System-Level Optimization of AUV Swarm Control and Perception: An Energy-Aware Federated Meta-Transfer Learning Framework with Digital Twin Validation

Deep-sea exploration increasingly relies on Autonomous Underwater Vehicles (AUVs) to enable persistent, wide-area surveying in harsh and uncertain environments. In practice, however, deployments are constrained by tight energy budgets and bandwidth-limited, intermittent acoustic links, which complic...

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Bibliographic Details
Published in:	Journal of marine science and engineering 2026-02, Vol.14 (4), p.384
Main Authors:	Nie, Zinan, Tian, Hongjun, Yin, Yijie, Zhou, Yuhan, Li, Wei, Xiong, Yang, Wang, Yichen, Zhang, Zitong, Yang, Yang, Xie, Dongxiao, Wang, Manlin, Huang, Shijie
Format:	Article
Language:	English
Subjects:	Acoustics Adaptation Adaptive control Autonomous underwater vehicles AUV swarm Bandwidths Closed loops Cognition & reasoning Collaboration Communication cooperative perception Data exchange Deep sea Deep water Digital twins Energy Energy budget Energy management explainable AI Explainable artificial intelligence Exploration Federated learning Geology Geophysics Hardware-in-the-loop simulation intelligent multi-agent systems meta-learning Multiagent systems Optimization Paradigms Pareto analysis Perception Physics Representation learning Robotics Semantics Sensors Transfer learning Underwater exploration Underwater vehicles
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