Q-LEARNING ALGORITHM FOR PATH-PLANNING TO MANEUVER THROUGH A SATELLITE CLUSTER

被引：0

作者：

Chu, Xiaoyu ^{[1
]}

Alfriend, Kyle T. ^{[2
]}

Zhang, Jingrui ^{[1
]}

Zhang, Yao ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Aerosp Engn, Beijing 100081, Peoples R China

[2] Texas A&M Univ, Dept Aerosp Engn, College Stn, TX 77843 USA

来源：

ASTRODYNAMICS 2018, PTS I-IV | 2019年 / 167卷

关键词：

D O I：

暂无

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

In this paper, a path planning method for maneuvering through a satellite cluster using Q-learning is presented. An on-orbit servicing spacecraft is supposed to rendezvous with the failed central satellite of a formation and avoid collisions with the other satellites. The dynamic model of the satellite cluster is first established by Lawden equations. Then the theory of Q-learning is introduced and the reward shaping is specified to guide the learning system quickly to success. Furthermore, combining Q-leaming with deep neural networks, deep Q-network (DQN) is employed when the dimension of the problem is enormous. Finally, the rendezvous mission is simulated in 2D and 3D scenarios separately to demonstrate the effectiveness of the proposed method.

引用

页码：2063 / 2082

页数：20

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