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Human Recognition for Resource-Constrained Mobile Robot Applied to Covid-19 Disinfection

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Advances in System-Integrated Intelligence (SYSINT 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 546))

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Abstract

The global COVID-19 pandemic has stimulated the use of disinfection robots: in September 2021, following a European Commission’s action, 200 disinfection robots were delivered to European Hospitals. UV-C light is a common disinfection method, however, direct exposure to UV-C radiation is harmful and disinfection can be operated only in areas strictly forbidden to human personnel. We believe more advanced safety mechanisms are needed to increase the operational flexibility and safety level. We propose a safety mechanism based on vision and artificial intelligence, optimised for execution on mobile robot platforms. It analyses in real-time four video streaming and disables UV-C lamps when needed. Concerning other detection methods, it has a relatively wider and deeper range, and the capability to operate in a dynamic environment. We present the development of the method with a performance comparison of different implementation solutions, and an on-field evaluation through integration on a mobile disinfection robot.

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Author information

Authors and Affiliations

  1. Institute of Mechanical Intelligence, Scuola Superiore Sant’Anna of Pisa, PERCRO Lab, Via Alamanni, 13b, San Giuliano Terme, Ghezzano, 56010, Pisa, Italy

    Andrea Mattia Garavagno, Daniele Leonardis & Antonio Frisoli

Authors
  1. Andrea Mattia Garavagno
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  2. Daniele Leonardis
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  3. Antonio Frisoli
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Corresponding author

Correspondence to Andrea Mattia Garavagno .

Editor information

Editors and Affiliations

  1. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Maurizio Valle

  2. Fraunhofer Institute for Manufacturing Technology and Advanced Materials, Bremen, Germany

    Dirk Lehmhus

  3. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Christian Gianoglio

  4. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Edoardo Ragusa

  5. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Lucia Seminara

  6. Computer Science and Mathematics, University of Bremen, Bremen, Germany

    Stefan Bosse

  7. Department of Electrical and Electronics Engineering (EEE), Lebanese International University (LIU), Beirut, Lebanon

    Ali Ibrahim

  8. BIBA, University of Bremen, Bremen, Germany

    Klaus-Dieter Thoben

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Garavagno, A.M., Leonardis, D., Frisoli, A. (2023). Human Recognition for Resource-Constrained Mobile Robot Applied to Covid-19 Disinfection. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_12

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  • DOI: https://doi.org/10.1007/978-3-031-16281-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16280-0

  • Online ISBN: 978-3-031-16281-7

  • eBook Packages: EngineeringEngineering (R0)

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