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    Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach

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    Name:
    Adaptive_generalized_ZEM_ZEV_A ...
    Embargo:
    2022-03-04
    Size:
    1.815Mb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
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    Author
    Furfaro, Roberto
    Scorsoglio, Andrea
    Linares, Richard
    Massari, Mauro
    Affiliation
    Univ Arizona, Dept Syst & Ind Engn, Dept Aerosp & Mech Engn
    Univ Arizona, Dept Syst & Ind Engn
    Issue Date
    2020-06
    Keywords
    Optimal landing guidance
    Deep reinfocement learning
    Closed-loop guidance
    
    Metadata
    Show full item record
    Publisher
    PERGAMON-ELSEVIER SCIENCE LTD
    Citation
    Furfaro, R., Scorsoglio, A., Linares, R., & Massari, M. (2020). Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach. Acta Astronautica. https://doi.org/10.1016/j.actaastro.2020.02.051
    Journal
    ACTA ASTRONAUTICA
    Rights
    © 2020 IAA. Published by Elsevier Ltd. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Precision landing on large and small planetary bodies is a technology of utmost importance for future human and robotic exploration of the solar system. In this context, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) feedback guidance algorithm has been studied extensively and is still a field of active research. The algorithm, although powerful in terms of accuracy and ease of implementation, has some limitations. Therefore with this paper we present an adaptive guidance algorithm based on classical ZEM/ZEV in which machine learning is used to overcome its limitations and create a closed loop guidance algorithm that is sufficiently lightweight to be implemented on board spacecraft and flexible enough to be able to adapt to the given constraint scenario. The adopted methodology is an actor-critic reinforcement learning algorithm that learns the parameters of the above-mentioned guidance architecture according to the given problem constraints.
    Note
    24 month embargo; published online: 4 March 2020
    ISSN
    0094-5765
    DOI
    10.1016/j.actaastro.2020.02.051
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.actaastro.2020.02.051
    Scopus Count
    Collections
    UA Faculty Publications

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