Modular Artificial Intelligence for Faults: Local Online Watch and Efficient Response

MAIFLOWER (Modular AI for Faults: Local Online Watch and Efficient Response) leverages previous NASA investments to develop a generalized architecture for fault management that is capable of being deployed across space platforms of all kinds. We have significant experience in all of the required technologies and have already integrated them into a general MAESTRO architecture designed to be easily applied to all spacecraft subsystems.MAIFLOWER Phase I will aim to detect, diagnose, and mitigate the effects of faults on Astroboticrsquo;s Vertical Solar Array Technology (VSAT), a rover that will egress from its lander, transit to a desired location near the lunar South Pole, ldquo;wigglerdquo; into the lunar soil, and deploy a 60rsquo; high solar array to generate and distribute power to other lunar systems. MAIFLOWER will target faults that may occur during navigation to the destination, the leveling process performed when wiggling into the regolith, and deployment of the solar array as well as issues that may arise due to loss of communications and related to thermal management.MAIFLOWER will augment previous NASA-funded MAESTRO technology by introducing transformers, a machine learning method commonly utilized on series data, to the space domain for fault detection. This addition will enablenbsp;MAIFLOWER to not only better diagnose faults but also be alerted to novel off-nominal conditions. MAIFLOWER will make use of a suite of AI technologies: model-based reasoning, case-based reasoning, and machine learning to detect, diagnose, and triage faults as they occur; efficient algorithms to plan courses of action (COAs) and schedule a response (built on our very successful Aurora technology); and behavior transition networks to adaptively execute selected COAs to mitigate the effects of the fault.nbsp;