Rapid manufacturing of color-based hemispherical soft tactile fingertips

Conference paper (2022)

Authors

R.B.N. Scharff Istituto Italiano di Tecnologia
D. Boonstra Human-Robot Interaction - Mechanical, Maritime and Materials Engineering
L. Willemet Human-Robot Interaction - Mechanical, Maritime and Materials Engineering
X. Lin Carl Zeiss Meditec AG
M. Wiertlewski Human-Robot Interaction - Mechanical, Maritime and Materials Engineering
Research Group
Human-Robot Interaction (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 R.B.N. Scharff, D. Boonstra, L. Willemet, X. Lin, M. Wiertlewski
DOI
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https://doi.org/10.1109/RoboSoft54090.2022.9762136
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Human-Robot Interaction

Abstract

Tactile sensing can provide access to information about the contact (i.e. slippage, surface feature, friction), which is out of reach of vision but crucial for manipulation. To access this information, a dense measurement of the deformation of soft fingertips is necessary. Recently, tactile sensors that rely on a camera looking at a deformable membrane have demonstrated that a dense measurement of the contact is possible. However, their manufacturing can be time-consuming and labor-intensive. Here, we show a new design method that uses multi-color additive manufacturing and silicone casting to efficiently manufacture soft marker-based tactile sensors that are able to capture with high-resolution the three-dimensional deformation field at the interface. Each marker is composed of two superimposed color filters. The subtractive color mixing encodes the normal deformation of the membrane, and the lateral deformation is found by centroid detection. With this manufacturing method, we can reach a density of 400 markers on a 21 mm radius hemisphere, allowing for regular and dense measurement of the deformation. We calibrated and validated the approach by finding the curvature of objects with a threefold increase in accuracy as compared to previous implementations. The results demonstrate a simple yet effective approach to manufacturing artificial fingertips for capturing a rich image of the tactile interaction at the location of contact.