多机器人在受限环境中实现无需全局坐标系的高效协同导航

Multi-robot navigation in unknown, structurally constrained, and GPS-denied environments presents a fundamental trade-off between global strategic foresight and local tactical agility, particularly under limited communication. Centralized methods achieve global optimality but suffer from high communication overhead, while distributed methods are efficient but lack the broader awareness to avoid deadlocks and topological traps. To address this, we propose a fully decentralized, hierarchical relative navigation framework that achieves both strategic foresight and tactical agility without a unified coordinate system. At the strategic layer, robots build and exchange lightweight topological maps upon opportunistic encounters. This process fosters an emergent global awareness, enabling the planning of efficient, trap-avoiding routes at an abstract level. This high-level plan then inspires the tactical layer, which operates on local metric information. Here, a sampling-based escape point strategy resolves dense spatio-temporal conflicts by generating dynamically feasible trajectories in real time, concurrently satisfying tight environmental and kinodynamic constraints. Extensive simulations and real-world experiments demonstrate that our system significantly outperforms in success rate and efficiency, especially in communication-limited environments with complex topological structures.