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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2023, Vol. 44 ›› Issue (6): 26762-026762.doi: 10.7527/S1000-6893.2022.26762

• Reviews •    

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

Xizhen GAO1,2(), Liang TANG1,2, Huang HUANG1,2   

  1. 1.Beijing Institute of Control Engineering,Beijing  100094,China
    2.Key Laboratory of Space Intelligent Control Technology,Beijing  100094,China
  • Received:2021-12-07 Revised:2022-01-06 Accepted:2022-03-24 Online:2022-04-01 Published:2022-03-30
  • Contact: Xizhen GAO E-mail:gaoxizhen_qd@126.com
  • Supported by:
    National Key Research and Development Program of China(2018AAA0102700)

Abstract:

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.

Key words: celestial bodies exploration, deep reinforcement learning, autonomous manipulation, landing and roving exploration, sample acquisition

CLC Number: