Image-based Deep Reinforcement Learning for Autonomous Lunar Landing

• Main Authors: Scorsoglio, Andrea (Author), Furfaro, Roberto (Author), Linares, Richard (Author), Gaudet, Brian (Author)
• Format: Article
• Language: English
• Published: American Institute of Aeronautics and Astronautics (AIAA), 2021-11-08T18:04:09Z.
• Subjects: Article
• Online Access: Get fulltext

 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.