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Dynamic stochasticmodeling for adaptive sampling of environmental variables using an AUV

Abstract

Discharge of mine tailings significantly impacts the ecological status of the sea. Methods to efficiently monitor the extent of dispersion is essential to protect sensitive areas. By combining underwater robotic sampling with ocean models, we can choose informative sampling sites and adaptively change the robot’s path based on in situ measurements to optimally map the tailings distribution near a seafill. This paper creates a stochastic spatio-temporal proxy model of dispersal dynamics using training data from complex numerical models. The proxy model consists of a spatio-temporal Gaussian process model based on an advection–diffusion stochastic partial differential equation. Informative sampling sites are chosen based on predictions from the proxy model using an objective function favoring areas with high uncertainty and high expected tailings concentrations. A simulation study and data from real-life experiments are presented.
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Category

Academic article

Language

English

Author(s)

  • Gunhild Elisabeth Berget
  • Jo Eidsvik
  • Morten Omholt Alver
  • Tor Arne Johansen

Affiliation

  • SINTEF Community / Mobility
  • Norwegian University of Science and Technology

Year

2023

Published in

Autonomous Robots

ISSN

0929-5593

Volume

47

Page(s)

483 - 502

View this publication at Norwegian Research Information Repository