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/.

翻译：在许多机器人应用中，多个机器人在共享工作空间内协同工作，以尽可能快地完成一组任务。此类场景可建模为多模态多机器人多目标路径规划问题，其中每台机器人需要到达一组目标点。现有方法通过优先级排序或假设同步任务完成来求解该问题，因此既非最优也不完备。我们将该问题形式化为单一集中式路径规划问题，并提出具有概率完备性和渐近最优性的规划器。这些规划器在所有机器人构成的复合空间中进行规划，是对标准基于采样的规划器的改进，通过必要的调整以适用于我们的多模态、多机器人、多目标场景。我们在涵盖多种机器人类型、规划时间跨度和协作任务（如物体交接）的多样化问题上验证了规划器的性能，并将其与次优的优先级规划器进行了对比。相关视频、代码及基准测试结果可访问 https://vhartmann.com/mrmg-planning/ 获取。