dc.creator | Peralta, Federico | es |
dc.creator | Gutiérrez Reina, Daniel | es |
dc.creator | Toral, S. L. | es |
dc.creator | Arzamendia, Mario | es |
dc.creator | Gregor, Derlis | es |
dc.date.accessioned | 2021-06-23T14:29:48Z | |
dc.date.available | 2021-06-23T14:29:48Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Peralta, F., Gutiérrez Reina, D., Toral Marín, S., Arzamendia, M. y Gregor, D. (2021). A Bayesian Optimization Approach for Multi-Function Estimation for Environmental Monitoring Using an Autonomous Surface Vehicle: Ypacarai Lake Case Study. Electronics, 10 (8), Article number 963. | |
dc.identifier.issn | 2079-9292 | es |
dc.identifier.uri | https://hdl.handle.net/11441/114732 | |
dc.description | Article number 963 | es |
dc.description.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. | es |
dc.description.sponsorship | Ministerio de Ciencia, innovación y Universidades RTI2018-098964-B-I00 | es |
dc.description.sponsorship | Junta de Andalucía PY18-RE0009 | es |
dc.description.sponsorship | Junta de Andalucía US-1257508 | es |
dc.description.sponsorship | Junta de Andalucía PAIDI TIC 201 | es |
dc.format | application/pdf | es |
dc.format.extent | 24 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Electronics, 10 (8), Article number 963. | |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Multi-function estimation | es |
dc.subject | Bayesian optimization | es |
dc.subject | Data acquisition | es |
dc.subject | Environmental monitoring | es |
dc.subject | Autonomous vehicles | es |
dc.subject | Multiple-objective Bayesian optimization | es |
dc.title | A Bayesian Optimization Approach for Multi-Function Estimation for Environmental Monitoring Using an Autonomous Surface Vehicle: Ypacarai Lake Case Study | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Electrónica | es |
dc.relation.projectID | RTI2018-098964-B-I00 | es |
dc.relation.projectID | PY18-RE0009 | es |
dc.relation.projectID | US-1257508 | es |
dc.relation.projectID | PAIDI TIC 201 | es |
dc.relation.publisherversion | https://www.mdpi.com/2079-9292/10/8/963 | es |
dc.identifier.doi | 10.3390/electronics10080963 | es |
dc.journaltitle | Electronics | es |
dc.publication.volumen | 10 | es |
dc.publication.issue | 8 | es |
dc.publication.initialPage | Article number 963 | es |