- Title
- An efficient adaptive sampling approach for mobile robotic sensor networks using proximal ADMM
- Creator
- Le, Viet-Anh; Nguyen, Linh; Nghiem, Truong
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179569
- Identifier
- vital:15624
- Identifier
-
https://doi.org/10.23919/ACC50511.2021.9482987
- Identifier
- ISBN:0743-1619 (ISSN); 9781665441971 (ISBN)
- Abstract
- Adaptive sampling in a resource-constrained mobile robotic sensor network for monitoring a spatial phenomenon is a fundamental but challenging problem. In applications where a Gaussian Process is employed to model a spatial field and then to predict the field at unobserved locations, the adaptive sampling problem can be formulated as minimizing the negative log determinant of a predicted covariance matrix, which is a non-convex and highly complex function. Consequently, this optimization problem is typically addressed in a grid-based discrete domain, although it is combinatorial NP-hard and only a near-optimal solution can be obtained. To overcome this challenge, we propose using a proximal alternating direction method of multipliers (Px-ADMM) technique to solve the adaptive sampling optimization problem in a continuous domain. Numerical simulations using a real-world dataset demonstrate that the proposed PxADMM-based method outperforms a commonly used grid-based greedy method in the final model accuracy. © 2021 American Automatic Control Council.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2021 American Control Conference, ACC 2021 Vol. 2021-May, p. 1101-1106
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 IEEE
- Rights
- Open Access
- Full Text
- Reviewed
- Hits: 992
- Visitors: 1019
- Downloads: 48
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Accepted version | 599 KB | Adobe Acrobat PDF | View Details Download |