In modern Beyond-Visual-Range(BVR)aerial combat,unmanned loyal wingmen are pivotal,yet their autonomous capabilities are limited.Our study introduces an advanced control algorithm based on hierarchical reinforcement l...In modern Beyond-Visual-Range(BVR)aerial combat,unmanned loyal wingmen are pivotal,yet their autonomous capabilities are limited.Our study introduces an advanced control algorithm based on hierarchical reinforcement learning to enhance these capabilities for critical missions like target search,positioning,and relay guidance.Structured 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 commands.This approach enables accurate navigation and effective reconnaissance for lead aircraft.Notably,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 reacquisition.This paper also improves missile relay guidance and promotes the effective guidance.By integrating this system with a human-piloted lead aircraft,this paper proposes a potent solution for cooperative aerial warfare.Rigorous experiments demonstrate enhanced survivability and efficiency of loyal wingmen,marking a significant contribution to Unmanned Aerial Vehicles(UAV)formation control research.This advancement is poised to drive substantial interest and progress in the related technological fields.展开更多
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).
文摘In modern Beyond-Visual-Range(BVR)aerial combat,unmanned loyal wingmen are pivotal,yet their autonomous capabilities are limited.Our study introduces an advanced control algorithm based on hierarchical reinforcement learning to enhance these capabilities for critical missions like target search,positioning,and relay guidance.Structured 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 commands.This approach enables accurate navigation and effective reconnaissance for lead aircraft.Notably,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 reacquisition.This paper also improves missile relay guidance and promotes the effective guidance.By integrating this system with a human-piloted lead aircraft,this paper proposes a potent solution for cooperative aerial warfare.Rigorous experiments demonstrate enhanced survivability and efficiency of loyal wingmen,marking a significant contribution to Unmanned Aerial Vehicles(UAV)formation control research.This advancement is poised to drive substantial interest and progress in the related technological fields.
