数字海洋与水下攻防2024，Vol.7Issue(5)：前插1,464-470,8.DOI:10.19838/j.issn.2096-5753.2024.05.001
基于强化学习的AUV对接控制算法研究
Research on AUV Docking Control Algorithm Based on Reinforcement Learning
摘要
Abstract
Autonomous underwater vehicles(AUVs)is an important kind of equipment for human to explore and utilize the ocean.Intelligent solution of path planning and control is the basis for an AUV to accomplish other complex tasks.Considering the local path planning problem under terminal attitude constraint and combining with AUV autonomous docking control,a docking controller is developed based on the improved Deep Reinforcement Learning(DRL)algorithm.It enables the AUV to dock autonomously and can increase AUV endurance.Considering the complex wave disturbance factors in the practical operating scenario,a nonlinear disturbance observer(NDO)is used to estimate the external disturbances of each degree of freedom in AUV three-dimensional motion.In order to ensure that the AUV can accomplish the three-dimensional docking control task in a disturbed environment,scientific observation quantities and reward functions are designed for the DRL agent in combination with measurable state quantities.Simulation results demonstrate the effectiveness and robustness of the proposed method.关键词
自主式水下航行器/路径规划/对接控制/强化学习Key words
autonomous underwater vehicle/path planning/docking control/reinforcement learning分类
信息技术与安全科学引用本文复制引用
庄英豪,张天泽,张悦,李沂滨..基于强化学习的AUV对接控制算法研究[J].数字海洋与水下攻防,2024,7(5):前插1,464-470,8.基金项目
国家自然科学基金面上项目"面向多潜艇故障分布式诊断的增量联邦迁移学习"(62273202). （62273202）
