dc.creator | Yanes Luis, Samuel | es |
dc.creator | Gutiérrez Reina, Daniel | es |
dc.creator | Toral, S. L. | es |
dc.date.accessioned | 2023-02-13T17:58:42Z | |
dc.date.available | 2023-02-13T17:58:42Z | |
dc.date.issued | 2023-01 | |
dc.identifier.citation | Yanes 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.issn | 1568-4946 | es |
dc.identifier.uri | https://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.abstract | Monitoring 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.format | application/pdf | es |
dc.format.extent | 17 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Applied Soft Computing, 132, 109874. | |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Deep Reinforcement Learning | es |
dc.subject | Autonomous Surface Vehicles | es |
dc.subject | Information gathering | es |
dc.subject | Environmental monitoring | es |
dc.subject | Patrolling | es |
dc.title | Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles | es |
dc.type | info:eu-repo/semantics/article | es |
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.projectID | RTI2018-098964-B-I00 | es |
dc.relation.projectID | US-1257508 | es |
dc.relation.projectID | PY18-RE0009 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1568494622009231 | es |
dc.identifier.doi | 10.1016/j.asoc.2022.109874 | es |
dc.contributor.group | Universidad de Sevilla. TIC201: ACE-TI | es |
dc.journaltitle | Applied Soft Computing | es |
dc.publication.volumen | 132 | es |
dc.publication.initialPage | 109874 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades RTI2018-098964-B-I00 | es |
dc.contributor.funder | Junta de Andalucía US-1257508 | es |
dc.contributor.funder | Junta de Andalucía PY18-RE0009 | es |