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Enhancing Safety of Students with Mobile Air Filtration during School Reopening from COVID-19

Haoguang Yang, Mythra V. Balakuntala, Abigayle E. Moser, Jhon J. Quiñones, Ali Doosttalab, Antonio Esquivel-Puentes, Tanya Purwar, Luciano Castillo, Nina Mahmoudian, Richard M. Voyles

Year
2021
Citations
2
Access
Open access

Abstract

The paper discusses how robots enable occupant-safe continuous protection for students when schools reopen. Conventionally, fixed air filters are not used as a key pandemic prevention method for public indoor spaces because they are unable to trap the airborne pathogens in time in the entire room. However, by combining the mobility of a robot with air filtration, the efficacy of cleaning up the air around multiple people is largely increased. A disinfection co-robot prototype is thus developed to provide continuous and occupant-friendly protection to people gathering indoors, specifically for students in a classroom scenario. In a static classroom with students sitting in a grid pattern, the mobile robot is able to serve up to 14 students per cycle while reducing the worst-case pathogen dosage by 20%, and with higher robustness compared to a static filter. The extent of robot protection is optimized by tuning the passing distance and speed, such that a robot is able to serve more people given a threshold of worst-case dosage a person can receive.

Keywords

RobotCoronavirus disease 2019 (COVID-19)Mobile robotRobustness (evolution)Air filtrationFiltration (mathematics)Computer scienceSimulationEngineeringArtificial intelligence

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