航空科学技术2023，Vol.34Issue(12)：118-124,7.DOI:10.19452/j.issn1007-5453.2023.12.014
基于深度强化学习的无人机航路规划算法研究
Research on UAV Path Planning Method Based on Deep Reinforcement Learning
摘要
Abstract
Path planning is one of the key technologies for UAVs to accomplish operation missions in complex battlefield environments.In this paper,we propose a UAV path planning method based on PER-D3QN,which realizes the path planning for UAVs in the battlefield environment through network model design,state space design,action space design and reward function design.The PER-D3QN algorithm combines the target network,dueling network and prioritized experience replay,which effectively solves the overfitting problem and unstable problem in deep reinforcement learning.In the end,it is verified through simulation experiments that the proposed method achieved better convergence,stability and applicability compared with double DQN and DQN algorithms,and better real-time performance compared with A* algorithm,which can efficiently realize the path planning of UAVs in the complex battlefield environment,and effectively help the UAVs to attempt the operational mission.关键词
无人机/航路规划/深度强化学习/战场环境建模/PER-D3QNKey words
UAV/path planning/deep reinforcement learning/battlefield environment modeling/PER-D3QN分类
航空航天引用本文复制引用
毕文豪,段晓波..基于深度强化学习的无人机航路规划算法研究[J].航空科学技术,2023,34(12):118-124,7.基金项目
航空科学基金(201905053001) （201905053001）
国家自然科学基金(62073267,61903305) Aeronautical Science Foundation of China(201905053001) （62073267,61903305）
National Natural Science Foundation of China(62073267,61903305) （62073267,61903305）
