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

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

doi:10.20944/preprints202102.0454.v1

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preprints.org > environmental and earth sciences > atmospheric science and meteorology > doi: 10.20944/preprints202102.0454.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

^* ,

Lakitha O.H. Wijeratne

Version 1 : Received: 6 February 2021 / Approved: 22 February 2021 / Online: 22 February 2021 (11:06:36 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Abstract

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 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 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.

Keywords

Machine Learning; Hyper-spectral Imaging; Robot Team; Autonomous; UAV; Robotic Boat

Subject

Environmental and Earth Sciences, Atmospheric Science and Meteorology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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