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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.18709 |
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| _version_ | 1866915634821988352 |
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| author | Oh, Xueyan Her, Jonathan Ong, Zhixiang Koh, Brandon Tan, Yun Hann Tan, U-Xuan |
| author_facet | Oh, Xueyan Her, Jonathan Ong, Zhixiang Koh, Brandon Tan, Yun Hann Tan, U-Xuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Autonomous Surface Selection For Manipulator-Based UV Disinfection In Hospitals Using Foundation Models Oh, Xueyan Her, Jonathan Ong, Zhixiang Koh, Brandon Tan, Yun Hann Tan, U-Xuan Robotics 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. |
| title | Autonomous Surface Selection For Manipulator-Based UV Disinfection In Hospitals Using Foundation Models |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.18709 |