Deep reinforcement learning for six degree-of-freedom planetary landing
Name
J18_2020.pdf
Description
Accepted version
Size
2.21 MB
Format
Adobe PDF
Checksum (MD5)
0426dc3b8d0b45f6f8a61db845122d20
Author(s)
Gaudet, Brian
Linares, Richard
Furfaro, Roberto
Date Issued
March 2018
Journal
Advances in Space Research
Publisher
Elsevier BV
Citation
Gaudet, Brian, Linares, Richard and Furfaro, Roberto. 2018. "Deep reinforcement learning for six degree-of-freedom planetary landing." Advances in Space Research, 65 (7).
Version
Author's final manuscript
Abstract
© 2020 COSPAR This work develops a deep reinforcement learning based approach for Six Degree-of-Freedom (DOF) planetary powered descent and landing. Future Mars missions will require advanced guidance, navigation, and control algorithms for the powered descent phase to target specific surface locations and achieve pinpoint accuracy (landing error ellipse <5 m radius). This requires both a navigation system capable of estimating the lander's state in real-time and a guidance and control system that can map the estimated lander state to a commanded thrust for each lander engine. In this paper, we present a novel integrated guidance and control algorithm designed by applying the principles of reinforcement learning theory. The latter is used to learn a policy mapping the lander's estimated state directly to a commanded thrust for each engine, resulting in accurate and almost fuel-optimal trajectories over a realistic deployment ellipse. Specifically, we use proximal policy optimization, a policy gradient method, to learn the policy. Another contribution of this paper is the use of different discount rates for terminal and shaping rewards, which significantly enhances optimization performance. We present simulation results demonstrating the guidance and control system's performance in a 6-DOF simulation environment and demonstrate robustness to noise and system parameter uncertainty.
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.ASR.2019.12.030
