Multi-robot navigation in cluttered environments presents fundamental challenges in balancing reactive collision avoidance with long-range goal achievement. When navigating through narrow passages or confined spaces, deadlocks frequently emerge that prevent agents from reaching their destinations, particularly when Reinforcement Learning (RL) control policies encounter novel configurations out of learning distribution. Existing RL-based approaches suffer from limited generalization capability in unseen environments. We propose a hybrid framework that seamlessly integrates RL-based reactive navigation with on-demand Multi-Agent Path Finding (MAPF) to explicitly resolve topological deadlocks. Our approach integrates a safety layer that monitors agent progress to detect deadlocks and, when detected, triggers a coordination controller for affected agents. The framework constructs globally feasible trajectories via MAPF and regulates waypoint progression to reduce inter-agent conflicts during navigation. Extensive evaluation on dense multi-agent benchmarks shows that our method boosts task completion from marginal to near-universal success, markedly reducing deadlocks and collisions. When integrated with hierarchical task planning, it enables coordinated navigation for heterogeneous robots, demonstrating that coupling reactive RL navigation with selective MAPF intervention yields a robust, zero-shot performance.

翻译：在杂乱环境中进行多机器人导航面临平衡反应式避障与长程目标达成的基本挑战。当机器人穿过狭窄通道或受限空间时，常出现死锁现象阻碍其抵达目标，尤其当强化学习控制策略遭遇超出学习分布的新场景配置时。现有基于强化学习的方法在未见环境中泛化能力有限。本文提出一种混合框架，将基于强化学习的反应式导航与按需触发的多智能体路径规划无缝集成，以显式解决拓扑死锁问题。该方法集成安全监控层以检测智能体行进停滞状态，一旦识别死锁即触发受影响智能体的协调控制器。该框架通过多智能体路径规划构建全局可行轨迹，并通过航点进度调控减少导航过程中的智能体间冲突。在密集多智能体基准测试中的广泛评估表明，本方法将任务完成率从临界水平提升至接近完全成功，显著减少死锁与碰撞。当与分层任务规划结合时，可实现异构机器人的协调导航，证明反应式强化学习导航与选择性多智能体路径规划干预的耦合能产生鲁棒的零样本性能。