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Multi-robot coverage planning has gained significant attention in recent years. In this letter, we introduce a novel approach called APF-CPP (Artificial Potential Field Based Multi-Robot Online Coverage Path Planning) to enhance the collaboration of multi-robot systems to accomplish coverage tasks in unknown dynamic environments. Our approach presents a unique coverage policy that leverages the concept of artificial potential field (APF). In contrast to the conventional APF-based path planning methods that directly generate paths based on the field gradient, we utilize the APF to derive coverage policies for individual robots within a multi-robot system to achieve efficient task allocation and maintain regular coverage patterns. We have developed a policy update mechanism that allows the system to adapt its task allocation policy based on real-time conditions while minimizing the impact caused by policy changes. To better handle dead-end conditions, we use the APF concept to allocate tasks better during the dead-end recovery process. We also show that our algorithm has a low computational complexity and guarantees complete coverage in a finite time. We conduct extensive comparisons with other state-of-the-art (SOTA) approaches and validate our method through simulations and real-world experiments. The experimental results demonstrate the advantages of our proposed method over existing approaches and confirm the effectiveness and robustness of real-world implementation.
Wang et al. (Tue,) studied this question.