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Completed TRL 3 (started at 3, targeting 4)
This project developed a system that can read analog gauges using a video source. The low TRL work implemented a combination of algorithms that included artificial intelligence (AI), computer vision, and statistics. This work developed a framework for the processes of collecting and pre-processing gauge image data, selecting the structure and parameters of a convolutional neural network, and optimizing them for deep learning. The work also included the development of algorithms and processes to recognize a particular gauge, from others, on a ground support equipment (GSE) panel visible from a single camera field of view. In addition to the implementation of the gauge system in MATLAB, this work implemented the machine learning and AI algorithms in C-language for deployment in small form, low-cost embedded systems such as a raspberry pi single board computer.
The main application of this research in machine learning is to assist humans with the task of reading, recording, and transmitting analog gauge value data from locations where current systems are not capable or unavailable. For example, the autonomous system framework and the implemented algorithms developed for this research can be customized to alert personnel if gauge values are outside nominal range. Whether that is on the Moon or at KSC, having this capability can enhance efficiency and productivity by offloading these tasks to a portable smart machine.
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