# A HUMAN-ROBOT INTERACTION PLATFORM FOR INTRAORAL DENTAL SCAN

Rebecca Allport, Lebon Nicolas, Floriane Laverne, Swaminath Venkateswaran

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Rebecca Allport, Lebon Nicolas, Floriane Laverne, Swaminath Venkateswaran. A HUMAN-ROBOT INTERACTION PLATFORM FOR INTRAORAL DENTAL SCAN. IMECE 2025, American Society of Mechanical Engineers, Nov 2025, Memphis, United States. hal-05304473

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# A HUMAN-ROBOT INTERACTION PLATFORM FOR INTRAORAL DENTAL SCAN

Rebecca Allport<fnref n="1" />, Nicolas Lebon<fnref n="2" />,<fnref n="3" />, Floriane Laverne<fnref n="4" />, Swaminath Venkateswaran<fnref n="2" />,*

## 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.

**Keywords:** Intraoral scanning, Collaborative Robot, Dentistry, Medical Robotics

## NOMENCLATURE

AVD Area of Valid Distance

CAD Computer-Aided Design

|ID| Integrated absolute Distance

IOS IntraOral Scanner

OVL Occlusal Vestibular Lingual

STL Standard Tessellation Language

wND Weighted Normalized Distance

## 1. INTRODUCTION

Recent advances in digital technologies have significantly transformed healthcare. Dental care also benefits from this evolution. The introduction of computer-aided design and computer-aided manufacturing (CAD/CAM) workflows has revolutionized restorative dentistry by offering a digital alternative to conventional techniques. Among the most impactful innovations is the Intraoral Scanner (IOS), a device that has redefined oral imaging for dental restorations. The incorporation of IOS into daily dental practice provides several compelling advantages. Digital files produced by IOS demonstrate superior dimensional and geometric stability over time, eliminating distortions commonly associated with traditional plaster materials. Furthermore, IOS enhances patient comfort by avoiding the discomfort linked to impression trays, facilitates clearer and faster communication between clinicians and prosthetists and reduces chairside time during clinical procedures [1, 2]. For these benefits to be fully realized, the generation of high-quality Standard Tessellation Language (STL) files, the foundational element of the CAD/CAM process, is paramount [1, 2].

Despite these technological advancements, intraoral digitization remains a highly operator-dependent process [3–5]. The accuracy and precision of scanned data can vary based on the expertise of the practitioner, scanning technique and familiarity with the device. Currently, dental students are not systematically trained in IOS usage during their education; instead they often acquire these skills empirically, through trial and error in clinical daily practice. This informal learning process introduces vari-

<footnote n="1">¹De Vinci Higher Education, Paris, France</footnote>

<footnote n="2">²De Vinci Higher Education, De Vinci Research Center, Paris, France</footnote>

<footnote n="3">³Université Paris Cité and Université Sorbonne Paris Nord, Inserm, Santé Orale, Montrouge, France</footnote>

<footnote n="4">⁴Université Sorbonne Paris Nord and Université Paris Cité, Inserm, Santé Orale, Montrouge, France</footnote>

<footnote>*Corresponding author: swaminath.venkateswaran@devinci.fr
Documentation for asmeconf.cls: Version 1.43, July 10, 2025.</footnote>
ability and potential inaccuracies early in the workflow. Such
errors can accumulate throughout subsequent stages, resulting in
prosthetic devices that fit poorly. These inadequately fitted pros-
theses require extensive manual chairside adjustments, such as
milling and polishing, which not only diminish their final quality
but also compromises their mechanical integrity and long-term
durability.

