An Efficient Adaptive Sampling Approach for Mobile Robotic Sensor Networks using Proximal ADMM

Viet Anh Le, Linh Nguyen, Truong X. Nghiem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2021 American Control Conference, ACC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665441971
StatePublished - May 25 2021
Externally publishedYes
Event2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States
Duration: May 25 2021May 28 2021

Publication series

NameProceedings of the American Control Conference


Conference2021 American Control Conference, ACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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