This paper presents a systematic study of pathfinding algorithms in the context of Dynamic Multi-Agent Pathfinding (D-MAPF), a setting that combines dynamic obstacles, partial observability, and inter-agent conflicts. We evaluate six representative algorithms: Dijkstra, D* Lite, Space-Time A*, WHCA*, M*, and a novel method denoted as A** within a unified simulation framework. The proposed A** algorithm introduces a template-based approach that decouples offline geometric path generation from online temporal adaptation. By precomputing multiple diverse candidate paths and dynamically reconnecting to them using space-time planning, A** improves solution quality in environments with frequent changes and limited sensing

翻译：本文系统研究了动态多智能体路径规划（D-MAPF）背景下的路径搜索算法，该设定融合了动态障碍物、部分可观测性以及多智能体间的冲突。我们在统一的仿真框架内评估了六种代表性算法：Dijkstra、D* Lite、Space-Time A*、WHCA*、M*，以及一种被命名为A**的新方法。所提出的A**算法引入了一种基于模版的方法，将离线几何路径生成与在线时间自适应解耦。通过预计算多条多样化候选路径，并利用时空规划动态重连至这些路径，A**在频繁变化和有限感知的环境中提升了求解质量。