IOS devices are not only employed for in vivo digitization
but also play a crucial role in the fabrication of final dental prod-
ucts such as restorations and orthodontic aligners. A critical
aspect of validating these digital workflows involves comparing
the resulting STL files in order to determine which clinical pro-
tocols, biomaterials, or manufacturing parameters yield superior
outcomes. Various methods have been developed for STL file
comparison, most of which rely on superimposition techniques
[6]. One established approach involves the use of physical ref-
erence artefacts such as cubes, hemispheres [7], or cuboids [8],
that are positioned on the surfaces of the samples to be compared.
By reconstructing these simple geometrical shapes and aligning
them with their canonical models, it is possible to define reference
entities that serve as alignment anchors for STL superimposition.
Although canonical geometries facilitate the determination of ref-
erence values [9], this method proves inadequate when applied to
the dental arch, due to its complex, horseshoe-shaped anatomy.
Indeed, establishing a standardized reference coordinate system,
a prerequisite for evaluating dimensional deviations and calculat-
ing trueness and precision as stipulated in ISO 5725-1:2023 [10],
is particularly challenging without altering the natural morphol-
ogy of the arch.

An alternative method for comparing STL files involves the best-fit alignment procedure, which typically employs the Iterative Closest Point (ICP) algorithm [11]. This technique is better suited for dental arches, as stable anatomical landmarks for alignment are generally lacking [10]. Studies utilizing best-fit alignment indicate that distortions after superimposition predominantly affect occlusal surfaces [12] and interproximal areas [11]; furthermore, second molars are observed to be more susceptible to distortion compared to first molars [13]. Consequently, there is a clear need to develop comparison methodologies that leverage current digital tools but are specifically adapted for the intricacies of dental arches, accounting for both their complex geometry and the heterogeneous nature of the tissues scanned across different regions.

Additionally, the physical demands of using an IOS present
ergonomic challenges. Scanning intraoral areas requires repeti-
tive wrist, elbow and shoulder movements, fine motor control and
awkward postures, particularly when operating heavier IOS de-
vices. These factors contribute to an elevated risk of work-related
musculoskeletal disorders among dental practitioners, exacerbat-
ing a well-documented occupational health issue [14].

Since the early 1980s, the implementation of robotics has
become increasingly prevalent across industries to minimize
human-induced variability and reduce operational time [15, 16].
More recently, dentistry has begun exploring the integration of
robotics to mitigate practitioner-dependent factors and enhance
clinical outcomes. In the dental context, where proximity be-
tween practitioner and patient is intrinsic, the use of cobots to

handle the IOS on behalf of the clinician holds considerable
promise. Such an approach offers several potential benefits such
as:

* Improving, stabilizing and standardizing the quality of intraoral scans by minimizing operator variability,

* Reducing the risk of musculoskeletal disorders by alleviating ergonomic strain associated with manual scanning,

* Optimizing practitioner efficiency by freeing up clinician time, particularly valuable in settings where skilled practitioners are in high demand or short supply,

* Enhancing educational outcomes by serving as a didactic tool for dental students to learn and refine IOS techniques within academic training environments.

Robotic technology applied in healthcare has made significant advances and now offers a broad range of applications [17, 18], the most common of which include patient assistance and monitoring, as well as supporting paramedical staff in non-critical tasks. More recently, the development of interventional robots designed to assist clinicians in performing safe and efficient procedures has emerged as a particularly dynamic and disruptive area of research. In the field of dental care, ongoing studies in oral rehabilitation and implantology are focusing on the development of robotic systems aimed at enhancing the precision of clinical interventions, especially in dental preparations [15].

Recent findings demonstrate that a poly-articulated robot—specifically, the UR5e cobot - trained to autonomously reposition itself within an in-vitro patient environment, can achieve accurate and repeatable positioning during drilling operations [19]. This performance has been validated even within the constrained spaces of the oral cavity, where adaptive control strategies have proven effective [20]. These promising results suggest that robotic systems could also be well suited for IOS, provided that the experimental biases introduced by robotic manipulation remain minimal. However, it remains to be demonstrated whether handling an IOS, a bulkier instrument than the tools employed in previous studies, is feasible within the confined space of the mouth and whether it can achieve outcomes comparable to those obtained manually by practitioners. Addressing this question requires a careful examination of how the quality of intraoral scans performed robotically, is assessed.

