多机器人多目标路径规划新方法，兼顾效率与全局最优。
Sampling-Based Multi-Modal Multi-Robot Multi-Goal Path Planning
- 将多机器人路径规划统一为单一复合空间中的采样规划问题
- 在多种场景下实现概率完备性，且渐近最优
- 适合需要协同任务（如交接）的复杂多机器人系统
在多个机器人共享工作空间完成任务的应用中，如何高效规划路径是关键挑战。本文将此类问题建模为多模式、多机器人、多目标路径规划问题，提出一种集中式采样规划方法，能够处理不同机器人、不同规划时长及协作任务（如手递手交接）。该方法基于标准采样算法改进，具备概率完备性和渐近最优性，不依赖优先级或同步假设。我们在多种复杂场景中验证了其有效性，并与次优的优先级规划器进行对比。相关视频与代码已公开于 https://vhartmann.com/mrmg-planning/。
原文摘要 · Abstract (English)
In many robotics applications, multiple robots are working in a shared workspace to complete a set of tasks as fast as possible. Such settings can be treated as multi-modal multi-robot multi-goal path planning problems, where each robot has to reach a set of goals. Existing approaches to this type of problem solve this using prioritization or assume synchronous task completion, and are thus neither optimal nor complete. We formalize this problem as a single centralized path planning problem and present planners that are probabilistically complete and asymptotically optimal. The planners plan in the composite space of all robots and are modifications of standard sampling-based planners with the required changes to work in our multi-modal, multi-robot, multi-goal setting. We validate the planners on a diverse range of problems including scenarios with various robots, planning horizons, and collaborative tasks such as handovers, and compare the planners against a suboptimal prioritized planner. Videos and code for the planners and the benchmark is available at https://vhartmann.com/mrmg-planning/.
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