Deep distributed optimization algorithm enhances energy consumption and path length in AUV clusters, indicating improved performance in complex underwater environments.
Aiming at the cooperative path planning problem of AUV cluster in underwater complex environment, this paper proposes a deep distributed optimization algorithm (DNPFS-OA) based on the intelligent behavior of Tanzhou Pseudosciaena crocea. This algorithm is different from the traditional leader-follower architecture. By establishing the deep coupling between the bio-pressure sensing model and the hydrodynamic equation, and combining with the communication-control collaborative optimization framework, the autonomous collaboration of AUV clusters in the three-dimensional strong disturbance environment is realized. The sea test shows that the path length of DNPFS-OA is reduced by 28.3%, the energy consumption is reduced by 35.7%, and the mapping efficiency is improved by 42.6% when the communication packet loss rate is 30%.
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Liu et al. (2025) studied this question.
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