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

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 an...

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Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004590/
https://www.ncbi.nlm.nih.gov/pubmed/33806854
http://dx.doi.org/10.3390/s21062240

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004590/
https://www.ncbi.nlm.nih.gov/pubmed/33806854
http://dx.doi.org/10.3390/s21062240

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