DotView: A Low-Cost Compact Tactile Sensor for Pressure, Shear, and Torsion Estimation
Haoran Zheng, Yongbin Jin, Hongtao Wang, Pei Zhao
Abstract
Tactile sensation is one of the most important sensing methods of humans, which helps humans complete complex decisions and actions by itself or in cooperation with other sensations. Similarly, tactile sensors are also crucial for robots to interact with the environment and fulfill dexterous object manipulation. In this study, we present DotView, a compact, low-cost, and high-performance tactile sensor that utilizes a biomimetic micro-structured soft layer to generate rich, high-resolution tactile images captured by a commercialized capacitive sensing array, which exhibits sensitive and robust responses under pressure, shear and torsion applied by different indenters. Furthermore, we utilize a neural network to estimate multiaxial contact forces and torques, and achieve good accuracy in different contact geometries. Finally, we demonstrate the capability of DotView in a dexterous manipulation scenario by performing the task of guiding a tool through a maze with only tactile feedback.