SAFE LUNAR LANDING VIA IMAGES: A REINFORCEMENT META-LEARNING APPLICATION TO AUTONOMOUS HAZARD AVOIDANCE AND LANDING

Future missions to the Moon and Mars will require advanced Guidance, Navigation, and Control (GNC) algorithms for the powered descent phase. GNC tasks are generally performed by independent modules. In this paper, reinforcement meta-learning and hazard detection and avoidance are embedded into a single system to derive the optimal thrust command for a safe lunar pinpoint landing using sequences of images and radar altimeter data as inputs. In particular, we incorporate an image-based autonomous hazard detection and avoidance algorithm with real-time GNC for a successful landing. The former is achieved using a machine learning model trained in a supervised fashion to recognize hazardous areas in the camera field of view and selecting a safe point accordingly. Then, within the reinforcement meta-learning framework, this information is used by the agent to learn how to behave in this simulated environment and land safely.