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Completed TRL 5 (started at 2, targeting 5)
This effort will develop and test advanced Artificial Intelligence/Machine Learning (AI/ML) techniques for path planning/adaptive observing using a suite of airborne terrestrial and ocean remote sensing (RS) instruments. An airborne computational and instrument capability would be used for both reactive and limited memory AI tasks. The present focus is to transform the observing capability of a gimbled dual-axis hyper spectrometer (180-1200nm, 4096 wavebands) from that of a deterministic programmed scanning capability to an Ai-directed scanning process. The effort would demonstrate using AI/ML to direct remote data collection, test several AI/ML embedded developer packages (Google TPU, NVIDIA GPU), and finalize the requirements and a plan to move the capability forward for supporting NASA in its goal to develop autonomous observing instruments and systems. This year's effort will focus on development and testing of AI applications using observations obtain from aircraft surveys obtained in 2022 and it will develop a plan embedding the AI components into a prototype aircraft system.
The technology that is being developed aims to support the capability of carrying out fully autonomous scientific observation and/or applied surveillance or Earth features. The technology aims to use embedded AI/ML components to derive information, such as inference maps, from real time observations from thermal, visible, and hyper spectral imagery in order to enable a decision-making software to control an optical pointing instrument for collection of finer-scale hyper spectral observations. This effort will support NASA Decadal Survey Incubation opportunities for the Planetary boundary Layer (PLB), Surface Topography and Vegetation (STV) and Surface Biology and Geology (SBG) science pathfinder and incubation funding opportunities (i.e., ESTO Decadal Survey Incubation).
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