Multi-agent path finding (MAPF) involves planning efficient paths for multiple agents to move simultaneously while avoiding collisions. In typical warehouse environments, agents are often sparsely distributed along aisles; however, increasing the agent density can improve space efficiency. When the agent density is high, it becomes necessary to optimize the paths not only for goal-assigned agents but also for those obstructing them. This study proposes a novel MAPF framework for high-density environments (MAPF-HD). Several studies have explored MAPF in similar settings using integer linear programming (ILP). However, ILP-based methods require substantial computation time to optimize all agent paths simultaneously. Even in small grid-based environments with fewer than $100$ cells, these computations can take tens to hundreds of seconds. Such high computational costs render these methods impractical for large-scale applications such as automated warehouses and valet parking. To address these limitations, we introduce the phased null-agent swapping (PHANS) method. PHANS employs a heuristic approach to incrementally swap positions between agents and empty vertices. This method solves the MAPF-HD problem within a few seconds, even in large environments containing more than $700$ cells. The proposed method has the potential to improve efficiency in various real-world applications such as warehouse logistics, traffic management, and crowd control. The implementation is available at https://github.com/ToyotaCRDL/MAPF-in-High-Density-Envs.

翻译：多智能体路径规划（MAPF）旨在为多个智能体同时规划高效路径并避免碰撞。在典型的仓库环境中，智能体通常稀疏分布在通道中；然而，提高智能体密度可以提升空间利用效率。当智能体密度较高时，不仅需要为目标分配的智能体优化路径，还需为阻碍其通行的其他智能体进行路径优化。本研究提出了一种适用于高密度环境的新型MAPF框架（MAPF-HD）。已有若干研究使用整数线性规划（ILP）探索类似场景下的MAPF问题，但基于ILP的方法需要大量计算时间以同时优化所有智能体路径。即使在少于$100$个单元格的小型网格环境中，此类计算也可能耗时数十至数百秒。如此高的计算成本使得这些方法难以应用于自动化仓库和代客泊车等大规模场景。为应对这些局限，我们提出了分阶段空智能体交换（PHANS）方法。PHANS采用启发式方法，逐步交换智能体与空顶点之间的位置。该方法可在数秒内解决MAPF-HD问题，即使在包含超过$700$个单元格的大规模环境中亦然。所提方法有望提升仓库物流、交通管理和人群控制等多种实际应用的效率。实现代码发布于https://github.com/ToyotaCRDL/MAPF-in-High-Density-Envs。