提出StAC方法，高效解决高自由度机械臂在狭窄空间的多机器人避碰路径规划问题。
Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation
- 通过调度机制协调各机械臂路径，结合双向反馈优化采样策略。
- 相比顶尖混合方法，低层规划生成路径数量减少10至100倍。
- 适用于手术、建筑等高精度协同作业场景，适合复杂约束环境下的多臂系统。
在共享、受限且狭窄的空间中，对高自由度机械臂进行多机器人运动规划是一项复杂但至关重要的任务，广泛应用于建筑、手术等领域。传统耦合方法直接在复合配置空间中规划，扩展性差；解耦方法虽独立规划，但缺乏完备性；混合方法需枚举大量路径才能获得有效复合解。本文提出调度避碰（StAC）方法，通过调度（添加停顿与协调动作）并行生成各机器人路径，利用调度器与运动规划器间的双向反馈实现有信息的采样，显著提升路径可行性。在复杂机械臂任务中，该方法所需低层规划路径数量仅为当前最优混合基线的1/10至1/100。
原文摘要 · Abstract (English)
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.
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