Development of an Autonomous Mobile Robotic System for Efficient and Precise Disinfection

TL;DR

This paper presents a mobile robotic system for UV disinfection in hospitals that targets virus hotspots, optimizing UV dosage to improve efficiency and reduce disinfection time by around 31%, enhancing safety and resource use.

Contribution

It introduces a novel robotic disinfection approach that prioritizes high-risk areas and optimizes UV exposure, significantly reducing disinfection time compared to existing methods.

Findings

Achieved approximately 31% reduction in disinfection time in hospital scenarios.

Maintained disinfection effectiveness while optimizing UV dosage.

Enhanced safety by minimizing unnecessary UV exposure.

Abstract

The COVID-19 pandemic has severely affected public health, healthcare systems, and daily life, especially amid resource shortages and limited workers. This crisis has underscored the urgent need for automation in hospital environments, particularly disinfection, which is crucial to controlling virus transmission and improving the safety of healthcare personnel and patients. Ultraviolet (UV) light disinfection, known for its high efficiency, has been widely adopted in hospital settings. However, most existing research focuses on maximizing UV coverage while paying little attention to the impact of human activity on virus distribution. To address this issue, we propose a mobile robotic system for UV disinfection focusing on the virus hotspot. The system prioritizes disinfection in high-risk areas and employs an approach for optimized UV dosage to ensure that all surfaces receive an adequate level of UV exposure while significantly reducing disinfection time. It not only improves disinfection efficiency but also minimizes unnecessary exposure in low-risk areas. In two representative hospital scenarios, our method achieves the same disinfection effectiveness while reducing disinfection time by 30.7% and 31.9%, respectively. The video of the experiment is available at: https://youtu.be/wHcWzOcoMPM.