Real-time UAV path planning based on LSTM network

作     者：ZHANG Jiandong GUO Yukun ZHENG Lihui YANG Qiming SHI Guoqing WU Yong ZHANG Jiandong;GUO Yukun;ZHENG Lihui;YANG Qiming;SHI Guoqing;WU Yong

作者机构：School of Electronics and InformationNorthwestern Polytechnical UniversityXi’an 710129China The Flight Automatic Control Research Institute of AVICXi’an 710065China Military Representative Office of Marine Wuhan Bureau in Luoyang AreaLuoyang 471000China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2024年第35卷第2期

页      面：374-385页

核心收录：

学科分类：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] 081105[工学-导航、制导与控制] 082503[工学-航空宇航制造工程] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 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 Natural Science Basic Research Prog ram of Shaanxi(2022JQ-593) 

主　　题：deep Q network path planning neural network unmanned aerial vehicle(UAV) long short-term memory(LSTM) 

摘      要：To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning *** networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some *** to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current ***,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path *** verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.