Coverage exploration of unknown obstacle-cluttered environments using a swarm of ground robots

This paper introduces a coverage exploration algorithm for unknown obstacle-cluttered environments using a swarm of ground robots. A key contribution of this work is the proposed fitness function, which balances multiple exploration objectives and encourages robots to disperse effectively, avoiding excessive overlapping visits. The robots are assumed to start from a single corner of the environment, reflecting practical situations where pre-distributing them is not feasible. This setup highlights a key feature of the algorithm, as it enables self-organization and effective distribution of the robots throughout the environment. The robustness of the method is demonstrated through experiments in various environmental setups, showing its resilience to different obstacle structures and reliable performance across diverse scenarios. The approach also leverages the benefits of swarm behavior, where an increasing number of robots improves exploration efficiency through enhanced collaboration and coverage. The algorithm is evaluated against a swarm random walk approach and two multi-robot meta-heuristic methods, significantly outperforming both in terms of coverage efficiency and robustness. • Novel fitness-based swarm exploration algorithm for unknown environments. • Achieves reliable full coverage under obstacles and communication limits. • Outperforms Chaotic DF, Levy DF, and CME-SSA in coverage efficiency. • Scales with swarm size while reducing redundancy in robot trajectories. • Potential applications in search, rescue, and environmental monitoring.