High-Resolution 3-Dimensional Contact Deformation Tracking for FingerVision Sensor With Dense Random Color Pattern

Du, Yipai; Zhang, Guanlan; Wang, Michael Yu

doi:10.1109/lra.2021.3061306

Cited by 32 publications

(9 citation statements)

References 26 publications

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“…Considering that the sensor is of medium accuracy, this result is a very accurate identification rate (Du et al., 2021 ). The robot developed by T6-ER1, used in physical education classes, focused on e-sports, caused a relevant secondary result.…”

Section: Resultsmentioning

confidence: 99%

Applying Gestalt approach as a method for teaching computer science practice in the classroom: A case study in primary schools in Brazil

et al. 2022

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The use of information technology in the academic environment has grown. Building different didactic techniques to help students learn and practice with Information Technology (IT) resources is common. However, applying these techniques does not necessarily mean that students may acquire knowledge. The differential idea of this work is to create an approach in which students are protagonists and not just absorbers of IT. Based on this perspective, we applied a Gestalt approach to assist students in practicing these technological resources. They produce new hardware and software tools during classes based on their personal needs and worldviews. We analyzed applications of this novel way of computer science teaching in three different schools. It was possible to observe greater motivation from the students to experience new knowledge from technological resources. The common aspect was that solutions were conceived and developed from students’ needs. The development followed a Gestalt approach, which combines the idea of form and imagination. Thus, with this approach, reactivity towards IT was reduced. It helped construct technological tools to acquire propaedeutic knowledge.

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Section: Resultsmentioning

confidence: 99%

Applying Gestalt approach as a method for teaching computer science practice in the classroom: A case study in primary schools in Brazil

et al. 2022

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“… Optical flow method [28], [37]  Finite element mode [63], [44]  Neural network [58], [53] mainly using the machine learningbased approaches  Speckle detection [77], [80]  Feature enhancement [81], [82] mainly using the physical model-based approaches  Stereo vision [25], [94]  Virtual stereo vision [23], [26] mainly using the physical model-based approaches  Contact area [24]  2D force distribution [17]  Slip field [79]  Contact area [20]  2D force distribution [86]  Slip field [21]  Contact area [91]  Friction coefficient [23]  2D force distribution [97] 3D tactile perception  Geometric features [46]  3D geometry [18]  3D force distribution [63]  Geometric features [82]  3D geometry [82]  3D force distribution [87]  Geometric features [ commonly used in the field of visuotactile sensing. By using a camera to photograph the marks prepared on the sensor contact elastomer, a tactile image containing the position change of the markers can be obtained, and the tactile information can be further obtained by post-processing and analyzing the tactile image.…”

Section: Common Technologiesmentioning

confidence: 99%

Marker Displacement Method Used in Vision-Based Tactile Sensors—From 2D to 3D: A Review

Li¹,

Li²,

Jiang³

2023

Preprint

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<p>This article presents a detailed review and categorizing of the marker displacement method (MDM) used in vision-based tactile sensors. Vision-based tactile sensors have been proven to be a promising solution for robot tactile perception. Among such sensors, MDM is one of the most commonly used contact characterization and extraction methods. It uses visual approaches to obtain contact deformation and achieve multimodal tactile perception using physical models and post-processing algorithms. In recent years, many tactile sensors using MDM have been developed. However, the existing research does not strictly distinguish between the different types of methods but is uniformly grouped into MDM. Without differentiation, there might be a lack of systematic and comprehensive guidance in analyzing and optimizing the characteristics of MDM and selecting the most suitable method. This article is the first to classify MDM into three typical categories based on the dimensionality perspective: 2D MDM, 2.5D MDM, and 3D MDM. 2D MDM relies only on the monocular camera to acquire the marker array’s 2D displacement field. 2.5D MDM supplements 2D MDM with selected indirect features reflecting the location of the markers in the third dimension. 3D MDM employs a multi-camera system and can obtain the 3D displacement field using the stereo vision method common. Based on the latest literature, we compare the principles, characteristics, advantages and disadvantages, and applications of the three ways in detail. This work can provide a valuable reference for researchers interested in applying MDM in fields such as vision-based tactile sensors.</p>

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“…From the deformation image, Tactip deduces the contact contour and forces. The Fingervision [28] sensor uses optical flow to extract the deformation vector field from a random dense pattern. Using a learningbased method, it achieves online contact feature extraction with high accuracy and frequency.…”

Section: Vision-based Tactile Sensormentioning

confidence: 99%

Viko 2.0: A Hierarchical Gecko-inspired Adhesive Gripper with Visuotactile Sensor

Pang¹,

Wang²,

Mak³

et al. 2022

Preprint

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Robotic grippers with visuotactile sensors have access to rich tactile information for grasping tasks but encounter difficulty in partially encompassing large objects with sufficient grip force. While hierarchical gecko-inspired adhesives are a potential technique for bridging performance gaps, they require a large contact area for efficient usage. In this work, we present a new version of an adaptive gecko gripper called Viko 2.0 that effectively combines the advantage of adhesives and visuotactile sensors. Compared with a non-hierarchical structure, a hierarchical structure with a multi-material design achieves approximately a 1.5 times increase in normal adhesion and double in contact area. The integrated visuotactile sensor captures a deformation image of the hierarchical structure and provides a real-time measurement of contact area, shear force, and incipient slip detection at 24 Hz. The gripper is implemented on a robotic arm to demonstrate an adaptive grasping pose based on contact area, and grasps objects with a wide range of geometries and textures.

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High-Resolution 3-Dimensional Contact Deformation Tracking for FingerVision Sensor With Dense Random Color Pattern

Cited by 32 publications

References 26 publications

Applying Gestalt approach as a method for teaching computer science practice in the classroom: A case study in primary schools in Brazil

Applying Gestalt approach as a method for teaching computer science practice in the classroom: A case study in primary schools in Brazil

Marker Displacement Method Used in Vision-Based Tactile Sensors—From 2D to 3D: A Review

Viko 2.0: A Hierarchical Gecko-inspired Adhesive Gripper with Visuotactile Sensor

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