Generally, the calculation and memory space required for multi-agent path finding (MAPF) grows exponentially as the number of agents increases. This often results in some MAPF instances being unsolvable under limited computational resources and memory space, thereby limiting the application of MAPF in complex scenarios. Hence, we propose a decomposition approach for MAPF instances, which breaks down instances involving a large number of agents into multiple isolated subproblems involving fewer agents. Moreover, we present a framework to enable general MAPF algorithms to solve each subproblem independently and merge their solutions into one conflict-free final solution, without compromising on solvability. Unlike existing works that propose isolated methods aimed at reducing the time cost of MAPF, our method is applicable to all MAPF methods. In our results, we apply decomposition to multiple state-of-the-art MAPF methods using a classic MAPF benchmark (https://movingai.com/benchmarks/mapf.html). The decomposition of MAPF instances is completed on average within 1s, and its application to seven MAPF methods reduces the memory usage and time cost significantly, particularly for serial methods. To facilitate further research within the community, we have made the source code of the proposed algorithm publicly available (https://github.com/JoeYao-bit/LayeredMAPF).

翻译：通常，多智能体路径规划（MAPF）所需的计算量和存储空间会随智能体数量增加而呈指数增长。这导致在有限计算资源和存储空间下，部分MAPF实例无法求解，进而限制了MAPF在复杂场景中的应用。为此，我们提出一种MAPF实例分解方法，该方法可将包含大量智能体的实例分解为多个包含较少智能体的独立子问题。同时，我们提出一个通用框架，使常规MAPF算法能够独立求解每个子问题，并将子问题的解合并为无冲突的最终解，且不影响可解性。与现有旨在降低MAPF时间成本的孤立方法不同，我们的方法适用于所有MAPF算法。在实验结果中，我们使用经典MAPF基准测试（https://movingai.com/benchmarks/mapf.html）对多种最先进的MAPF方法进行分解。MAPF实例分解平均在1秒内完成，将其应用于七种MAPF方法后，内存占用和时间成本均显著降低，尤其在串行方法中效果更为明显。为促进社区进一步研究，我们已将所提算法的源代码公开（https://github.com/JoeYao-bit/LayeredMAPF）。