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 metalearning 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 autonomous hazard detection and avoidance and real-time GNC, which are essential for a successful landing. The former are 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 optimally behave in this simulated environment and land safely.