论文标题
拓扑指导的拥塞环境可扩展的多机器人运动计划
Scalable Multi-robot Motion Planning for Congested Environments With Topological Guidance
论文作者
论文摘要
多机器人运动计划（MRMP）是在连续状态空间中找到一组机器人的无碰撞路径的问题。 MRMP的难度随着机器人的数量而增加，并且在机器人必须通过的狭窄通道的环境中会加剧，例如需要在机器人之间协调的仓库过道。在单机器人设置中，拓扑引导的运动计划方法在这些狭窄的环境中显示出改善的性能。在这项工作中，我们将现有的拓扑指导的单机器人运动计划方法扩展到多机器人域，以利用拓扑指南提供的提高效率。我们证明了我们的方法在许多狭窄段落的复杂环境中有效计划路径的能力，将大小的机器人团队扩展到该类别问题中现有方法的25倍。通过利用对环境拓扑的知识，我们还发现了比其他方法更高的解决方案。
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.
