single robot scenarios with narrow passages. Additionally, our prior work has explored using topological guidance in multi-robot settings where a high degree of coordination is required of the full robot group. This high level of coordination, however, is not always necessary and results in excessive computational overhead for large groups. Here, we propose a novel multi-robot motion planning method that leverages topological guidance to inform the planner when coordination between robots is necessary, leading to a significant improvement in scalability.

翻译：我们提出了一种多机器人运动规划算法，该算法能够在存在狭窄通道的拥挤环境中，高效地为比现有方法规模大十倍以上的机器人团队找到路径。狭窄通道对基于采样的运动规划算法构成难点，这一问题在多机器人系统中尤为突出，因为规划器还必须避免这些拥挤空间内的机器人间碰撞，从而需要协调。拓扑引导利用机器人环境的信息，已被证明能够提升单机器人场景中狭窄通道下的移动机器人运动规划性能。此外，我们先前的工作探索了在需要整个机器人群体高度协调的多机器人场景中使用拓扑引导。然而，这种高度协调并非总是必要，且会为大型群体带来过高的计算开销。本文提出了一种新型多机器人运动规划方法，该方法利用拓扑引导来告知规划器何时需要机器人之间的协调，从而显著提升了可扩展性。