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

被引：0
作者：
Gao X. [1 ,2 ]
Tang L. [1 ,2 ]
Huang H. [1 ,2 ]
机构：
[1] Beijing Institute of Control Engineering, Beijing
[2] Key Laboratory of Space Intelligent Control Technology, Beijing
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2023年 / 44卷 / 06期
关键词：
autonomous manipulation; celestial bodies exploration; deep reinforcement learning; landing and roving exploration; sample acquisition;
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.
引用
收藏
相关论文
共 75 条
  • [61] D'SOUZA C, D'SOUZA C., An optimal guidance law for planetary landing, (1997)
  • [62] ZHOU S Y, BAI C C., Research on planetary rover path planning method based on deep reinforcement learning ［J］, Unmanned Systems Technology, 2, 4, pp. 38-45, (2019)
  • [63] GONZALEZ F，, Et al., A review of current approaches for UAV autonomous mission planning for Mars biosignatures detection, 2020 IEEE Aerospace Conference, pp. 1-15, (2020)
  • [64] MCEWEN A S, BERGSTROM J W，, Et al., Mars reconnaissance orbiter’s High Resolution Imaging Science Experiment（HiRISE）［J］, Journal of Geophysical Research, 112, E5, (2007)
  • [65] A reinforcement learning framework for space missions in unknown environments, 2020 IEEE Aerospace Conference, pp. 1-8, (2020)
  • [66] PFLUEGER M, SUKHATME G S., Rover-IRL：Inverse reinforcement learning with soft value iteration networks for planetary rover path planning［J］, IEEE Robotics and Automation Letters, 4, 2, pp. 1387-1394, (2019)
  • [67] HUANG Y X, Et al., A multi-agent reinforcement learning method for swarm robots in space collaborative exploration ［C］, 2020 6th International Conference on Control， Automation and Robotics （ICCAR）, pp. 139-144, (2020)
  • [68] SUI Y N., Safe reinforcement learning in constrained Markov decision processes［C］, Proceedings of the 37th International Conference on Machine Learning, pp. 9797-9806, (2020)
  • [69] KRAUSE A., Safe exploration in finite Markov decision processes with Gaussian processes, Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 4312-4320, (2016)
  • [70] YUE Y S，, Et al., Safe exploration and optimization of constrained MDPs using Gaussian processes［J］, Proceedings of the AAAI Conference on Artificial Intelligence, 32, 1, pp. 52-58, (2018)
12345678