Hee-ju, Hong Kook, Shin Won JiEun, Oh Ellen Lee, Sun Woo YOUNG, KIM TAE Lee, Woo sub Choi, Jong Suk Suh, Seung Beum Kim, KangGeon 2024-01-19T13:04:06Z 2024-01-19T13:04:06Z 2022-01-10 2021-12 1424-8220 https://pubs.kist.re.kr/handle/201004/115993 Recent outbreaks and the worldwide spread of COVID-19 have challenged mankind with unprecedented difficulties. The introduction of autonomous disinfection robots appears to be indispensable as consistent sterilization is in desperate demand under limited manpower. In this study, we developed an autonomous navigation robot capable of recognizing objects and locations with a high probability of contamination and capable of providing quantified sterilization effects. In order to quantify the 99.9% sterilization effect of various bacterial strains, as representative contaminants with robots operated under different modules, the operating parameters of the moving speed, distance between the sample and the robot, and the radiation angle were determined. We anticipate that the sterilization effect data we obtained with our disinfection robot, to the best of our knowledge, for the first time, will serve as a type of stepping stone, leading to practical applications at various sites requiring disinfection. ？ 2021 by the authors. Licensee MDPI, Basel, Switzerland. English MDPI Standard for the quantification of a sterilization effect using an artificial intelligence disinfection robot Article 10.3390/s21237776 1 Sensors, v.21, no.23 Sensors 21 23 N scie scopus 000734572800001 2-s2.0-85119582623 Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering Instruments & Instrumentation Article COVID-19 Deep learning Disinfection robot Object detection Ultraviolet disinfection (UVD)