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.

翻译：针对高自由度机械臂在共享、受限及狭窄空间中的多机器人运动规划是一个复杂问题，对建筑、手术等诸多场景至关重要。传统的耦合方法直接在复合构型空间中规划，其可扩展性较差；而解耦方法虽对各机器人分别规划，却缺乏完备性。现有混合方法需要从各机器人获取多条路径，并枚举大量路径组合才能找到有效的复合解。本文提出避碰调度方法，这是一种混合规划方法，通过调度（在所有路径中添加停顿点并协调运动）更有效地组合各机器人的路径，并利用调度器与运动规划器之间的双向反馈进行启发式采样，从而生成高可行性的路径。在机械臂场景的挑战性问题上，相比当前最先进的混合基线方法，本方法所需底层规划器生成的路径数量减少10至100倍。