Energy-aware multi-robot exploration and coverage in fragmented unknown environments using collaborative reinforcement learning

Navigating disaster-stricken environments presents significant challenges for autonomous multi-robot exploration, primarily due to extreme topological fragmentation characterized by an abundance of obstacles, narrow passages and numerous dead ends. Traditional multi-robot exploration strategies based on classical frontiers or static region partitioning, prove inadequate in these settings, leading to critical deficiencies in collaborative stability and energy safety, and a drastic reduction in overall exploration efficiency as robots frequently become stalled or disoriented. Therefore, this paper proposes the CERES-Q (Collaborative Energy-aware Reinforcement Exploration System with Q-learning), a novel closed-loop multi-robot exploration system for fragmented post-disaster scenarios that integrates a comprehensive ”perception-decision-planning-execution-return” framework. This system leverages a phase-aware frontier clustering and allocation (CP-AFC) module, a dynamic task transfer (DTT) module, a safe return switch (SRS) with a safety Double Q-learning (SDQ) strategy to ensure reliable and efficient exploration. Comparative experiments on maps with varying degrees of fragmentation show that, compared with traditional multi-robot exploration methods, CERES-Q improves coverage efficiency by about 20% and reduces energy consumption by about 25%; compared with the recent Co-Explore method, it achieves about 10% better performance in highly fragmented environments. The advantages are particularly pronounced in scenarios with narrow passages and multiple dead ends. The results show that the CERES-Q is the key to coping with unknown and fragmented terrain after disasters and achieving reliable exploration and high-quality mapping; at the same time, the modular implementation of the framework facilitates expansion to larger teams and actual deployment.