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Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning.
Lary, David J; Schaefer, David; Waczak, John; Aker, Adam; Barbosa, Aaron; Wijeratne, Lakitha O H; Talebi, Shawhin; Fernando, Bharana; Sadler, John; Lary, Tatiana; Lary, Matthew D.
Afiliación
  • Lary DJ; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Schaefer D; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Waczak J; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Aker A; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Barbosa A; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Wijeratne LOH; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Talebi S; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Fernando B; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Sadler J; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Lary T; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Lary MD; Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.
Sensors (Basel) ; 21(6)2021 Mar 23.
Article en En | MEDLINE | ID: mdl-33806854
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza