A Distributed Multi-Agent Dynamic Area Coverage Algorithm Based on Reinforcement Learning

Abstract

Dynamic area coverage is widely used in military and civil fields. Improving coverage efficiency is an important research direction for multi-agent dynamic area coverage. In this paper, we focus on the non-optimal coverage problem of free dynamic area coverage algorithms. We propose a distributed dynamic area coverage algorithm based on reinforcement learning and a $\gamma $ -information map. The $\gamma $ -information map can transform the continuous dynamic coverage process into a discrete $\gamma $ point traversal process, while ensuring no-hole coverage. When agent communication covers the whole target area, agents can obtain the global optimal coverage strategy by learning the whole dynamic coverage process. In the event that communication does not cover the whole target area, agents can obtain a local optimal coverage strategy; in addition, agents can use the proposed algorithm to obtain a global optimal coverage path through off-line planning. Simulation results demonstrate that the required time for area coverage with the proposed algorithm is close to the optimal value, and the performance of the proposed algorithm is significantly better than the distributed anti-flocking Algorithms for dynamic area coverage.