Learning Environmental Field Exploration with Computationally Constrained Underwater Robots: Gaussian Processes Meet Stochastic Optimal Control

Autonomous exploration of environmental fields is one of the most promising tasks to be performed by fleets of mobile underwater robots. The goal is to maximize the information gain during the exploration process by integrating an information-metric into the path-planning and control step. Therefore...

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Detalles Bibliográficos
Autores principales: Duecker, Daniel Andre, Geist, Andreas Rene, Kreuzer, Edwin, Solowjow, Eugen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539130/
https://www.ncbi.nlm.nih.gov/pubmed/31064096
http://dx.doi.org/10.3390/s19092094

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539130/
https://www.ncbi.nlm.nih.gov/pubmed/31064096
http://dx.doi.org/10.3390/s19092094

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