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
Affiliations
David J. Lary
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
David Schaefer
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
John Waczak
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
Adam Aker
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
Aaron Barbosa
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
Lakitha O. H. Wijeratne
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
Shawhin Talebi
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
Bharana Fernando
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
John Sadler
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
Tatiana Lary
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
Matthew D. Lary
Hanson Center for Space Sciences, University of Texas at Dallas, Richardson, TX 75080, USA
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.
