Networked and Deep Reinforcement Learning-Based Control for Autonomous Marine Vehicles: A Survey

被引:0
|
作者
Wang, Yu-Long [1 ,2 ]
Wang, Cheng-Cheng [1 ,2 ]
Han, Qing-Long [3 ]
Wang, Xiaofan [2 ,4 ]
机构
[1] Shanghai Univ, Shanghai Key Lab Power Stn Automat Technol, Shanghai 200444, Peoples R China
[2] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China
[3] Swinburne Univ Technol, Sch Sci Comp & Engn Technol, Melbourne, Vic 3122, Australia
[4] Shanghai Inst Technol, Sch Elect & Elect Engn, Shanghai 201418, Peoples R China
基金
中国国家自然科学基金;
关键词
Marine vehicles; Mathematical models; Task analysis; Dynamics; Target tracking; Surveys; Stochastic processes; Autonomous marine vehicles; deep reinforcement learning (DRL); networked control; PATH-FOLLOWING CONTROL; COOPERATIVE FORMATION CONTROL; SURFACE VEHICLE; COLLISION-AVOIDANCE; TARGET TRACKING; VESSELS; AGENTS; USVS;
D O I
10.1109/TSMC.2023.3346401
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Autonomous marine vehicles, which provide a platform for the successful implementation of special tasks, such as maritime rescue, maritime measurement, and dangerous goods monitoring, have been widely utilized. In the last decade, considerable attention has been paid to the analysis, modeling, and networked control of autonomous marine vehicles. This survey provides recent advances in networked and DRL-based control for autonomous marine vehicles. Typical mathematical models of autonomous marine vehicles are introduced first as the foundation for control of autonomous marine vehicles. Then, networked and DRL-based control for autonomous marine vehicles is reviewed. Finally, some challenges and open issues are presented to motivate the future research.
引用
收藏
页码:1 / 14
页数:14
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