A Multi-AUV Cooperative Search Scheme Based on Acoustic-optical Communication and Deep Reinforcement Learning

In complex underwater environments, multiple autonomous underwater vehicles (AUVs) typically use underwater acoustic communication to collaboratively search for unknown targets. However, traditional underwater acoustic communication suffers from high latency and low bandwidth issues. In contrast, optical communication provides higher bandwidth and lower latency, but its propagation distance is limited, which seriously affects the efficiency of AUV search. To address this challenge, this paper proposes a multi-AUV acoustic-optical multimodal communication target search scheme based on deep reinforcement learning (DRL). Firstly, the AUV search problem under communication constraints is analyzed, aiming to maximize search success rate, minimize search time, and maximize effective search area per unit time. Secondly, this problem is modeled as a distributed partially observable Markov decision process (Dec-POMDP), and then a multi-AUV acoustic-optical multimodal search algorithm (AOSA) is designed based on the multi-agent deep deterministic policy gradient (MADDPG) method. The AOSA algorithm enhances information sharing among AUVs through acoustic-optical multimodal communication, promoting accurate updating of probability maps. Additionally, by incorporating pheromones to correction probability maps, it enables AUVs to effectively adjust their search paths, thereby improving the efficiency of collaborative search. The numerical simulation results verify the efficiency of this scheme.