Straight-Path Following and Formation Control of USVs Using Distributed Deep Reinforcement Learning and Adaptive Neural Network

作     者：Zhengqing Han Yintao Wang Qi Sun Zhengqing Han;Yintao Wang;Qi Sun

作者机构：the School of Marine Science and TechnologyNorthwestern Polytechnical UniversityXi’an 710072China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2023年第10卷第2期

页      面：572-574页

核心收录：

学科分类：0710[理学-生物学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0824[工学-船舶与海洋工程] 0835[工学-软件工程] 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(U2141238) 

主　　题：Deep policy heading 

摘      要：Dear Editor,This letter presents a distributed deep reinforcement learning(DRL) based approach to deal with the path following and formation control problems for underactuated unmanned surface vehicles(USVs). By constructing two independent actor-critic architectures,the deep deterministic policy gradient(DDPG) method is proposed to determine the desired heading and speed command for each USV.