A Novel Frontier-Based Multi-Robot Cooperative Exploration Method

Abstract

Multi-robot cooperative exploration can reduce the time required for exploration tasks in unknown environments and improve system efficiency. This paper improves two aspects of the frontier-based multi-robot exploration method. Firstly, we propose a two-stage frontier clustering algorithm. First-stage clustering aggregates frontiers into new frontiers based on continuity, significantly reducing their quantity, and then a GriTDBSCAN clustering is performed to effectively remove small and dense frontier tasks, thereby reducing the number of points to be evaluated and improving detection efficiency. Additionally, the number of obstacles between robots and task points is incorporated into the utility value calculation to avoid forming detection islands. The proposed method was evaluated by simulating four contrasting methods in two different environments. The results clearly indicate that the proposed method outperforms the others, yielding the best detection results.