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dc.creatorYanes Luis, Samueles
dc.creatorGutiérrez Reina, Danieles
dc.creatorToral, S. L.es
dc.date.accessioned2021-05-27T08:26:36Z
dc.date.available2021-05-27T08:26:36Z
dc.date.issued2020
dc.identifier.citationYanes Luis, S., Gutiérrez Reina, D. y Toral Marín, S. (2020). A Deep Reinforcement Learning Approach for the Patrolling Problem of Water Resources Through Autonomous Surface Vehicles: The Ypacarai Lake Case. IEEE Access, 8
dc.identifier.issn2169-3536es
dc.identifier.urihttps://hdl.handle.net/11441/110871
dc.description.abstractAutonomous Surfaces Vehicles (ASV) are incredibly useful for the continuous monitoring and exploring task of water resources due to their autonomy, mobility, and relative low cost. In the path planning context, the patrolling problem is usually addressed with heuristics approaches, such as Genetic Algorithms (GA) or Reinforcement Learning (RL) because of the complexity and high dimensionality of the problem. In this paper, the patrolling problem of Ypacarai Lake (Asunción, Paraguay) has been formulated as a Markov Decision Process (MDP) for two possible cases: the homogeneous and the nonhomogeneous scenarios. A tailored reward function has been designed for the non-homogeneous case. Two Deep Reinforcement Learning algorithms such as Deep Q-Learning (DQL) and Double Deep Q-Learning (DDQL) have been evaluated to solve the patrolling problem. Furthermore, due to the high number of parameters and hyperparameters involved in the algorithms, a thorough search has been conducted to nd the best values for training the neural networks and the proposed reward function. According to the results, a suitable con guration of the parameters allows better results for coverage, obtaining more than the 93% of the lake surface on average. In addition, the proposed approach achieves higher sample redundancy of important zones than other common-used algorithms for non-homogeneous coverage path planning such as Policy Gradient, lawnmower algorithm or random exploration, achieving an 64% improvement of the mean time between visits.es
dc.description.sponsorshipMinisterio de Ciencia, innovación y Universidades RTI2018-098964-B-I00es
dc.description.sponsorshipJunta de Andalucía US-1257508es
dc.description.sponsorshipJunta de Andalucía PY18-RE0009es
dc.formatapplication/pdfes
dc.format.extent18 p.es
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineerses
dc.relation.ispartofIEEE Access, 8
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDeep reinforcement learninges
dc.subjectmonitoringes
dc.subjectpath planninges
dc.subjectautonomous surface vehiclees
dc.subjectpatrollinges
dc.subjectcomplete coveragees
dc.titleA Deep Reinforcement Learning Approach for the Patrolling Problem of Water Resources Through Autonomous Surface Vehicles: The Ypacarai Lake Casees
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.relation.projectIDRTI2018-098964-B-I00es
dc.relation.projectIDUS-1257508es
dc.relation.projectIDPY18-RE0009es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/abstract/document/9252944es
dc.identifier.doi10.1109/ACCESS.2020.3036938es
dc.journaltitleIEEE Accesses
dc.publication.volumen8es
dc.description.awardwinningPremio Trimestral Publicación Científica Destacada de la US. Escuela Técnica Superior de Ingeniería

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