Autonomous Surface Selection For Manipulator-Based UV Disinfection In Hospitals Using Foundation Models

TL;DR

This paper introduces a foundation model-based approach for automating surface selection in UV disinfection tasks in hospitals, reducing human effort and improving scene understanding for safer, more efficient sterilization.

Contribution

It leverages foundation models for surface selection without training, and introduces a VLM-assisted segmentation refinement to improve accuracy in UV disinfection applications.

Findings

Over 92% success rate in surface segmentation

Effective real-world UV disinfection with manipulator and simulated UV light

Reduced mis-segmentation errors with proposed refinement

Abstract

Ultraviolet (UV) germicidal radiation is an established non-contact method for surface disinfection in medical environments. Traditional approaches require substantial human intervention to define disinfection areas, complicating automation, while deep learning-based methods often need extensive fine-tuning and large datasets, which can be impractical for large-scale deployment. Additionally, these methods often do not address scene understanding for partial surface disinfection, which is crucial for avoiding unintended UV exposure. We propose a solution that leverages foundation models to simplify surface selection for manipulator-based UV disinfection, reducing human involvement and removing the need for model training. Additionally, we propose a VLM-assisted segmentation refinement to detect and exclude thin and small non-target objects, showing that this reduces mis-segmentation errors. Our approach achieves over 92\% success rate in correctly segmenting target and non-target surfaces, and real-world experiments with a manipulator and simulated UV light demonstrate its practical potential for real-world applications.