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

1
Institute of Mechanics and Ocean Engineering, Hamburg University of Technology, 21073 Hamburg, Germany
2
Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany
3
Siemens Corporate Technology, Berkeley, CA 94704, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(9), 2094; https://doi.org/10.3390/s19092094
Received: 4 April 2019 / Revised: 25 April 2019 / Accepted: 27 April 2019 / Published: 6 May 2019
(This article belongs to the Special Issue Mobile Robot Navigation)
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Abstract

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, the system maintains an internal belief representation of the environmental field which incorporates previously collected measurements from the real field. In contrast to surface robots, mobile underwater systems are forced to run all computations on-board due to the limited communication bandwidth in underwater domains. Thus, reducing the computational cost of field exploration algorithms constitutes a key challenge for in-field implementations on micro underwater robot teams. In this work, we present a computationally efficient exploration algorithm which utilizes field belief models based on Gaussian Processes, such as Gaussian Markov random fields or Kalman regression, to enable field estimation with constant computational cost over time. We extend the belief models by the use of weighted shape functions to directly incorporate spatially continuous field observations. The developed belief models function as information-theoretic value functions to enable path planning through stochastic optimal control with path integrals. We demonstrate the efficiency of our exploration algorithm in a series of simulations including the case of a stationary spatio-temporal field. View Full-Text
Keywords: autonomous exploration; environmental field monitoring; gaussian processes; gaussian markov random fields; kalman filtering; stochastic optimal control autonomous exploration; environmental field monitoring; gaussian processes; gaussian markov random fields; kalman filtering; stochastic optimal control
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Duecker, D.A.; Geist, A.R.; Kreuzer, E.; Solowjow, E. Learning Environmental Field Exploration with Computationally Constrained Underwater Robots: Gaussian Processes Meet Stochastic Optimal Control. Sensors 2019, 19, 2094.

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