<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-06-30T05:20:23Z</responseDate><request verb="GetRecord" identifier="oai:riubu.ubu.es:10259/7483" metadataPrefix="mods">https://riubu.ubu.es/oai/request</request><GetRecord><record><header><identifier>oai:riubu.ubu.es:10259/7483</identifier><datestamp>2024-05-14T10:27:11Z</datestamp><setSpec>com_10259_6155</setSpec><setSpec>com_10259_4266</setSpec><setSpec>com_10259.4_106</setSpec><setSpec>com_10259_2604</setSpec><setSpec>col_10259_6156</setSpec></header><metadata><mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:name>
<mods:namePart>Rodrigo, Daniel Vicente</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Sierra Garcia, Jesús Enrique</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Santos, Matilde</mods:namePart>
</mods:name>
<mods:extension>
<mods:dateAvailable encoding="iso8601">2023-03-02T11:15:32Z</mods:dateAvailable>
</mods:extension>
<mods:extension>
<mods:dateAccessioned encoding="iso8601">2023-03-02T11:15:32Z</mods:dateAccessioned>
</mods:extension>
<mods:originInfo>
<mods:dateIssued encoding="iso8601">2023-01</mods:dateIssued>
</mods:originInfo>
<mods:identifier type="issn">0965-9978</mods:identifier>
<mods:identifier type="uri">http://hdl.handle.net/10259/7483</mods:identifier>
<mods:identifier type="doi">10.1016/j.advengsoft.2022.103330</mods:identifier>
<mods:abstract>The COVID-19 pandemic made robot manufacturers explore the idea of combining mobile robotics with UV-C&#xd;
light to automate the disinfection processes. But performing this process in an optimum way introduces some&#xd;
challenges: on the one hand, it is necessary to guarantee that all surfaces receive the radiation level to ensure&#xd;
the disinfection; at the same time, it is necessary to minimize the radiation dose to avoid the damage of the&#xd;
environment. In this work, both challenges are addressed with the design of a complete coverage path planning&#xd;
(CCPP) algorithm. To do it, a novel architecture that combines the glasius bio-inspired neural network (GBNN),&#xd;
a motion strategy, an UV-C estimator, a speed controller, and a pure pursuit controller have been designed.&#xd;
One of the main issues in CCPP is the deadlocks. In this application they may cause a loss of the operation, lack&#xd;
of regularity and high peaks in the radiation dose map, and in the worst case, they can make the robot to get&#xd;
stuck and not complete the disinfection process. To tackle this problem, in this work we propose a preventive&#xd;
deadlock processing algorithm (PDPA) and an escape route generator algorithm (ERGA). Simulation results&#xd;
show how the application of PDPA and the ERGA allow to complete complex maps in an efficient way where&#xd;
the application of GBNN is not enough. Indeed, a 58% more of covered surface is observed. Furthermore, two&#xd;
different motion strategies have been compared: boustrophedon and spiral motion, to check its influence on&#xd;
the performance of the robot navigation.</mods:abstract>
<mods:language>
<mods:languageTerm>eng</mods:languageTerm>
</mods:language>
<mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by-nc-nd/4.0/</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">info:eu-repo/semantics/openAccess</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">Attribution-NonCommercial-NoDerivatives 4.0 Internacional</mods:accessCondition>
<mods:subject>
<mods:topic>Complete coverage path planning</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Mobile robot</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>UV-C</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Deadlocks</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Escape routes</mods:topic>
</mods:subject>
<mods:titleInfo>
<mods:title>Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing algorithm</mods:title>
</mods:titleInfo>
<mods:genre>info:eu-repo/semantics/article</mods:genre>
</mods:mods></metadata></record></GetRecord></OAI-PMH>