The objective of this article is to introduce an in vitro study
investigating the cobot-assisted intraoral digitization. Specifi-
cally, this study aims to evaluate and objectively compare the
quality and repeatability of digitization obtained via cobotized
scanning methods, thereby providing a foundation for future clin-
ical applications and integration into dental education. The arti-
cle is organized as follows: Section-2 presents the materials and
methods used to perform the intraoral scanning using the cobot.
Section-3 presents the results obtained from the trials. The article
then ends with the conclusion in Section-4.

## 2. MATERIALS AND METHODS

### 2.1. Dental artefact, IOS and cobot

This study investigates the digitization process of a mandibu-
lar model, specifically a lower dental arch, using a robotic-assisted
intraoral scanning system. A standardized anatomical mandible, commonly utilized in dental training, was selected to ensure reproducibility and consistency in the experimental setup. For this purpose, the STL file of the Frasaco ANA-4 anatomical model (hereafter referred to as the nominal mesh) was employed. The physical model was fabricated using a Form 3B Stereolithography (SLA) 3D printer (Formlabs, USA), utilizing Dental Model V2 resin (Formlabs). The printed mandible was fixed on an articulator set to an average mouth opening of 45 mm, in accordance with the mean maximal interincisal distance reported in the literature [21], in order to approximate realistic in vivo conditions. This assembly was subsequently positioned within the workspace of a cobot.

For digital acquisition, a Dexis IS 3800W wireless IOS was selected. The device, weighing 240 grams, is equipped with a standard tip and offers a field of view of 16 mm × 14 mm. Scanning was performed using the Dexis IS ScanFlow software platform.

A UR5e collaborative robot (Universal Robots) equipped with a 2F-85 Adaptive Gripper (Robotiq) was used to automate manipulation of the IOS. An overview of the configuration is shown below in Fig. 1. The UR5e offers a payload capacity of

<figure><img src="image_3.png" /><figcaption>FIGURE 1: THE CONFIGURATION SETUP</figcaption></figure>

5 kg, a working reach of 850 mm and six fully articulated joints capable of ±360° rotation at a maximum speed of ±180°/s. These specifications align well with the mass of the IOS and the spatial constraints typically encountered in dental clinical environments. In essence, a cobot operates under the supervision of a user. This is why we propose a cobot rather than a fully autonomous robot. The user or the healthcare staff will therefore be able to intervene and secure the scan in the event of a problem. In addition, the cobot has torque and force sensors integrated into its end-effectors. These sensors will enable it to detect in real time any abnormal force thresholds (caused, for example, by any jamming in the scanner during movement) that have already been set and to react accordingly by interrupting the movement and releasing the scanner from the oral cavity.

Prior to the scanning procedure, the cobot was programmed to autonomously identify the IOS, stored on its base, using its integrated Wrist Camera. The cobot was configured to execute a pick-and-place operation: grasping the scanner with the gripper,

returning it to its base and returning to its home position. These motion sequences were programmed manually via the 3-Position Enabling Device and the PolyScope programming environment.

## 2.2. Cobot scanning pathway

Two Occlusal-Vestibular-Lingual (OVL) scanning pathways, commonly used by practitioners, are tested on the cobot, as shown in Fig. 2. The first scanning pathway, shown in Fig. 2(a), begins at the second left molar. From there, it digitizes the occlusal surface of the mandible before shifting to the vestibular side (i.e. the surface adjacent to the lips). The pathway concludes on the lingual surface of the right side. The second scanning pathway, shown in Fig. 2(b), starts at the second right molar. It similarly progresses across the occlusal surface of the mandible, tilts toward the vestibular side and finishes on the lingual surface of the left side.

<figure><img src="image_4.png" /><figcaption>FIGURE 2: OVL COBOT PATHWAYS</figcaption></figure>

For both strategies, guide curves are generated in SolidWorks above the nominal mesh. These curves are designed to ensure smooth trajectories and are defined using control points positioned as follows:

* Above each tooth at an average height of 2 mm for the OVL pathway.

* At the foot of each tooth on the internal and external side of the gum and above the occlusal face for the S-shape pathway, with a distance range from 1 mm to 2 mm.

