混合路径规划框架提升动态环境多智能体导航成功率与效率
Cooperative Hybrid Multi-Agent Pathfinding Based on Shared Exploration Maps
- 结合D* Lite全局搜索与强化学习，动态切换策略应对环境变化
- 在POGEMA中成功率达94.3%，碰撞率降低62%，路径效率提升58%
- 适合大规模机器人部署与频繁变动的复杂场景应用
多智能体路径规划广泛应用于多机器人编队、仓储物流和智能车辆等领域。然而，在环境不完整或频繁变化的场景下，传统集中式规划或纯强化学习难以兼顾全局解质量与局部灵活性。本文提出一种融合D* Lite全局搜索与多智能体强化学习的混合框架，通过切换机制和防冻结策略应对动态条件与高密度场景。在离散的POGEMA环境中评估，结果表明该框架显著提升成功率、降低碰撞率并提高路径效率。模型进一步在EyeSim平台验证，在频繁变化及大规模机器人部署下仍能保持可行路径规划。
原文摘要 · Abstract (English)
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.
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