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

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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Bibliographic Details
Main Authors: Daniel Andre Duecker, Andreas Rene Geist, Edwin Kreuzer, Eugen Solowjow
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Sensors
Subjects:
autonomous exploration
environmental field monitoring
gaussian processes
gaussian markov random fields
kalman filtering
stochastic optimal control
Online Access:https://www.mdpi.com/1424-8220/19/9/2094

Internet

https://www.mdpi.com/1424-8220/19/9/2094

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