These configurations ensure both anatomical conformity and continuity of the trajectories for subsequent processing tasks.

## 2.3. Virtual experimental environment

RoboDK software is used to develop a comprehensive digital twin of the experimental setup. All components of the system, including the anatomical model, articulator, IOS, cobot and the support structure of the cobot, are either custom-designed using SolidWorks or sourced directly from the RoboDK library. Within this virtual environment, the IOS is pre-set in the gripper of the UR5e cobot as shown in Fig. 3(a).

The center of the IOS window is defined as the pilot point of the scanning trajectory. Guide curves, imported into RoboDK via the RoboDK Add-In for SolidWorks, establish the desired scanning pathways. The orientation of the IOS is manually configured to align with the specific anatomical features of interest.
<figure><img src="image_5.png" /><figcaption>FIGURE 3: VIRTUAL ROBODK ENVIRONMENT</figcaption></figure>

Additionally, a custom Python program has been adapted to simulate the IOS's field of vision during the digitization process, thereby enabling real-time virtual feedback of the scanning operation as shown in Fig. 3(b). As a result, during IOS operation, a virtual video stream is generated to verify that all teeth and relevant sections of the gum are theoretically captured as the scanner moves across the surface. Based on these input data, RoboDK simulates the motion of the UR5e cobot, maintaining constant scanning speeds across various regions of the mandible for the two predefined pathways. These simulations serve several critical purposes, especially:

* To adjust the IOS position and orientation relative to the mandible for optimal scanning coverage.

* To ensure that the scanning trajectory is smooth and continuous, minimizing disruptions.

* To identify and eliminate configurations that are problematic during operation, such as potential collisions between the UR5e and surrounding components.

For the validation of final scanning strategies, certain waypoints along the trajectory are repositioned or supplemented to eliminate crossover points that are incompatible with effective scanning. Moreover, surface normal vectors are refined in regions where the initial pathway does not guarantee complete digitization of the mandible, particularly in areas with limited visibility. An optimal experimental setup is also determined, taking into account the operational constraints of the cobot and capabilities. This optimization process considers:

* Accessibility of the target anatomy within the workspace of the cobot,

* Minimization of unnecessary cobot movements by reducing stoppage times and stopping distances, thereby enhancing scanning efficiency [22].

## 2.4. Files recording and post-processing

The two strategies of cobot-assisted digitization are each repeated ten times under identical experimental conditions (temperature, lighting). All resulting datasets are exported as STL files

in both medium and high resolutions. The medium-resolution setting, recommended by the manufacturer for orthodontic treatments, corresponds to an average inter-vertex distance of 150 µm. In contrast, the high-resolution setting, advised for prosthetic applications, corresponds to an average inter-vertex distance of 70 µm. In total, 40 STL files are generated, as follows: (2 digitization pathways × 2 resolution settings = 4 experimental groups) × 10 trials = 40 files. The four experimental groups are named as indicated in Table 1

<table><caption>TABLE 1: EXPERIMENTAL GROUPS</caption><thead><tr><td>Name</td><td>Code</td><td>Begin side</td><td>Resolution</td></tr></thead><tbody><tr><td>OVL Left Medium</td><td>L_M</td><td>Left</td><td>Medium</td></tr><tr><td>OVL Left High</td><td>L_H</td><td>Left</td><td>High</td></tr><tr><td>OVL Right Medium</td><td>R_M</td><td>Right</td><td>Medium</td></tr><tr><td>OVL Right High</td><td>R_H</td><td>Right</td><td>High</td></tr></tbody></table>

