Mobile robotic sensors for environmental monitoring using gaussian markov random field

Nguyen, L; Kodagoda, S; Ranasinghe, R; Dissanayake, G

Mobile robotic sensors for environmental monitoring using gaussian markov random field

Nguyen, L

Kodagoda, S

Ranasinghe, R Dissanayake, G

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Publisher:: Cambridge University Press
Publication Type:: Journal Article
Citation:: Robotica, 2021, 39, (5), pp. 862-884
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Nguyen, L https://orcid.org/0000-0001-5360-886X
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138
dc.contributor.author	Ranasinghe, R
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680
dc.date.accessioned	2022-06-03T05:07:43Z
dc.date.available	2022-06-03T05:07:43Z
dc.date.issued	2021
dc.identifier.citation	Robotica, 2021, 39, (5), pp. 862-884
dc.identifier.issn	0263-5747
dc.identifier.issn	1469-8668
dc.identifier.uri	http://hdl.handle.net/10453/157905
dc.description.abstract	© 2020 Georg Thieme Verlag. All rights reserved. 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.
dc.language	English
dc.publisher	Cambridge University Press
dc.relation.ispartof	Robotica
dc.relation.isbasedon	10.1017/S026357472000079X
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Mobile robotic sensors for environmental monitoring using gaussian markov random field
dc.type	Journal Article
utslib.citation.volume	39
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-03T05:07:42Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	39
utslib.citation.issue	5

Abstract:

© 2020 Georg Thieme Verlag. All rights reserved. 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.

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http://hdl.handle.net/10453/157905