Closed-loop AI-aided image-based GNC for autonomous inspection of uncooperative space objects

Abstract

Autonomy is increasingly crucial in space missions due to several factors driving the exploration and utilization of space. In the meanwhile, Artificial Intelligence methods begin to play a crucial role in addressing the challenges associated with and enhancing autonomy in space missions. The proposed work develops a closed-loop simulator for proximity operations scenarios, particularly for the inspection of an unknown and uncooperative target object, with a fully AI-based image processing and GNC chain. This tool is based on four main blocks: image generation, image processing, navigation filter, and guidance and control blocks. All of them have been separately tested and tuned to ensure the correct interface and compatibility in the close-loop architecture. Afterwards, the overall architecture is deployed in an extensive Montecarlo testing campaign to verify and validate the performance of the proposed IP-GNC loop.