Multi-Agent Pathfinding is used in areas including multi-robot formations, warehouse logistics, and intelligent vehicles. However, many environments are incomplete or frequently change, making it difficult for standard centralized planning or pure reinforcement learning to maintain both global solution quality and local flexibility. This paper introduces a hybrid framework that integrates D* Lite global search with multi-agent reinforcement learning, using a switching mechanism and a freeze-prevention strategy to handle dynamic conditions and crowded settings. We evaluate the framework in the discrete POGEMA environment and compare it with baseline methods. Experimental outcomes indicate that the proposed framework substantially improves success rate, collision rate, and path efficiency. The model is further tested on the EyeSim platform, where it maintains feasible Pathfinding under frequent changes and large-scale robot deployments.

翻译：多智能体路径规划技术广泛应用于多机器人编队、仓储物流及智能车辆等领域。然而，许多环境具有不完全性或频繁动态变化的特性，使得传统的集中式规划方法或纯强化学习方法难以同时保证全局解的质量与局部灵活性。本文提出一种混合框架，该框架将D* Lite全局搜索与多智能体强化学习相结合，通过切换机制与防冻结策略来处理动态环境与拥挤场景。我们在离散POGEMA环境中对该框架进行评估，并与基准方法进行对比。实验结果表明，所提出的框架在成功率、碰撞率及路径效率方面均有显著提升。该模型进一步在EyeSim平台上进行测试，结果表明在频繁环境变化与大规模机器人部署场景下，该模型仍能保持可行的路径规划能力。