Deep reinforcement learning in autonomous manipulation for celestial bodies exploration: Applications and challenges

被引：0
|
作者：
Gao, Xizhen [1 ,2 ]
Tang, Liang [1 ,2 ]
Huang, Huang [1 ,2 ]
机构：
[1] Beijing Institute of Control Engineering, Beijing,100094, China
[2] Key Laboratory of Space Intelligent Control Technology, Beijing,100094, China
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2023年 / 44卷 / 06期
关键词：
Deep learning;
D O I：
10.7527/S1000-6893.2022.26762
中图分类号：
学科分类号：
摘要：
According to the higher requirements with regard to control system autonomy for future celestial body exploration missions, the importance of intelligent control technology is introduced. Based on the characteristics of manipulation missions for celestial bodies exploration, the technical challenges of autonomous control are analyzed and summarized. Existing Deep Reinforcement Learning (DRL) based autonomous manipulation algorithms are summarized. According to different difficulties faced by the deep learning based manipulation missions for celestial bodies, achievements of applications of the manipulation skills based on DRL methods are discussed. A prospect of future research directions for intelligent manipulation technologies is given. © 2023 AAAS Press of Chinese Society of Aeronautics and Astronautics. All rights reserved.
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