Loyal wingman task execution for future aerial combat:A hierarchical prior-based reinforcement learning approach
作者机构:School of Electronics and InformationNorthwestern Polytechnical UniversityXi’an 710129China
出 版 物:《Chinese Journal of Aeronautics》 (中国航空学报(英文版))
年 卷 期:2024年第37卷第5期
页 面:462-481页
核心收录:
学科分类:08[工学] 081105[工学-导航、制导与控制] 082503[工学-航空宇航制造工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程]
基 金:This study was co-supported by the Natural Science Basic Research Program of Shaanxi,China(No.2022JQ-593) the Key R&D Program of Shaanxi Provincial Department of Science and Technology,China(No.2022GY-089) the Aeronautical Science Foundation of China(No.20220013053005)
主 题:Beyond-visual-range Loyal wingmen Hierarchical prior-augmented proximal policy optimization Unmanned aerial vehicles Warfare
摘 要:In modern Beyond-Visual-Range(BVR)aerial combat,unmanned loyal wingmen are pivotal,yet their autonomous capabilities are *** study introduces an advanced control algorithm based on hierarchical reinforcement learning to enhance these capabilities for critical missions like target search,positioning,and relay *** on a dual-layer model,the algorithm’s lower layer manages basic aircraft maneuvers for optimal flight,while the upper layer processes battlefield dynamics,issuing precise navigational *** approach enables accurate navigation and effective reconnaissance for lead ***,our Hierarchical Prior-augmented Proximal Policy Optimization(HPE-PPO)algorithm employs a prior-based training,prior-free execution method,accelerating target positioning training and ensuring robust target *** paper also improves missile relay guidance and promotes the effective *** integrating this system with a human-piloted lead aircraft,this paper proposes a potent solution for cooperative aerial *** experiments demonstrate enhanced survivability and efficiency of loyal wingmen,marking a significant contribution to Unmanned Aerial Vehicles(UAV)formation control *** advancement is poised to drive substantial interest and progress in the related technological fields.