We study the iterative refinement of path planning for multiple robots, known as multi-agent pathfinding (MAPF). Given a graph, agents, their initial locations, and destinations, a solution of MAPF is a set of paths without collisions. Iterative refinement for MAPF is desirable for three reasons: 1) optimization is intractable, 2) sub-optimal solutions can be obtained instantly, and 3) it is anytime planning, desired in online scenarios where time for deliberation is limited. Despite the high demand, this is under-explored in MAPF because finding good neighborhoods has been unclear so far. Our proposal uses a sub-optimal MAPF solver to obtain an initial solution quickly, then iterates the two procedures: 1) select a subset of agents, 2) use an optimal MAPF solver to refine paths of selected agents while keeping other paths unchanged. Since the optimal solvers are used on small instances of the problem, this scheme yields efficient-enough solutions rapidly while providing high scalability. We also present reasonable candidates on how to select a subset of agents. Evaluations in various scenarios show that the proposal is promising; the convergence is fast, scalable, with reasonable quality, and practical because it can be interrupted whenever the solution is needed.

中文翻译：

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实时多机器人路径规划的迭代优化

我们研究了多机器人路径规划的迭代优化，称为多主体路径查找（MAPF）。给定一个图，代理，它们的初始位置和目的地，MAPF的解决方案是一组没有冲突的路径。出于以下三个原因，需要对MAPF进行迭代优化：1）优化难以解决； 2）可以立即获得次优解决方案； 3）随时准备进行计划，这是审议时间有限的在线方案的理想选择。尽管需求量很大，但MAPF对此尚未进行充分的探索，因为到目前为止，尚不清楚如何找到合适的社区。我们的建议使用次优MAPF求解器快速获取初始解，然后重复执行两个过程：1）选择代理的子集； 2）使用最佳MAPF求解器精炼所选代理的路径，同时保持其他路径不变。由于最佳求解器用于解决问题的小实例，因此该方案可快速提供足够有效的解决方案，同时提供较高的可扩展性。我们还介绍了有关如何选择代理子集的合理人选。在各种情况下的评估表明，该提案是有希望的；融合是快速的，可扩展的，具有合理的质量和实用的，因为只要需要解决方案，它都可以中断。