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dc.creatorYanes Luis, Samueles
dc.creatorPeralta Samaniego, Federicoes
dc.creatorTapia Córdoba, Alejandroes
dc.creatorRodríguez del Nozal, Álvaroes
dc.creatorToral, S. L.es
dc.creatorGutiérrez Reina, Danieles
dc.date.accessioned2022-07-07T15:46:53Z
dc.date.available2022-07-07T15:46:53Z
dc.date.issued2022-03
dc.identifier.citationYanes Luis, S., Peralta Samaniego, F., Tapia Córdoba, A., Rodríguez del Nozal, Á., Toral, S.L. y Gutiérrez Reina, D. (2022). An evolutionary multi-objective path planning of a fleet of ASVs for patrolling water resources. Engineering Applications of Artificial Intelligence, 112, 104852.
dc.identifier.issn0952-1976es
dc.identifier.urihttps://hdl.handle.net/11441/135124
dc.description.abstractThe rapid increase of human activities with direct influence on the environment has motivated the global awareness of the need to efficiently monitor the natural resources. Among the wide range of problems addressed, such as overuse of agrochemicals, uncontrolled waste, etc., the contamination of water resources plays a protagonist role, given its close links with biodiversity and the food chain. Water monitoring is considered one of the most efficient ways to deal with these problems, especially through the use of autonomous vehicles, which can boost the capabilities and efficiency of the monitoring routines with appropriate strategies. In this work, the monitoring problem is addressed by means of the Non-Homogeneous Patrolling Problem with closed circuits. This problem has a great computational complexity, especially when multiple targets are included in a monitoring mission. A formulation based on closed metric graphs and the application of a multi-objective genetic algorithm is proposed to provide Pareto-efficient monitoring solutions for a variable number of Autonomous Surface Vehicles. To address the multi-agent, multi-objective and constrained paradigm, efficient genetic operators have been designed for the generation of valid solutions in an affordable time. The method results in Pareto-efficient solutions for scenarios with disjoint and uncorrelated objectives, which outperform the fitness of other solutions by a factor of 2, on average. The results provide decision makers a method to find different non-dominated strategies depending on the monitoring needs, depending on fleet and vehicle size.es
dc.description.sponsorshipMinisterio de Ciencia e Innovación RTI2018- 098964-B-I00es
dc.description.sponsorshipJunta de Andalucía US-1257508 y PY18-RE0009es
dc.formatapplication/pdfes
dc.format.extent12 p.es
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofEngineering Applications of Artificial Intelligence, 112, 104852.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAutonomous surface vehicleses
dc.subjectWater monitoringes
dc.subjectGenetic algorithmes
dc.subjectPatrolling problemes
dc.subjectMulti-objective optimizationes
dc.titleAn evolutionary multi-objective path planning of a fleet of ASVs for patrolling water resourceses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ingeniería Electrónicaes
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ingeniería Eléctricaes
dc.relation.projectIDRTI2018- 098964-B-I00es
dc.relation.projectIDUS-1257508es
dc.relation.projectIDPY18-RE0009es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0952197622001051es
dc.identifier.doi10.1016/j.engappai.2022.104852es
dc.journaltitleEngineering Applications of Artificial Intelligencees
dc.publication.volumen112es
dc.publication.initialPage104852es
dc.contributor.funderMinisterio de Ciencia e Innovación (MICIN). Españaes
dc.contributor.funderJunta de Andalucíaes

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