ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-03072022-154317


Tipo di tesi
Tesi di laurea magistrale
Autore
GARAVAGNO, ANDREA MATTIA
URN
etd-03072022-154317
Titolo
Human recognition for resource constrained mobile robot applied to Covid-19 Disinfection
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
EMBEDDED COMPUTING SYSTEMS
Relatori
relatore Frisoli, Antonio
relatore Leonardis, Daniele
Parole chiave
  • human-machine cooperation
  • Covid-19 disinfection
  • medical robots
Data inizio appello
29/04/2022
Consultabilità
Non consultabile
Data di rilascio
29/04/2092
Riassunto
The global COVID-19 pandemic has stimulated the production of disinfection robots by institutions and companies. The concept of automated disinfection without involving human operators looks interesting in the eyes of the hospital management, and not only. It can save lives by avoiding the cleaning staff working in highly infected environments. At the same time, it can reduce costs by diminishing staff.

The most commonly adopted robots, like the one from the UVD company, use UV-C light to disinfect surfaces. UV-C radiations alter DNA and RNA so that organisms cannot replicate. Others use also vapor and fogging systems that spray chemical disinfectants, such as ozone.

However, UV-C lamps strongly limit human-machine cooperation. Direct exposure to UV-C radiation to the skin has to be avoided for health reasons.

Fortunately, the outstanding results of machine learning offer new possibilities for robotics automation. It can be used to deeply understand the outside world and take actions accordingly, shutting down the lamps whenever a human is detected. So that human-machine cooperation is enabled.
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