Mantelli M, Dos Santos L, de Fraga L, Miotto G, Bergamin A, Cardoso E, Serrano M, Maffei R, Prestes E, Netto J, Kolberg M. Autonomous Environment Disinfection Based on Dynamic UV-C Irradiation Map.
IEEE Robot Autom Lett 2022;
7:4789-4796. [PMID:
35582267 PMCID:
PMC9014471 DOI:
10.1109/lra.2022.3152719]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 02/13/2022] [Indexed: 11/24/2022]
Abstract
The COVID-19 pandemic has become a worldwide concern and has motivated the entire scientific community to join efforts to fight it. Studies have shown that SARS-CoV-2 remains viable onsurfaces for days, increasing the chances of human infection. Environmental disinfection is thus an important action to prevent the transmission of the virus. Despite the valuable contribution of the research community to the field of UV-C disinfection by robots, there still lacks a disinfection system that is fully autonomous and computes its trajectory in real-time and in unknown environments. To meet this need, we propose an autonomous UV-C disinfection strategy for indoor environments based on a dynamic Irradiation Map that indicates the amount of energy applied in each region. Our method was tested in different scenarios and compared with other disinfection strategies. Experiments show that our approach delivers better results, especially when targeting high ideal UV-C doses.
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