提出新算法A**，提升动态多智能体路径规划的适应性与质量。
On dynamic multi-agent pathfinding methods: review, simulations and modifications
- 用离线生成多候选路径，线上通过时空规划动态切换
- 在频繁变化环境中路径质量显著优于传统方法
- 适合传感器受限、环境动态的多机器人系统
本文系统研究动态多智能体路径规划（D-MAPF），该场景包含动态障碍物、部分可观测性及智能体间冲突。我们在统一仿真框架中评估六种代表性算法：Dijkstra、D* Lite、Space-Time A*、WHCA*、M* 及一种新方法A**。A**采用模板化思路，将离线几何路径生成与在线时间适应解耦。通过预计算多个多样化候选路径，并利用时空规划动态重连，A**在环境频繁变化且感知受限时显著提升解的质量。
原文摘要 · Abstract (English)
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
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