- Title
- Mobile robotic sensors for environmental monitoring using gaussian markov random field
- Creator
- Nguyen, Linh; Kodagoda, Sarath; Ranasinghe, Ravindra; Dissanayake, Gamini
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176959
- Identifier
- vital:15200
- Identifier
-
https://doi.org/10.1017/S026357472000079X
- Identifier
- ISBN:0263-5747 (ISSN)
- Abstract
- 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. Copyright © The Author(s), 2020. Published by Cambridge University Press.
- Publisher
- Cambridge University Press
- Relation
- Robotica Vol. 39, no. 5 (2021), p. 862-884
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © The Author(s), 2020. Published by Cambridge University Press.
- Rights
- Open Access
- Subject
- 0801 Artificial Intelligence and Image Processing; 0906 Electrical and Electronic Engineering; 0913 Mechanical Engineering; Gaussian Markov random field; KEYWORDS: Environmental monitoring; Mobile robotic sensors; Sensor networks; Spatial prediction
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