Zeiss Inspect optical 3D 2025 software is used to compare the nominal mesh with all 40 STL files. The scanner-generated mesh had its normal vectors automatically oriented during processing to ensure consistency. To prevent potential biases in subsequent deviation calculations, any incorrectly oriented normal vectors were manually inverted. Each STL file is subsequently superimposed onto the nominal mesh to enable comparative analysis. The alignment procedure is performed in two sequential steps. First, an automatic global pre-alignment is conducted between the nominal mesh and the scanned mesh to roughly superimpose the two datasets. Second, a refined local best-fit alignment is applied, restricted to mesh points where the calculated Euclidean distance following the pre-alignment stage is less than 0.01 mm. This two-step process ensures both robust initial positioning and high-precision matching in regions of close proximity. To eliminate most of the gingival regions from the scanned models — elements considered irrelevant in this study since the restorative treatment focuses exclusively on teeth — a cutting plane must be defined. To construct such a plane, several studies have proposed placing landmarks manually (i.e. markers) on the mesh to establish a reference plane. This is typically achieved by positioning 3 points, subsequently applying a parallel plane cut [11, 12, 23–25]. However, to minimize potential bias associated with manual point placement, this study employs a geometrically defined plane that passes through three anatomically consistent landmarks. Specifically, one point is positioned at the inter-incisal region superior to the labial frenulum, while the remaining two points are located beneath the vestibular surfaces of the second molars bilaterally. To ensure maximal repeatability and reproducibility, the coordinates of these three points are determined and fixed on the nominal mesh, as this surface remains unchanged throughout the processing. Once defined, this plane is used to cut both the scanned and the nominal meshes. The mean and standard deviation of the four STL experimental groups characteristics (Number of vertices, Number of facets, Total surface, Points density) are calculated. Three-dimensional (3D) color maps are subsequently generated by orthogonally projecting the vertices of the cut scanned mesh onto the corresponding cut nominal mesh. To ensure the relevance of the comparison, vertices
with a projected distance greater than 0.1 mm are excluded from the analysis. For each color map, two quantitative indicators are automatically computed by Zeiss Inspect software:

* The area of valid distance (AVD) i.e. the area of the surface of comparison

* The integrated absolute distance (|ID|) which represents the absolute value of the inscribed volume of the surface comparison i.e. the sum of absolute volumetric deviation between the two meshes.

To contextualize the surface coverage, R<sub>mesh</sub> i.e. the ratio between the AVD of each scanned mesh and the surface of the cut nominal mesh (S = 3567.68 mm²) is calculated. Although R<sub>mesh</sub> provides a preliminary comparative indicator of surface coverage, but it does not in itself, sufficiently characterise the accuracy of the mesh files. Indeed, variations in the scanned mesh area can influence AVD values. To compensate for this and enable robust comparison across models, the integrated absolute distance (|ID|) is normalized relative to the AVD. Accordingly, the weighted normalized distance (wND), as defined by Fasham et al.[26] as the ratio between |ID| and the product AVD × R<sub>mesh</sub>, is computed. This metric expresses the discrepancies between the two meshes in micrometers (μm); : low wND values indicate a closer correspondence between the scanned STL and the nominal mesh. As such, the wND can be considered as analogous indicator to trueness, as defined in ISO 5725-1:2023 [10]. A schematic overview of the entire methodological workflow is presented in Fig. 4. Finally, AVD, R<sub>mesh</sub> and wND values obtained for the four batches are statistically compared using a Kruskal-Wallis test with a P value of 5%.

# 3. RESULTS AND DISCUSSIONS

## 3.1. STL characteristics

Table 2 summarizes the experimental groups' key characteristics of the digitized STL files.

<table><caption>TABLE 2: STL FILES CHARACTERISTIC FOR EACH EXPERIMENTAL GROUPS</caption><thead><tr><th>Group code</th><th>L_M</th><th>L_H</th><th>R_M</th><th>R_H</th></tr></thead><tbody><tr><td>Number of vertices</td><td>46811 ±1054</td><td>94161 ±2138</td><td>46449 ±301</td><td>92762 ±3741</td></tr><tr><td>Number of facets</td><td>91954 ±2089</td><td>185494 ±4251</td><td>91310 ±591</td><td>182583 ±7451</td></tr><tr><td>Total digitized surface (mm²)</td><td>3325 ±6.87</td><td>3345 ±6.52</td><td>3315 ±3.96</td><td>3352 ±4.52</td></tr><tr><td>Points density (pts/mm²)</td><td>14.08 ±0.29</td><td>28.41 ±0.61</td><td>14.01 ±0.09</td><td>27.68 ±1.10</td></tr></tbody></table>

### 3.1.1. Mean values: Medium vs High resolution
Regarding the number of vertices and facets, it is noteworthy that increasing the resolution from medium to high results in an approximate doubling of these values, whatever the position of the starting point. This increase can be attributed to the fact that enhancing the recording resolution inherently leads to a denser mesh, characterized by a greater number of vertices and facets.

