Autonomous Mobile Robots Using Machine Learning Methods to Recognise the Rapid Spread of the Ongoing COVID-19 Epidemic

Anca-Gabriela PETRESCU¹, Lucian Ștefăniță GRIGORE², Ionica ONCIOIU²,
Florentina Raluca BÎLCAN¹, Delia Mioara POPESCU¹, Mihai PETRESCU¹*
¹ Valahia University of Targoviste, 2 Carol I Bvd, Targoviste, 130024, Romania
profanca.petrescu@gmail.com, raluca.bilcan@yahoo.com,
depopescu@yahoo.com, mihai_tina@yahoo.com (*Corresponding author)
² Titu Maiorescu University, 189 Calea Vacaresti Street, Bucharest, 040051, Romania
lucian.grigore@prof.utm.ro, ionica.oncioiu@prof.utm.ro

Abstract: The purpose of this article is to implement an algorithm that allows an autonomous ground robot to intervene for the rapid identification of patients with symptoms of COVID-19 in the absence of the medical staff needed to sort patients in hospitals. Based on this autonomous mobile robot-type UGV, it is possible to quickly detect people who show signs of infection with COVID-19. In order to address these problems, investigative equipment (for 3D perception, mapping, navigation, thermal scanning, thermal imaging, detection of facial expressions and of the presence or absence of masks, etc.), as well as behavioral control work were considered on complex scenarios, initially generated online and then by introducing random obstacles. The obtained results showed that the use of mobile robots for special purposes is thus an excellent solution, both for reducing the exposure of medical personnel to the virus, and for increasing the capacity to identify, analyse and warn about the observance of the protection protocols against COVID-19.

Keywords: Autonomous vehicle, Stability, Mobility, Engine, COVID-19, Unmanned ground vehicle.

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CITE THIS PAPER AS:
Anca-Gabriela PETRESCU, Lucian Ștefăniță GRIGORE, Ionica ONCIOIU, Florentina Raluca BÎLCAN, Delia Mioara POPESCU, Mihai PETRESCU, Autonomous Mobile Robots Using Machine Learning Methods to Recognise the Rapid Spread of the Ongoing COVID-19 Epidemic, Studies in Informatics and Control, ISSN 1220-1766, vol. 31(1), pp. 79-88, 2022. https://doi.org/10.24846/v31i1y202208