Dynamic Swarm Reconfiguration Via Multi-Level Adaptive Cooperative Architecture for Multi-Robot Exploration

This study proposes a hierarchical cooperative control architecture for multi-robot exploration, centered on a dynamic branching–integration mechanism that adapts swarm structures to environmental conditions. The architecture inte- grates a System Agent for global coordination and a Swarm Agent for local action decisions, enabling adaptive and scalable cooperation. Simulations in multiple environments show that the branch- ing–integration mechanism alone surpasses the baseline in all cases, with gains in complex settings. Learning-based opti- mization of branching–integration conditions further improves early-stage exploration speed and robustness. These results indicate that dynamic swarm reconfiguration is the primary driver of exploration efficiency, while learning effectively enhances its benefits. Future work will investigate other reinforcement learning algorithms and exploration strate- gies, evaluate performance under dynamic or communication- constrained conditions, and conduct field tests with real robots.