PHOTOTOUCH: SOFTWARE AND HARDWARE METHODS FOR PHOTOELASTIC TACTILE SENSOR

Abstract

This thesis introduces a novel approach for predicting force at the fingertips of tendon driven robotic fingers using the photoelastic effect. The core of this technique lies in the detection of stress-induced birefringence in silicone, which becomes visible as distinctive fringe patterns under polarized light. These patterns emerge when tendons, embedded within a silicone matrix, apply forces that compress the material. They are not merely visual markers, but also contain valuable data about the applied forces and their distribution within the silicone. To extract and utilize this data effectively, Convolutional Neural Network (CNN) was employed, specially designed to analyze and interpret the intricate fringe patterns. Thousands of these images were captured in various force application states, resulting in a substantial dataset for the CNN to learn from. The finger was used to show the position control and force control capabilities of photoealastic tactile sensor. It was successful in following the sine wave during force control mode, with RMSE of 0.59 N at a frequency of 0.05 Hz. This document will further delve into relevant literature, elaborate on the research methodology, describe the experimental setup, and present preliminary findings. Col lectively, these components unveil the immense potential of the proposed system in augmenting the tactile capabilities of robotic appendages, with far-reaching implica tions for the fields of robotics and automated systems.