A Human-Robot Interaction Platform for Intraoral Dental Scan

Abstract This article presents the development and evaluation of a robot-assisted intraoral scanning platform to enhance the precision and repeatability of dental digitization. Intraoral scanners (IOS), a key device in modern restorative dentistry, are highly dependent on practitioner skill, leading to variation in accuracy and increased risk of poorly fitting dental prostheses. A collaborative robot (cobot) is used to mitigate operator variability, reduce musculoskeletal strain and improve educational training. A UR5e cobot equipped with an adaptive gripper and a Dexis IS 3800W IOS was used to digitize a mandibular model, following two occlusal-vestibular-lingual (OVL) scanning pathways and employing two different recording resolutions. Trajectories were generated using SolidWorks and RoboDK software and validated through repeated trials, generating 40 STL files analyzed via Zeiss Inspect software. Metrics such as area of valid distance (AVD), integrated absolute distance (|ID|) and weighted normalized distance (wND) were computed to assess scan quality. In our experimental conditions, high-resolution scans increased surface coverage but did not improve trueness (wND). Initiating scans from the right side improved dimensional precision. Results demonstrate that cobot-assisted scanning can achieve accurate, repeatable digital impressions, offering significant benefits for clinical practice, dental education and telemedicine, particularly in underserved areas. Further trajectory optimization could enhance these outcomes, positioning robotic intraoral scanning as a promising tool in future dental workflows.

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Publication Details

Published
2025-11-16
DOI
https://doi.org/10.1115/imece2025-159741
Primary Topic
Dental Research and COVID-19
Type
article
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article

A Human-Robot Interaction Platform for Intraoral Dental Scan

Swaminath Venkateswaran, Nicolas Lebon, Floriane Laverne, Rebecca Allport
Dental Research and COVID-19
article

A Human-Robot Interaction Platform for Intraoral Dental Scan

Swaminath Venkateswaran, Nicolas Lebon, Floriane Laverne, Rebecca Allport
article en

Abstract

Abstract This article presents the development and evaluation of a robot-assisted intraoral scanning platform to enhance the precision and repeatability of dental digitization. Intraoral scanners (IOS), a key device in modern restorative dentistry, are highly dependent on practitioner skill, leading to variation in accuracy and increased risk of poorly fitting dental prostheses. A collaborative robot (cobot) is used to mitigate operator variability, reduce musculoskeletal strain and improve educational training. A UR5e cobot equipped with an adaptive gripper and a Dexis IS 3800W IOS was used to digitize a mandibular model, following two occlusal-vestibular-lingual (OVL) scanning pathways and employing two different recording resolutions. Trajectories were generated using SolidWorks and RoboDK software and validated through repeated trials, generating 40 STL files analyzed via Zeiss Inspect software. Metrics such as area of valid distance (AVD), integrated absolute distance (|ID|) and weighted normalized distance (wND) were computed to assess scan quality. In our experimental conditions, high-resolution scans increased surface coverage but did not improve trueness (wND). Initiating scans from the right side improved dimensional precision. Results demonstrate that cobot-assisted scanning can achieve accurate, repeatable digital impressions, offering significant benefits for clinical practice, dental education and telemedicine, particularly in underserved areas. Further trajectory optimization could enhance these outcomes, positioning robotic intraoral scanning as a promising tool in future dental workflows.

Sorbonne Université (FR), Université Sorbonne Paris Nord (FR), Santen (France) (FR), Département Santé Animale (FR), Department of Higher Education (IN), Pôle Léonard de Vinci (FR), Ecole Supérieure d'Ingénieurs Léonard de Vinci (FR), Biomatériaux et Bioingénierie (FR)
Openalex Percentile: Top 44%
Dental Research and COVID-19
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