用历史经验解决多机器人运动规划难题，提升复杂场景下的效率。
Experience-based Subproblem Planning for Multi-Robot Motion Planning
- 基于过往解决方案构建小规模子问题数据库，聚焦少机器人交互。
- 可处理最多32个移动机器人或16个机械臂，规划效率显著提升。
- 适合需要高效多机器人协同的工业、物流等复杂场景。
多机器人系统在制造、监控等领域显著提升效率与生产力。尽管单机器人运动规划已通过调用历史解数据库得到改进，但将此方法扩展至多机器人运动规划（MRMP）面临任务与配置多样性的挑战。现有离散方法虽尝试聚焦低维子问题，但在涉及机械臂等复杂场景时仍显不足。为此，我们提出一种基于经验的规划新方法：构建并利用小规模子问题的解数据库，通过关注较少机器人的交互，减少对全量数据库的需求，从而更高效地应对复杂MRMP场景。实验验证了该方法在移动机器人和机械臂系统中的有效性，相比现有方法在可扩展性与规划效率上均有显著提升。贡献包括快速构建的低维MRMP解库、将小规模解应用于大规模问题的框架，以及包含最多32个移动机器人和16个机械臂的实证验证。
原文摘要 · Abstract (English)
Multi-robot systems enhance efficiency and productivity across various applications, from manufacturing to surveillance. While single-robot motion planning has improved by using databases of prior solutions, extending this approach to multi-robot motion planning (MRMP) presents challenges due to the increased complexity and diversity of tasks and configurations. Recent discrete methods have attempted to address this by focusing on relevant lower-dimensional subproblems, but they are inadequate for complex scenarios like those involving manipulator robots. To overcome this, we propose a novel approach that %leverages experience-based planning by constructs and utilizes databases of solutions for smaller sub-problems. By focusing on interactions between fewer robots, our method reduces the need for exhaustive database growth, allowing for efficient handling of more complex MRMP scenarios. We validate our approach with experiments involving both mobile and manipulator robots, demonstrating significant improvements over existing methods in scalability and planning efficiency. Our contributions include a rapidly constructed database for low-dimensional MRMP problems, a framework for applying these solutions to larger problems, and experimental validation with up to 32 mobile and 16 manipulator robots.
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