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<dc:creator>Rodrigo, Daniel Vicente</dc:creator>
<dc:creator>Sierra Garcia, Jesús Enrique</dc:creator>
<dc:creator>Santos, Matilde</dc:creator>
<dc:date>2023-01</dc:date>
<dc:description>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.</dc:description>
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<dc:identifier>http://hdl.handle.net/10259/7483</dc:identifier>
<dc:language>eng</dc:language>
<dc:publisher>Elsevier</dc:publisher>
<dc:title>Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing algorithm</dc:title>
<dc:type>info:eu-repo/semantics/article</dc:type>
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