信息与控制2025，Vol.54Issue(4)：545-555,11.DOI:10.13976/j.cnki.xk.2024.2122
基于深度强化学习的海上搜救覆盖路径规划算法应用
Application of Deep Reinforcement Learning-based Maritime Search and Rescue Coverage Path Planning Algorithm
摘要
Abstract
Given that current maritime search and rescue(SAR)decision support systems still rely on traditional fixed search patterns,which are inefficient and lack adaptability,we propose a maritime SAR coverage path planning model based on deep reinforcement learning.First,we formulate the maritime SAR coverage path planning problem as a Markov decision process.Then,by integrating a double deep Q-network(DDQN),prioritized DDQN,distributional DQN,and noisy DQN,we design a coverage path planning algorithm tailored for a single rescue vessel.Finally,we validate the feasibility and effectiveness of the proposed algorithm through simulation experiments.Compar-ison results demonstrate that the proposed algorithm substantially outperforms existing methods in path planning quality and search efficiency.关键词
海上搜救/深度强化学习/覆盖路径规划Key words
maritime search and rescue/deep reinforcement learning/coverage path planning分类
信息技术与安全科学引用本文复制引用
韩靖童,余倩,刘源..基于深度强化学习的海上搜救覆盖路径规划算法应用[J].信息与控制,2025,54(4):545-555,11.基金项目
军队后勤科研重大项目(AHJ22C003) （AHJ22C003）
