Title A Bayesian Optimization Approach for Multi-Function Estimation for Environmental Monitoring Using an Autonomous Surface Vehicle: Ypacarai Lake Case Study
Authors PERALTA SAMANIEGO, FEDERICO, GUTIÉRREZ REINA, DANIEL, Toral, Sergio , Arzamendia, Mario , Gregor, Derlis
External publication Si
Means ELECTRONICS
Scope Article
Nature Científica
JCR Quartile 3
SJR Quartile 2
JCR Impact 2.69
SJR Impact 0.59
Publication date 01/04/2021
ISI 000644020700001
DOI 10.3390/electronics10080963
Abstract Bayesian optimization is a sequential method that can optimize a single and costly objective function based on a surrogate model. In this work, we propose a Bayesian optimization system dedicated to monitoring and estimating multiple water quality parameters simultaneously using a single autonomous surface vehicle. The proposed work combines different strategies and methods for this monitoring task, evaluating two approaches for acquisition function fusion: the coupled and the decoupled techniques. We also consider dynamic parametrization of the maximum measurement distance traveled by the ASV so that the monitoring system balances the total number of measurements and the total distance, which is related to the energy required. To evaluate the proposed approach, the Ypacarai Lake (Paraguay) serves as the test scenario, where multiple maps of water quality parameters, such as pH and dissolved oxygen, need to be obtained efficiently. The proposed system is compared with the predictive entropy search for multi-objective optimization with constraints (PESMOC) algorithm and the genetic algorithm (GA) path planning for the Ypacarai Lake scenario. The obtained results show that the proposed approach is 10.82% better than other optimization methods in terms of R2 score with noiseless measurements and up to 17.23% better when the data are noisy. Additionally, the proposed approach achieves a good average computational time for the whole mission when compared with other methods, 3% better than the GA technique and 46.5% better than the PESMOC approach.
Keywords multi-function estimation; Bayesian optimization; data acquisition; environmental monitoring; autonomous vehicles; multiple-objective Bayesian optimization
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