Autonomous Learning of New Environments with a Robotic Team

We describe 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.

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

NASA Tech Brief: https://www.techbriefs.com/component/content/article/tb/pub/features/qa/39254