Multi-AUV Cooperative Underwater Multi-Target Tracking Based on Dynamic-Switching-enabled Multi-Agent Reinforcement Learning
<|reference_start|>Multi-AUV Cooperative Underwater Multi-Target Tracking Based on Dynamic-Switching-enabled Multi-Agent Reinforcement Learning: With the rapid development of underwater communication, sensing, automation, robot technologies, autonomous underwater vehicle (AUV) swarms are gradually becoming popular and have been widely promoted in ocean exploration and underwater tracking or surveillance, etc. However, the complex underwater environment poses significant challenges for AUV swarm-based accurate tracking for the underwater moving targets. In this paper, we aim at proposing a multi-AUV cooperative underwater multi-target tracking algorithm especially when the real underwater factors are taken into account.We first give normally modelling approach for the underwater sonar-based detection and the ocean current interference on the target tracking process.Then, we regard the AUV swarm as a underwater ad-hoc network and propose a novel Multi-Agent Reinforcement Learning (MARL) architecture towards the AUV swarm based on Software-Defined Networking (SDN).It enhances the flexibility and scalability of the AUV swarm through centralized management and distributed operations.Based on the proposed MARL architecture, we propose the "dynamic-attention switching" and "dynamic-resampling switching" mechanisms, to enhance the efficiency and accuracy of AUV swarm cooperation during task execution.Finally, based on a proposed AUV classification method, we propose an efficient cooperative tracking algorithm called ASMA.Evaluation results demonstrate that our proposed tracking algorithm can perform precise underwater multi-target tracking, comparing with many of recent research products in terms of convergence speed and tracking accuracy.<|reference_end|>
@article{wang2024multi-auv,
    title={Multi-AUV Cooperative Underwater Multi-Target Tracking Based on
  Dynamic-Switching-enabled Multi-Agent Reinforcement Learning},
    author={Shengbo Wang, Chuan Lin, Guangjie Han, Shengchao Zhu, Zhixian Li,
  Zhenyu Wang},
    journal={arXiv preprint arXiv:2404.13654},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13654},
    primaryClass={cs.MA}
}