dc.creator | Ten Kathen, Micaela Jara | es |
dc.creator | Jurado Flores, Isabel | es |
dc.creator | Gutiérrez Reina, Daniel | es |
dc.date.accessioned | 2021-09-22T16:59:38Z | |
dc.date.available | 2021-09-22T16:59:38Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Ten Kathen, M.J., Jurado Flores, I. y Gutiérrez Reina, D. (2021). An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications. Electronics, 10, Article number 1605. | |
dc.identifier.issn | 2079-9292 | es |
dc.identifier.uri | https://hdl.handle.net/11441/126117 | |
dc.description.abstract | Controlling the water quality of water supplies has always been a critical challenge,
and water resource monitoring has become a need in recent years. Manual monitoring is not
recommended in the case of large water surfaces for a variety of reasons, including expense and
time consumption. In the last few years, researchers have proposed the use of autonomous vehicles
for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource
explorations by using path planning techniques to guide the movements of each vehicle. The main
idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of
autonomous surface vehicles will be guided by an improved particle swarm optimization based
on the Gaussian process as a surrogate model. The purpose of using the surrogate model is to
model water quality parameter behavior and to guide the movements of the vehicles toward areas
where samples have not yet been collected; these areas are considered areas with high uncertainty or
unexplored areas and areas with high contamination levels of the lake. The results show that the
proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration
and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been
compared with other techniques like the original particle swarm optimization and the particle
swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake
environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based
PSO in terms of mean square error with respect to the other techniques. | es |
dc.format | application/pdf | es |
dc.format.extent | 21 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI AG | es |
dc.relation.ispartof | Electronics, 10, Article number 1605. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Particle swarm optimization | es |
dc.subject | Gaussian process | es |
dc.subject | Water monitoring | es |
dc.subject | Ypacarai Lake | es |
dc.subject | Autonomous surface vehicles | es |
dc.subject | Machine learning | es |
dc.title | An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications | 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.publisherversion | https://www.mdpi.com/2079-9292/10/13/1605 | es |
dc.identifier.doi | 10.3390/electronics10131605 | es |
dc.journaltitle | Electronics | es |
dc.publication.volumen | 10 | es |
dc.publication.initialPage | Article number 1605 | es |