Underwater Multi-agent Cooperative Formation Hunting Based on Deep Reinforcement Learning
作者单位：College of Electrical Engineering Zhejiang University
会议名称：《第43届中国控制会议》
会议日期：2024年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 080202[工学-机械电子工程] 081104[工学-模式识别与智能系统] 08[工学] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China under Grant U23B2060 the Joint Fund of Ministry of Education for Pre-research of Equipment under Grant 8091B042220 the Fundamental Research Funds for Xi'an Jiaotong University under Grant xtr072022001
关 键 词：Multi-agent reinforcement learning Multi-AUVs formation hunting trajectory planning collision avoidance
摘 要：In addressing the issue of formation hunting and trajectory planning for multi-autonomous underwater vehicles(AUVs) in complex underwater environments, traditional virtual structure algorithms, and leader-follower models exhibit shortcomings in environmental adaptability and vulnerability to single-point failures. To solve this problem, this article establishes a multi-agent reinforcement learning model with continuous state and action spaces, aiming to optimize the success rate and completion time of the formation hunting task. Furthermore, in establishing the simulation environment for underwater multi-AUVs, a reward function module for the formation hunting task is meticulously designed, taking into account various factors including navigation, formation, efficiency, boundary, and collision avoidance. The efficacy of the proposed methodology was substantiated through a comparative analysis involving the artificial potential field method and the proposed deep reinforcement learning algorithm within the simulation environment. Besides, the efficiency of task execution has improved by approximately 10%, with a success rate approaching 100%.