Toward Resilient Spacecraft: Artificially Intelligent Reasoning for Diagnosing Safe Modes
Description
This research will develop a proof-of-concept prototype for an intelligent and explainable reasoning system on-board, which is capable of diagnosis and decision making in real-time.
Detailed example
The framework will leverage classification systems such as Neural Networks (NN) and various uncertainty processing methods such as Dempster–Shafer theory (DST) to make use of low level data as a way to inform higher level reasoning system to generate on-board information at an abstract level which is human readable.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
We will look specifically at implementing our proof-of-concept as a way to diagnose spacecraft safe-mode events, in an attempt to disambiguate non-urgent anomalous safe mode events from more urgent safe-mode events which require a sunward burn maneuver.
Controls / human review
ATO: Not reported; PIA: Not published