Mostrar el registro sencillo del ítem

Artículo

dc.creatorYanes Luis, Samueles
dc.creatorGutiérrez Reina, Danieles
dc.creatorToral, S. L.es
dc.date.accessioned2023-02-13T17:58:42Z
dc.date.available2023-02-13T17:58:42Z
dc.date.issued2023-01
dc.identifier.citationYanes Luis, S., Gutiérrez Reina, D. y Toral, S.L. (2023). Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles. Applied Soft Computing, 132, 109874. https://doi.org/10.1016/j.asoc.2022.109874.
dc.identifier.issn1568-4946es
dc.identifier.urihttps://hdl.handle.net/11441/142678
dc.description© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)es
dc.description.abstractMonitoring and patrolling large water resources is a major challenge for nature conservation. The problem of acquiring data of an underlying environment that usually changes within time involves a proper formulation of the information. The use of Autonomous Surface Vehicles equipped with water quality sensor modules can serve as an early-warning system for contamination peak-detection, algae blooms monitoring, or oil-spill scenarios. In addition to information gathering, the vehicle must plan routes that are free of obstacles on non-convex static and dynamics maps. This work proposes a novel framework to obtain a collision-free policy using deterministic knowledge of the environment by means of a censoring operator and noisy networks that addresses the informative path planning with emphasis in temporal patrolling. Using information gain as a measure of the uncertainty reduction over data, it is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism for model-based obstacle avoidance. The obtained results demonstrate the effectiveness of the proposed algorithm for both cases in the Ypacaraí monitorization task. Simulations showed that the use of noisy-networks are a good choice for enhanced exploration, with 3 times less redundancy in the paths with respect to — greedy policy. Previous coverage strategies are also outperformed both in the accuracy of the obtained contamination model by a 13% on average and by a 37% in the detection of dangerous contamination peaks. Finally, the achieved results indicate the appropriateness of the proposed framework for monitoring scenarios with autonomous vehicles.es
dc.formatapplication/pdfes
dc.format.extent17 p.es
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofApplied Soft Computing, 132, 109874.
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectDeep Reinforcement Learninges
dc.subjectAutonomous Surface Vehicleses
dc.subjectInformation gatheringes
dc.subjectEnvironmental monitoringes
dc.subjectPatrollinges
dc.titleCensored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicleses
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ingeniería Electrónicaes
dc.relation.projectIDRTI2018-098964-B-I00es
dc.relation.projectIDUS-1257508es
dc.relation.projectIDPY18-RE0009es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1568494622009231es
dc.identifier.doi10.1016/j.asoc.2022.109874es
dc.contributor.groupUniversidad de Sevilla. TIC201: ACE-TIes
dc.journaltitleApplied Soft Computinges
dc.publication.volumen132es
dc.publication.initialPage109874es
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades RTI2018-098964-B-I00es
dc.contributor.funderJunta de Andalucía US-1257508es
dc.contributor.funderJunta de Andalucía PY18-RE0009es

FicherosTamañoFormatoVerDescripción
ASC_2023_Yanes_Censored_OA.pdf2.632MbIcon   [PDF] Ver/Abrir  

Este registro aparece en las siguientes colecciones

Mostrar el registro sencillo del ítem

Atribución 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como: Atribución 4.0 Internacional