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

Research output: Contribution to journalArticlepeer-review

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 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.

Original languageEnglish (US)
Article number2240
JournalSensors
Volume21
Issue number6
DOIs
StatePublished - Mar 2 2021
Externally publishedYes

Keywords

  • Autonomous
  • Hyper-spectral imaging
  • Machine learning
  • Robot team
  • Robotic boat
  • UAV

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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