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Completed TRL 4 (started at 4, targeting 6)
This project leverages recent advancements in Artificial Intelligence (AI) time-series prediction algorithms to forecast the results of decisions made by autonomous systems and to check the validity of these decisions. Such a forecasting module is crucial within autonomous systems to help inform the “planning” module of a system with data (i.e. future probable “system states”) for making better plans, as well as helping the “execution” module of an autonomous system to know when and how to alter the steps it is executing. AI prediction modules are an essential component of intelligent mission architectures, since these modules will help enable: autonomous, opportunistic, event-driven science, and dynamic system responses to faults. This technology is useful both on-board or in-situ for maximizing science return both from individual assets, as well as among components of distributed systems, and across diverse missions.
Our time-series AI Prediction Module modules will help enable: autonomous, opportunistic, event-driven science, and dynamic system responses to faults. This technology is useful both on-board or in-situ for maximizing science return both from individual assets, as well as among components of distributed systems, and across diverse missions. It will be useful in coordination among different missions whose combined observations could enhance total science return, by predicting future spacecraft states.
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