航空学报 > 2023, Vol. 44 Issue (6): 26762-026762   doi: 10.7527/S1000-6893.2022.26762

深度强化学习技术在地外探测自主操控中的应用与挑战

高锡珍1,2(), 汤亮1,2, 黄煌1,2   

  1. 1.北京控制工程研究所,北京  100094
    2.空间智能控制技术重点实验室,北京  100094
  • 收稿日期:2021-12-07 修回日期:2022-01-06 接受日期:2022-03-24 出版日期:2023-03-25 发布日期:2022-03-30
  • 通讯作者: 高锡珍 E-mail:gaoxizhen_qd@126.com
  • 基金资助:
    国家重点研发计划(2018AAA0102700)

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:2023-03-25 Published:2022-03-30
  • Contact: Xizhen GAO E-mail:gaoxizhen_qd@126.com
  • Supported by:
    National Key Research and Development Program of China(2018AAA0102700)

摘要:

围绕地外探测任务对全自主操控的需求,阐述了智能技术的引入对地外探测操控的重要意义。根据地外探测操控任务的发展现状和特点,总结出地外探测自主操控面临的挑战与难点,对现有基于深度强化学习的操控算法进行概括。以地外探测自主操控任务难点为驱动,对深度强化学习(DRL)技术在地外探测操控中的应用及成果进行了综述与分析,概括了未来地外探测自主智能操控发展中涉及的关键技术问题。

关键词: 地外探测, 深度强化学习, 自主操控, 着陆巡视, 采样

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

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