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Reinforcement learning based UAV formation control in GPS-denied environment

Chinese Journal of Aeronautics 2023年第11期36卷 281-296页

作者： Bodi MA Zhenbao LIU Feihong JIANG Wen ZHAO Qingqing DANG Xiao WANG Junhong ZHANG Lina WANG School of Civil Aviation Northwestern Polytechnical UniversityXi’an 710000China Research&Development Institute in Shenzhen Northwestern Polytechnical UniversityShenzhen 518000China flight control Division AVIC The First Aircraft InstituteXi’an 710089China

Highly accurate positioning is a crucial prerequisite of multi Unmanned Aerial Vehicle close-formation flight for target tracking,formation keeping,and collision *** the position of a UAV can be obtained through the Global Positioning System(GPS),it is difficult for a UAV to obtain highly accurate positioning data in a GPS-denied environment(e.g.,a GPS jamming area,suburb,urban canyon,or mountain area);this may cause it to miss a tracking target or collide with another *** particular,UAV close-formation control in GPS-denied environments faces difficulties owing to the low-accuracy position,close distance between vehicles,and nonholonomic dynamics of a *** this paper,on the one hand,we develop an innovative UAV formation localization method to address the formation localization issues in GPS-denied environments;on the other hand,we design a novel reinforcement learning based algorithm to achieve the high-efficiency and robust performance of the ***,a novel Lidar-based localization algorithm is developed to measure the localization of each aircraft in the formation *** our solution,each UAV is equipped with Lidar as the position measurement sensor instead of the GPS *** k-means algorithm is implemented to calculate the center point position of UAV.A novel formation position vector matching method is proposed to match center points with UAVs in the formation and estimate their position ***,a reinforcement learning based UAV formation control algorithm is developed by selecting the optimal policy to control UAV swarm to start and keep flying in a close formation of a specific ***,the innovative collision risk evaluation module is proposed to address the collision-free issues in the formation ***,a novel experience replay method is also provided in this paper to enhance the learning *** results validate the accuracy,effectiveness,and robustness of the proposed scheme.

关键词： Close formation control GPS-denied environment Reinforcement learning Unmanned aerial vehicles(UAVs) intelligent flight control

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