<figure><img src="image_6.png" /><figcaption>FIGURE 4: METHODOLOGICAL WORKFLOW</figcaption></figure>

The observed variations, which follow a ratio close to 2, are fully consistent with the manufacturer's recording resolution specifications provided for the IOS (150/70 ≈ 2). Moreover, the digitized surface area remains essentially constant despite the increase in resolution, resulting in a doubling of point density as the resolution transitions from medium to high (from ≈14 pts/mm² to ≈28 pts/mm²).

### 3.1.2. Mean values: Left vs Right starting side
The number of vertices and facets remains similar, regardless of the position of the starting point. The differences are within experimental error. In the same way, digitized surface areas are nearly identical for a given resolution level. Nevertheless, the standard deviation of the total digitized surface obtained with a left starting point is about 40-50% higher than those with a right starting
point. Point densities for left and right, at the same resolution, are also essentially identical (14.08 vs 14.01 and 28.41 vs 27.68 pts/mm²).

**3.1.3. Standard deviations** The standard deviation of the number of vertices or facets, for strategies with a starting point on the left, remains constant at around 2.23% of the mean value. Surprisingly, for trajectories starting on the right, these standard deviations vary. It only represents 0.65% of the mean value in medium resolution but 4.03% in high resolution. Focusing on the number of vertices and facets in each experimental group, we notice that 2 high-resolution trials (trials 1 and 3) have values 10% lower than the other results. This also increases the standard deviation. Even though all the tests were carried out under the same experimental conditions of lighting and temperature and in a very short time (25 minutes to carry out an experimental group of 10 trials), it may be possible that these 2 trials were disturbed by an external phenomenon. If we exclude these 2 trials and recalculate the mean and standard deviations on the other trials, we obtain number of vertices of 94494±916 and number of facets of 186033±1826. Thus, the standard deviation remains constant in proportion and represents 0.97% of the mean value.

**3.2. 3D color map analysis**

Most of the peripheral teeth areas exhibit a good match between the nominal mesh and the STL. The grey regions visible on these 3D color maps indicate areas where the distance between the lingual surfaces of the two meshes exceeds 0.1 mm. In contrast, the distance between the two meshes on the vestibular side remains below 0.1 mm. This pattern highlights that the superimposition of the two meshes was aligned on the vestibular side of the teeth. This phenomenon is most pronounced on the incisor teeth. Indeed, the vestibular surfaces are more easily digitized by the cobot equipped with an IOS. This can be attributed to the geometric accessibility of the surfaces: convex shapes, such as those found on the vestibular side, are inherently easier to scan than concave shapes, which present greater challenges for accurate data acquisition. Nevertheless, during chairside digitization, the presence of the lips can further complicate the cobot scanning process. In the same way, the incisive edges and some cuspid tips are grey regions (distance > 0.1 mm) due to the difficulty of scanning thin edges. Finally, the regions at the tooth-gingiva junction exhibit relatively large discrepancies between the meshes. This is particularly problematic in restorative dentistry, as this area demands a high level of precision to ensure an optimal fit between the prosthetic restoration and the prepared tooth structure. Fig. 5 illustrates some color maps for each experimental group.

**3.3. Files quality evaluation**

The indicators calculated from 3D color maps (AVD, R<sub>mesh</sub>, and wND) for the four experimental groups (grey areas are also excluded) are given in Fig. 6.

