Image-based Deep Reinforcement Learning for Autonomous Lunar Landing
Open accessAbstract
Future missions to the Moon and Mars will require advanced guidance navigation and control algorithms for the powered descent phase. These algorithm should be capable of reconstructing the state of the spacecraft using the inputs from an array of sensors and apply the required command to ensure pinpoint landing accuracy, possibly in an optimal way. This has historically been solved using off-line architectures that rely on the computation of the optimal trajectory beforehand which is then used to drive the controller. The advent of machine learning and artificial intelligence has opened new possibilities for closed-loop optimal guidance. Specifically, the use of reinforcement learning can lead to intelligent systems that learn from a simulated environment how to perform optimally a certain task. In this paper we present an adaptive landing algorithm that learns from experience how to derive the optimal thrust in a lunar pinpoint landing problem using images and altimeter data as input.
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Other works have explored the use of Reinforcement Learning for autonomous planetary landing.
“Other works have explored the use of Reinforcement Learning (RL) for autonomous planetary landing27,28.”
From Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning (2024) · cites this paper as [27]
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Cited by
Sorted by citations
Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning
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“Other works have explored the use of Reinforcement Learning (RL) for autonomous planetary landing 27,28 .”
- Authors
- Scorsoglio, A., Furfaro, R., Linares, R., & Gaudet, B.
- Affiliations
- University of Arizona (US)
- Massachusetts Institute of Technology (US)
- Venue
- AIAA Scitech 2020 Forum
- Published
- 2020-01-05
- OpenAlex
- W2997989123
- Cited by
- 31
- References
- 20
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 6 · 2023: 2 · 2022: 7 · 2021: 9 · 2020: 2
- FWCI
- 1.91
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-sa
- Locations
- AIAA SciTech Forum (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
- Physical Sciences › Engineering › Aerospace Engineering › Guidance and Control Systems
- Keywords
- powered descent guidance, deep reinforcement learning, visual servoing, altimeter data, spacecraft state estimation, thrust optimization, autonomous landing
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