摘要
针对无人水下航行器(unmanned underwater vehicle,UUV)如何进行任务分配、航路规划、指挥控制问题,提出一种新的控制实现方法。搭建UUV指挥智能体训练平台,设计学习训练所需的想定,进行状态设计、数据适配、决策解析和规则库建立,选定近端策略优化(proximal policy optimization,PPO)强化学习算法进行训练,并进行应用验证。结果表明:指挥智能体能有效对UUV进行任务分配、航路规划、指挥控制;通过不断优化算法,可提高战胜基于规则的传统控制方法的胜率。
Aiming at the methods of task allocation,route planning and command control of unmanned underwater vehicle(UUV),a new control implementation method,command agent based on deep reinforcement learning,is proposed to replace human in the loop or automatic command and control.Build UUV command agent training platform,design scenarios required for learning and training,conduct state design,data adaptation,decision analysis and rule base establishment,and select proximal policy optimization(PPO)reinforcement learning algorithm for training.The application verification of the command agent generated by training and learning is carried out.The results show that the command intelligence can effectively carry out task allocation,route planning,command and control of UUV,and make bold guesses.By continuously optimizing the algorithm,the winning rate of defeating the traditional rule-based control method can be improved.
作者
林九根
朱衍明
余景锋
宋家平
吴如悦
Lin Jiugen;Zhu Yanming;Yu Jingfeng;Song Jiaping;Wu Ruyue(Ai Department,CSSC Systems Engineering Research Institute,Beijing 100094,China)
出处
《兵工自动化》
北大核心
2024年第1期92-96,共5页
Ordnance Industry Automation
关键词
航路规划
任务分配
智能体
强化学习
route planning
task allocation
agent
reinforcement learning
