ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs

© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

Detalles Bibliográficos
Autores: Ten Kathen, Micaela Jara, Benítez, Natalia, Arzamendia, Mario, Gutiérrez Reina, Daniel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:dnet:idus________::de941122837cbccd7eadb7548994b46e
Acceso en línea:https://hdl.handle.net/11441/184116
https://doi.org/10.3390/electronics15030676
Access Level:acceso abierto
Palabra clave:Ant colony optimization
Autonomous surface vehicles
Gaussian process
Informative path planning
Water monitoring
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oai_identifier_str oai:dnet:idus________::de941122837cbccd7eadb7548994b46e
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spelling ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVsTen Kathen, Micaela JaraBenítez, NataliaArzamendia, MarioGutiérrez Reina, DanielAnt colony optimizationAutonomous surface vehiclesGaussian processInformative path planningWater monitoring© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Autonomous surface vehicles can support water-quality monitoring, but they require planners that place measurements where they most improve the environmental estimate under mission constraints. This paper proposes ACO-Path, an informative path planner that couples Ant Colony Optimization -Ant System- with online Gaussian Process mapping. During the mission, the Gaussian Process updates a mean or contamination map and a variance or uncertainty map, from which dynamic action zones are derived and used to guide an explicit explore then exploit policy. The method is evaluated in a simulated water resource monitoring scenario inspired by Lake Ypacaraí, considering three exploration distances and two heuristic weights. In a comparison against five baseline planners, ACO-Path achieves the lowest hotspot error, ⁢⁢⁢⁢peak =0.19896 ±0.39400, while remaining competitive in global reconstruction, ⁢⁢map =0.00144 ±0.00348, 2 =0.96066 ±0.09861. In addition, a turning analysis based on the absolute heading change between consecutive segments |Δ⁢| shows that ACO-Path produces smoother trajectories, with fewer sharp turns |Δ⁢| ≥45 ° than counterpart baselines under the same mission constraints.Multidisciplinary Digital Publishing Institute (MDPI)Ingeniería Electrónica2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/184116https://doi.org/10.3390/electronics15030676reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésElectronics, 15 (3), 676. https://www.mdpi.com/2079-9292/15/3/676info:eu-repo/semantics/openAccessoai:dnet:idus________::de941122837cbccd7eadb7548994b46e2026-06-17T12:51:07Z
dc.title.none.fl_str_mv ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs
title ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs
spellingShingle ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs
Ten Kathen, Micaela Jara
Ant colony optimization
Autonomous surface vehicles
Gaussian process
Informative path planning
Water monitoring
title_short ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs
title_full ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs
title_fullStr ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs
title_full_unstemmed ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs
title_sort ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs
dc.creator.none.fl_str_mv Ten Kathen, Micaela Jara
Benítez, Natalia
Arzamendia, Mario
Gutiérrez Reina, Daniel
author Ten Kathen, Micaela Jara
author_facet Ten Kathen, Micaela Jara
Benítez, Natalia
Arzamendia, Mario
Gutiérrez Reina, Daniel
author_role author
author2 Benítez, Natalia
Arzamendia, Mario
Gutiérrez Reina, Daniel
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería Electrónica
dc.subject.none.fl_str_mv Ant colony optimization
Autonomous surface vehicles
Gaussian process
Informative path planning
Water monitoring
topic Ant colony optimization
Autonomous surface vehicles
Gaussian process
Informative path planning
Water monitoring
description © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/184116
https://doi.org/10.3390/electronics15030676
url https://hdl.handle.net/11441/184116
https://doi.org/10.3390/electronics15030676
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Electronics, 15 (3), 676.
https://www.mdpi.com/2079-9292/15/3/676
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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