A Multi-robot Lunar Area Coverage Method Based on Deep Reinforcement Learning

When exploring celestial bodies like the Moon, lunar surface coverage becomes a critical task, necessitating multiple robots to cover as much area as possible on the lunar surface. However, the intricate lunar terrain and environmental uncertainties render traditional area coverage methods ineffective. Therefore, this paper proposes a multi-robot lunar surface area coverage approach based on deep reinforcement learning (DRL). The approach consists of two phases: the training phase and the execution phase. During the training phase, robots learn a strategy to maximize lunar surface area coverage, employing the multi-agent deep deterministic policy gradient (MADDPG) algorithm for deep reinforcement learning. In the execution phase, robots execute movements based on their current state and learned strategy to achieve lunar surface coverage. Furthermore, a series of simulation experiments were conducted, with coverage rate during the execution phase serving as the evaluation metric. Experimental results demonstrate that our approach significantly enhances lunar surface area coverage compared to traditional methods. In the future, we will further optimize the algorithm to accomplish more efficient and intelligent lunar exploration missions.