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Mobile Robotic Sensors for Environmental Monitoring using Gaussian Markov Random Field

Published online by Cambridge University Press:  01 September 2020

Linh Nguyen Open the ORCID record for Linh Nguyen [Opens in a new window] ,
Sarath Kodagoda ,
Ravindra Ranasinghe  and
Gamini Dissanayake
Linh Nguyen*
Affiliation:
School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Churchill, VIC 3842, Australia
Sarath Kodagoda
Affiliation:
Centre for Autonomous Systems, University of Technology Sydney, Ultimo, NSW 2007, Australia E-mails: sarath.kodagoda@uts.edu.au, ravindra.ranasinghe@uts.edu.au, gamini.dissanayake@uts.edu.au
Ravindra Ranasinghe
Affiliation:
Centre for Autonomous Systems, University of Technology Sydney, Ultimo, NSW 2007, Australia E-mails: sarath.kodagoda@uts.edu.au, ravindra.ranasinghe@uts.edu.au, gamini.dissanayake@uts.edu.au
Gamini Dissanayake
Affiliation:
Centre for Autonomous Systems, University of Technology Sydney, Ultimo, NSW 2007, Australia E-mails: sarath.kodagoda@uts.edu.au, ravindra.ranasinghe@uts.edu.au, gamini.dissanayake@uts.edu.au
*
*Corresponding author. E-mail: l.nguyen@federation.edu.au
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Summary

This paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.

Keywords

Environmental monitoringMobile robotic sensorsSensor networksSpatial predictionGaussian Markov random field
Type
Articles
Information
Robotica , Volume 39 , Issue 5 , May 2021 , pp. 862 - 884
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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