Decentralized Multi-Robot Cooperative Exploration with Convex Polygon Expansion and Hierarchical Frontier Selection

Cooperative exploration of unknown environments in multi-robot systems poses significant challenges, particularly in terms of efficiency and redundancy. Current approaches primarily rely on centralized systems for target point allocation and the construction of 2D grid maps, which often result in overlapping exploration efforts and reduced efficiency. This paper aims to enhance the cooperative behaviors of decentralized multi-robot systems, enabling effective exploration in large-scale and complex scenarios. We propose a decentralized multi-robot cooperative exploration framework that includes: (1) a trajectory-point extraction strategy for sequentially identifying key navigation points, (2) a dynamic convex polygon expansion method for delineating explored regions among robots, and (3) a novel hierarchical frontier selection mechanism to guide robots toward unexplored areas. By integrating these components, our framework enables coordinated exploration through the sharing of information about explored regions. Experimental results demonstrate that our approach reduces exploration time by 61.43% and overall travel distance by 56.14% compared to recent advancements in multi-robot exploration tasks.