Design and Fabrication of a Vision-based Tactile Sensor

Abstract

The sense of touch is an essential element for robotic arms in manipulating objects and their optimal interaction with the environment. Tactile sensors provide extensive quantitative and qualitative information about the contact between the sensor and the target object. With the advancements in machine vision and image processing, vision-based tactile sensors have been introduced as a more versatile sensing technology. This paper covers the steps of design, fabrication, and calibration of a vision-based tactile sensor. A brief introduction to optical simulation is presented and the convolutional neural networks model is used as the force estimation model for the simulation and experimental data. The fabricated sensor is capable of detecting normal-axis forces up to 5 newtons and tangential forces in two directions up to slip (approx. 1.4 times the applied normal force). The mean square error obtained for the built sensor is equal to 0.038 newtons in the vertical axis and 0.0033 and 0.0039 newtons in the two tangential axes on all the experimental data.