UV-C Mobile Robots with Optimized Path Planning: Algorithm Design and On-Field Measurements to Improve Surface Disinfection against SARS-CoV-2

Abstract

Ultraviolet type-C irradiation (UV-C) is an effective no-contact disinfection procedure for surfaces and environments to reduce the spread of severe acute respiratory syndrome coron avirus 2 (SARS-CoV-2), the virus that causes COVID-19. This work evaluates the effect of the adoption of mobile robots for UV-C irradiation, compared to conventional disinfection methods based on static UV-C lamps. On-field evaluation was conducted to measure the energy dose delivered by a robot-based moving source of UV-C radiation at different locations in an indoor environment. The effectively released radiation dose was experimentally measured using distributed UV-C-sensitive detectors, considering all of the environmental factors involved. Moreover, this article proposes a novel trajectory planner consisting of a genetic algorithm (GA) that explores the possible trajectories and disinfection outcomes of a robot moving in a tunable artificial potential field (APF) and is capable of maximizing the delivered UV dose based on ambient geometry. The experimental results show that, compared to a conventional trajectory, an optimized one has better performance in terms of both the coverage of the radiated energy in the environment and the time required to complete the disinfection task.