Fingertip Piezoelectric Tactile Sensor Array for Roughness Encoding Under Varying Scanning Velocity

Abstract

Roughness is a primary perceptual dimension of surface texture and plays an important role in human and robotic tactile object perception. In human, the magnitude estimates of roughness are independent of scanning velocity. On the other hand, artificial roughness encoding had to work under known scanning velocity or carry out stereotyped exploratory movement with almost the same velocity in each step action. We here presented a new fingertip piezoelectric tactile sensor array with a density similar to human Pacinian Corpuscles and capable of roughness eliciting from exploration. A novel characteristic variable $\Delta tf_{\mathrm {prin.}}$ , which is product of response time interval between adjacent sensor units and the principal frequency of vibration, is first time proposed for roughness recognition. And the new characteristic variable is sensitive to surface roughness but independent of the scanning velocity. With the proposed characteristic variable, seven stimuli with a spatial period of 300, 400, 440, 480, 600, 800, and $1000~\mu \text{m}$ were successfully distinguished under varying scanning velocity exploration, with an identification accuracy of 99.93%. Above used velocity range is from 10 to 150 mm/s, which can fully cover velocities in common application neurophysiologic studies and human natural exploration. Repeatability is comparatively good with average relative standard deviation of only 1.31%. Furthermore, experiments with elliptical grating verified that this roughness encoding method also fits for the texture with two-dimensional pattern. In addition, texture amplitude detection experiments were performed and results show that the vibration amplitude ( $\text{A}_{prin.}$ ) grows linearly when the texture amplitude ( $h$ ) changes from 25 to $300~\mu \text{m}$ .