Abstract
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 mis-sions like target search,positioning,and relay guidance.Structured on a dual-layer model,the algo-rithm's lower layer manages basic aircraft maneuvers for optimal flight,while the upper layer processes battlefield dynamics,issuing precise navigational commands.This approach enables accu-rate 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 effi-ciency 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.
基金项目
Natural Science Basic Research Program of Shaanxi,China(2022JQ-593)
Key R&D Program of Shaanxi Provincial Department of Science and Technology,China(2022GY-089)
Aeronautical Science Foundation of China(20220013053005)
维普
万方数据