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Autonomous Learning of New Environments With a Robotic Team Employing Hyper-Spectral Remote Sensing

Sensors

David J. Lary, David Schaefer, John Waczak, Adam Aker, Aaron Barbosa, Lakitha O. H. Wijeratne, Shawhin Talebi, Bharana Fernando, John Sadler, Tatiana Lary and Matthew D. Lary

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Overview

Using a base autonomous robotic team, researchers developed a paradigm that is easily scalable to multi-robot, multi-sensor autonomous teams. This paradigm can rapidly characterize an environment over a period of minutes and develop training data points for training other machine learning algorithms faster than ever before.
Figure 3. Panel (a) Chemicals absorb light in a characteristic way. Their absorption spectra is a function of their chemical structure. For every pixel we measure an entire spectrum with a hyper-spectral camera so we can identify chemicals within the scene. Panel (b) shows an example hyper-spectral data cube collected in North Texas on 23 November 2020. This particular data cube includes a simulant release, Rhodamine WT.

Figure 3. Panel (a) Chemicals absorb light in a characteristic way. Their absorption spectra is a function of their chemical structure. For every pixel we measure an entire spectrum with a hyper-spectral camera so we can identify chemicals within the scene. Panel (b) shows an example hyper-spectral data cube collected in North Texas on 23 November 2020. This particular data cube includes a simulant release, Rhodamine WT. The top layer of the hyper-spectral data cube shows the regular RGB image, the 462 stacked layers below show the reflectivity (on a log-scale) for each wavelength band between 391 and 1011 nm.

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David Lary, PhD

BrainHealth Investigator Professor of Physics, Hanson Center for Space Sciences Founding Director, MINTS


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