dc.creator | Yanes Luis, Samuel | es |
dc.creator | Gutiérrez Reina, Daniel | es |
dc.creator | Toral, S. L. | es |
dc.date.accessioned | 2021-05-27T08:26:36Z | |
dc.date.available | 2021-05-27T08:26:36Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Yanes 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.issn | 2169-3536 | es |
dc.identifier.uri | https://hdl.handle.net/11441/110871 | |
dc.description.abstract | Autonomous 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.sponsorship | Ministerio de Ciencia, innovación y Universidades RTI2018-098964-B-I00 | es |
dc.description.sponsorship | Junta de Andalucía US-1257508 | es |
dc.description.sponsorship | Junta de Andalucía PY18-RE0009 | es |
dc.format | application/pdf | es |
dc.format.extent | 18 p. | es |
dc.language.iso | eng | es |
dc.publisher | Institute of Electrical and Electronics Engineers | es |
dc.relation.ispartof | IEEE Access, 8 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Deep reinforcement learning | es |
dc.subject | monitoring | es |
dc.subject | path planning | es |
dc.subject | autonomous surface vehicle | es |
dc.subject | patrolling | es |
dc.subject | complete coverage | es |
dc.title | A Deep Reinforcement Learning Approach for the Patrolling Problem of Water Resources Through Autonomous Surface Vehicles: The Ypacarai Lake Case | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
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://ieeexplore.ieee.org/abstract/document/9252944 | es |
dc.identifier.doi | 10.1109/ACCESS.2020.3036938 | es |
dc.journaltitle | IEEE Access | es |
dc.publication.volumen | 8 | es |
dc.description.awardwinning | Premio Trimestral Publicación Científica Destacada de la US. Escuela Técnica Superior de Ingeniería | |