首页|Loyal wingman task execution for future aerial combat:A hierarchical prior-based reinforcement learning approach

Loyal wingman task execution for future aerial combat:A hierarchical prior-based reinforcement learning approach

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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.

Beyond-visual-rangeLoyal wingmenHierarchical prior-augmented proximal policy optimizationUnmanned aerial vehiclesWarfare

Jiandong ZHANG、Dinghan WANG、Qiming YANG、Zhuoyong SHI、Longmeng JI、Guoqing SHI、Yong WU

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School of Electronics and Information,Northwestern Polytechnical University,Xi'an 710129,China

Natural Science Basic Research Program of Shaanxi,ChinaKey R&D Program of Shaanxi Provincial Department of Science and Technology,ChinaAeronautical Science Foundation of China

2022JQ-5932022GY-08920220013053005

2024

10.1016/j.cja.2024.03.009

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(5)