Speakers
Description
With the advantages and appealing performances of artificial Intelligence (AI) in different applications, space scientists and engineers have shown great interest in AI-based solutions to space scenarios. However, different from terrain applications, the decision of the space vehicles offered are critical and should be trustable in the uncontrolled and risky environment, resulting in the most significant challenge in the use of AI-based techniques for space missions of acting sensibly for the unanticipated and complex situations. We, therefore, study the Explainable Artificial Intelligence (XAI) techniques that are potentially applicable to software-based GNC scenarios, including relative navigation for spacecraft rendezvous, crater detection and landing on asteroids or the Moon. The explainable tools related to the XAI algorithms are developed to make onboard intelligent techniques transparent to ensure a trustable decision while meeting the level of performances required by the space applications within an uncertain and acceptable boundary level. A comprehensive framework is proposed to address the XAI-base space software for spacecraft GNC systems, including syntenic dataset generation, relative navigation scenario building, XAI model developing, software verification and testing, etc.
