Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach
Name
J22_2020.pdf
Description
Accepted version
Size
1.82 MB
Format
Adobe PDF
Checksum (MD5)
cb67c119cb010d60ff38e8d6d98b1570
Author(s)
Furfaro, Roberto
Scorsoglio, Andrea
Linares, Richard
Massari, Mauro
Date Issued
2020
Journal
Acta Astronautica
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
© 2020 IAA 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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.ACTAASTRO.2020.02.051
