A Fully Autonomous On-Board GNC Methodology for Small-Body Environments Based on CNN Image Processing and MPCs

Abstract

The increasing need for autonomy in space exploration missions is becoming more and more relevant in the design of missions to small bodies. The long communication latencies and sensitivity of the system to unplanned environmental perturbations mean autonomous methods could be a key design block for this type of mission. In this work, a fully autonomous Guidance, Navigation, and Control (GNC) methodology is introduced. This methodology relies on published CNN-based techniques for surface recognition and pose estimation and also on existing MPC-based techniques for the design of a trajectory to perform a soft landing on an asteroid. Combining Hazard Detection and Avoidance (HDA) with relative navigation systems, a Global Safety Map (GSM) is built on the fly as images are acquired. These GSMs provide the GNC system with information about feasible landing spots and populate a longitude–latitude map with safe/hazardous labels that are later processed to find an optimal landing spot based on mission requirements and a distance-fromhazard metric. The methodology is exemplified using Bennu as the body of interest, and a GSM is built for an arbitrary reconnaissance orbit.