**3.3.1. Medium vs High resolution** There is a significant difference due to the IOS resolution (regardless of the starting point) for AVD and R<sub>mesh</sub>. AVD is larger for high-resolution digitization and the same applies to R<sub>mesh</sub>. One explanation is that increasing the resolution means that the digitized surface fits

the real surface better, as the facets are smaller and the chord error is therefore reduced. There is no difference for wND due to the IOS resolution: Increasing the resolution does not increase the adequacy between STL mesh and nominal mesh i.e. the quality of digitization. The standard deviation, however, increases when resolution is high: σ<sub>L-M</sub> = 0.12 µm vs σ<sub>L-H</sub> = 0.16 µm and σ<sub>R-M</sub> = 0.09 µm vs σ<sub>R-H</sub> = 0.19 µm. This also means that a high resolution brings more discrepancies to the mesh.

<figure><img src="image_7.png" /><figcaption>FIGURE 5: 3D COLOR MAPS EXAMPLES FOR EACH EXPERIMENTAL GROUP</figcaption></figure>
**3.3.2. Left vs Right starting side** When the effect of the starting point is compared (independent of the IOS resolution), a difference between the groups can be observed for the three criteria extracted from the 3D color maps. AVD has a lower mean value when scanning starts from the left. This means that the area used to compare with the surface of the cut nominal mesh depends on the choice of the starting point. This is because the IOS does not fly over the mandibular surfaces in the same way and also because the mandible is not a symmetrical element: the coordinates of the waypoints along the trajectory are identical, but the IOS orientations above the mandible are different. So, if IOS orientations changes this means that the positions and the joint angles adopted by the cobot when scanning the mandible have also changed. Then we can imagine that those used for a left-hand start are more restrictive and generate more errors. R<sub>mesh</sub> also has a lower mean value when scanning is starting from the left since it depends on the surface of the cut nominal mesh which remains constant and on AVD, which decreases. At the same time wND decreases when digitization starts from the right: wND<sub>L-M</sub> = 9.07 µm vs wND<sub>R-M</sub> = 8.887 µm and wND<sub>L-H</sub> = 9.125 µm vs wND<sub>R-H</sub> = 8.873 µm. Starting the digitization on the right side improves the dimensional precision of the STL file (quantitatively represented by wND). It should be noted that while the mean values obtained for wND with a right-hand scanning start, the standard deviations are higher.

In this study, the scanning time with the cobot was similar to that obtained manually (i.e., around 2min 30sec). In a single-chair practice, a consultation takes around 20 to 30 minutes. During this period, scanner use would therefore represent only 10-20% of total chair occupancy time. Consequently, the actual frequency of use between two patients will be lower than that tested experimentally (series of 10 scans performed in 25 min). In the case of a cobot shared between several chairs (the cobot performing scans in a chain), the frequency of use will also be lower, as cleaning and disinfection processes of the equipment are also required between two patients. Thus, according to our perspective, increasing the work rate of the cobot does not seem necessary, unlike evaluating the quality of the files.

# 4. CONCLUSIONS AND PERSPECTIVES

This study focuses on evaluating the ability of a Cobot to obtain STL files of equal or higher quality than those obtained by a practitioner. The big data approach does not seem necessary at this stage, but it will be useful for future development with the cobot. Several studies will be required on learning how to recognize the external anatomical elements of a patient (nose, open mouth, eyes, etc.) in order to enter the oral cavity; as well as internal anatomical elements (tooth typology, number, dial, etc.) in order to personalize the trajectory for each patient. Optimization of scanning trajectories by a collaborative robot would further enhance the reliability of STL file quality. These advances open up promising prospects for improving access to healthcare in medical deserts through telemedicine. They are also a major asset for training future practitioners through realistic digital models. These developments also mark the beginning of concrete clinical applications. Finally, it was also ensured that when deploying the cobot in a real-life situation, the setup will have to comply

<figure><img src="image_8.png" /><figcaption>FIGURE 6: 3D COLOR MAPS ATTRIBUTES</figcaption></figure>

with the legal and regulatory requirements defined for the marketing of medical devices (European regulations 2017/745/EC, CE marking, etc.).

# ACKNOWLEDGMENTS

The authors would like to kindly thank Dexis for supplying the IS 3800 IOS.

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