Autonomous maneuver strategy of swarm air combat based on DDPG

作     者：Luhe Wang Jinwen Hu Zhao Xu Chunhui Zhao 

作者机构：School of AutomationNorthwestern Polytechnical UniversityXi’an 710129China School of Electronics and InformationNorthwestern Polytechnical UniversityXi’an 710129China 

出 版 物：《Autonomous Intelligent Systems》 (自主智能系统（英文）)

年 卷 期：2021年第1卷第1期

页      面：232-243页

核心收录：

学科分类：08[工学] 082503[工学-航空宇航制造工程] 0825[工学-航空宇航科学与技术] 

基　　金：This work is supported by National Natural Science Foundation of China under Grant 61803309 the Key Research and Development Project of Shaanxi Province under Grant 2020ZDLGY06-02 the Aeronautical Science Foundation of China under Grant 2019ZA053008 the Open Foundation of CETC Key Laboratory of Data Link Technology under Grant CLDL-20202101 the China Postdoctoral Science Foundation under Grant 2018M633574 

主　　题：Deep reinforcement learning Cooperative air combat Swarm Maneuver strategy 

摘      要：Unmanned aerial vehicles(UAVs)have been found significantly important in the air combats,where intelligent and swarms of UAVs will be able to tackle with the tasks of high complexity and *** key to empower the UAVs with such capability is the autonomous maneuver decision *** this paper,an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is ***,based on the process of air combat and the constraints of the swarm,the motion model of UAV and the multi-to-one air combat model are ***,a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle ***,a swarm air combat algorithm based on deep deterministic policy gradient strategy(DDPG)is proposed for online strategy ***,the effectiveness of the proposed algorithm is validated by multi-scene *** results show that the algorithm is suitable for UAV swarms of different scales.