高效多机器人运动规划：通过随机调度和知情路径生成实现 manifold 约束操作臂的优化
Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation
AI总结：
本文提出StAC方法，通过随机调度和双向反馈实现高效多机器人运动规划，减少路径生成数量，提升在受限空间中的操作效率。
摘要：
多机器人在共享、受限和狭窄空间中进行高自由度操作臂的运动规划是一个复杂的问题，对于建筑、手术等许多场景至关重要。传统耦合方法直接在复合配置空间中规划，扩展性差；解耦方法则分别对每个机器人进行规划，但缺乏完整性。混合方法需要枚举许多路径才能找到有效的复合解决方案。本文介绍了一种名为调度以避免碰撞（StAC）的混合方法，通过调度（在所有路径上添加停止和协调运动）更有效地组合来自单个机器人的路径，并利用调度器与运动规划器之间的双向反馈生成可能可行的路径。StAC在操作臂案例中的挑战性问题中，比最先进的混合基线少10到100倍地使用低层规划器生成的路径。
英文摘要：
Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construction, surgery, and more. Traditional coupled methods plan directly in the composite configuration space, which scales poorly; decoupled methods, on the other hand, plan separately for each robot but lack completeness. Hybrid methods that obtain paths from individual robots together require the enumeration of many paths before they can find valid composite solutions. This paper introduces Scheduling to Avoid Collisions (StAC), a hybrid approach that more effectively composes paths from individual robots by scheduling (adding stops and coordination motion along all paths) and generates paths that are likely to be feasible by using bidirectional feedback between the scheduler and motion planner for informed sampling. StAC uses 10 to 100 times fewer paths from the low-level planner than state-of-the-art hybrid baselines on challenging problems in manipulator cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
