Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach
Name:
Adaptive_generalized_ZEM_ZEV_A ...
Embargo:
2022-03-04
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1.815Mb
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Description:
Final Accepted Manuscript
Affiliation
Univ Arizona, Dept Syst & Ind Engn, Dept Aerosp & Mech EngnUniv Arizona, Dept Syst & Ind Engn
Issue Date
2020-06
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PERGAMON-ELSEVIER SCIENCE LTDCitation
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.2020.02.051Journal
ACTA ASTRONAUTICARights
© 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 2020ISSN
0094-5765Version
Final accepted manuscriptae974a485f413a2113503eed53cd6c53
10.1016/j.actaastro.2020.02.051