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.

翻译：多机器人系统在从制造业到监控的各类应用中提升了效率与生产力。尽管单机器人运动规划通过利用先前解决方案数据库已得到改进，但将此方法扩展到多机器人运动规划（MRMP）时，由于任务与配置的复杂性和多样性增加而面临挑战。近期的离散方法尝试通过关注相关的低维子问题来解决此问题，但这些方法对于涉及机械臂机器人等复杂场景仍显不足。为克服这一局限，我们提出一种创新方法，通过构建并利用针对更小子问题的解决方案数据库来实现基于经验的规划。通过聚焦于较少机器人间的交互作用，我们的方法降低了对数据库穷举式扩展的需求，从而能够高效处理更复杂的MRMP场景。我们通过包含移动机器人和机械臂机器人的实验验证了所提方法，证明其在可扩展性和规划效率方面相较现有方法有显著提升。我们的贡献包括：针对低维MRMP问题的快速构建数据库、将这些解决方案应用于更大规模问题的框架，以及针对多达32个移动机器人和16个机械臂机器人的实验验证。