Utilizing deep reinforcement learning for tactile-based autonomous capture of non-cooperative objects in space-Reference-Cited by-同舟云学术

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Utilizing deep reinforcement learning for tactile-based autonomous capture of non-cooperative objects in space

Published:2023-11-24 Issue: Volume: Page:
ISSN:2523-3947
Container-title:Aerospace Systems
language:en
Short-container-title:AS

Author:

Beigomi Bahador,Zhu Zheng H.^ORCID

Funder

Natural Sciences and Engineering Research Council of Canada

Canadian Space Agency

Publisher

Springer Science and Business Media LLC

Subject

Space and Planetary Science,Computers in Earth Sciences,Mechanical Engineering,Aerospace Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42401-023-00254-1.pdf

Reference34 articles.

1. Shan M, Guo J, Gill E (2016) Review and comparison of active space debris capturing and removal methods. Prog Aerosp Sci 80:18–32. https://doi.org/10.1016/j.paerosci.2015.11.001

2. Zhao P, Liu J, Wu C (2020) Survey on research and development of on-orbit active debris removal methods. Sci China Technol Sci 63(11):2188–2210. https://doi.org/10.1007/s11431-020-1661-7

3. Macauley MK (2015) The economics of space debris: Estimating the costs and benefits of debris mitigation. Acta Astronaut 115:160–164. https://doi.org/10.1016/j.actaastro.2015.05.006

4. Schaub H, Jasper LEZ, Anderson PV, McKnight DS (2015) Cost and risk assessment for spacecraft operation decisions caused by the space debris environment. Acta Astronaut 113:66–79. https://doi.org/10.1016/j.actaastro.2015.03.028

5. Rybus T (2018) Obstacle avoidance in space robotics: Review of major challenges and proposed solutions. Prog Aerosp Sci 101:31–48. https://doi.org/10.1016/j.paerosci.2018.07.001

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