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dc.contributor.authorRodrigo, Daniel Vicente
dc.contributor.authorSierra Garcia, Jesús Enrique 
dc.contributor.authorSantos, Matilde
dc.date.accessioned2024-07-01T11:33:24Z
dc.date.available2024-07-01T11:33:24Z
dc.date.issued2023-09-22
dc.identifier.issn0266-4720
dc.identifier.urihttp://hdl.handle.net/10259/9331
dc.description.abstractIn order to maintain adequate levels of cleanliness and sanitation in public facilities, prevent the buildup of viruses and other harmful pathogens, and ensure health and safety, health and labor authorities have repeatedly warned of the need to adhere to proper disinfection protocols in the workplace. This is particularly important in public places where food is handled, where there are more vulnerable people, including hospitals and health care centers, or where there is a large concentration of people. One promising approach is the combination of ultraviolet-C (UV-C) light and mobile robots to automate disinfection processes. Being this technology effective for disinfection, an excessive dose of UV can damage the materials, limiting its applicability. Therefore, a major challenge for automatic disinfection is to find a route that covers the entire surface, ensures cleanliness, and provides the correct radiation dose while preventing environmental materials from being damaged. To achieve this, in this paper a novel intelligent control approach is proposed. A bio-inspired Glasius neural network with a motion planner, an UV estimation module, a speed regulator, and pure pursuit controller are combined into one intelligent system. The motion planner proposes a sequence of movements to go through the space in the most efficient way possible, avoiding obstacles of the environment. The speed controller adjusts the dose of UV-C radiation and the pure pursuit regulator ensures the following of the path. This approach has been tested in various simulation scenarios of increasing complexity and in four different areas of dosing requirements. In simulation, a 44% reduction of the maximum dose is achieved, 17% less distance travelled by the robot and, what is more important, 229% more locations with the appropriate dose.en
dc.language.isoenges
dc.publisherWileyen
dc.relation.ispartofExpert Systems. 2023, V. 40, n. 10en
dc.rightsAtribución-NoComercial 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectAutonomous roboten
dc.subjectComplete coverage path planningen
dc.subjectGlasius bio-inspired neural networken
dc.subjectUltraviolet germicidal irradiationen
dc.subject.otherIngeniería mecánicaes
dc.subject.otherMechanical engineeringen
dc.subject.otherElectrotecniaes
dc.subject.otherElectrical engineeringen
dc.titleGBNN algorithm enhanced by movement planner for UV‐C disinfectionen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1111/exsy.13455es
dc.identifier.doi10.1111/exsy.13455
dc.identifier.essn1468-0394
dc.journal.titleExpert Systemsen
dc.volume.number40es
dc.issue.number10es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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