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Article

Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning

Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
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Author to whom correspondence should be addressed.
Academic Editor: Mehmet Yuce
Sensors 2021, 21(6), 2240; https://doi.org/10.3390/s21062240
Received: 5 January 2021 / Revised: 17 February 2021 / Accepted: 5 March 2021 / Published: 23 March 2021
(This article belongs to the Special Issue Sensors: 20th Anniversary)
This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and it is relevant to satellite calibration/validation and the creation of new remote sensing data products. A case study is described for the rapid characterisation of the aquatic environment, over a period of just a few minutes we acquired thousands of training data points. This training data allowed for our machine learning algorithms to rapidly learn by example and provide wide area maps of the composition of the environment. Along side these larger autonomous robots two smaller robots that can be deployed by a single individual were also deployed (a walking robot and a robotic hover-board), observing significant small scale spatial variability. View Full-Text
Keywords: machine learning; hyper-spectral imaging; robot team; autonomous; UAV; robotic boat machine learning; hyper-spectral imaging; robot team; autonomous; UAV; robotic boat
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MDPI and ACS Style

Lary, D.J.; Schaefer, D.; Waczak, J.; Aker, A.; Barbosa, A.; Wijeratne, L.O.H.; Talebi, S.; Fernando, B.; Sadler, J.; Lary, T.; Lary, M.D. Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning. Sensors 2021, 21, 2240. https://doi.org/10.3390/s21062240

AMA Style

Lary DJ, Schaefer D, Waczak J, Aker A, Barbosa A, Wijeratne LOH, Talebi S, Fernando B, Sadler J, Lary T, Lary MD. Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning. Sensors. 2021; 21(6):2240. https://doi.org/10.3390/s21062240

Chicago/Turabian Style

Lary, David J., David Schaefer, John Waczak, Adam Aker, Aaron Barbosa, Lakitha O. H. Wijeratne, Shawhin Talebi, Bharana Fernando, John Sadler, Tatiana Lary, and Matthew D. Lary. 2021. "Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning" Sensors 21, no. 6: 2240. https://doi.org/10.3390/s21062240

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