Effects of Sensing Tactile Arrays, Shear Force, and Proprioception of Robot on Texture Recognition

Abstract

In robotics, tactile perception is important for fine control using robot grippers and hands. To effectively incorporate tactile perception in robots, it is essential to understand how humans use mechanoreceptors and proprioceptors to perceive texture. Thus, our study aimed to investigate the impact of tactile sensor arrays, shear force, and the positional information of the robot’s end effector on its ability to recognize texture. A deep learning network was employed to classify tactile data from 24 different textures that were explored by a robot. The input values of the deep learning network were modified based on variations in the number of channels of the tactile signal, the arrangement of the tactile sensor, the presence or absence of shear force, and the positional information of the robot. By comparing the accuracy of texture recognition, our analysis revealed that tactile sensor arrays more accurately recognized the texture compared to a single tactile sensor. The utilization of shear force and positional information of the robot resulted in an improved accuracy of texture recognition when using a single tactile sensor. Furthermore, an equal number of sensors placed in a vertical arrangement led to a more accurate distinction of textures during exploration when compared to sensors placed in a horizontal arrangement. The results of this study indicate that the implementation of a tactile sensor array should be prioritized over a single sensor for enhanced accuracy in tactile sensing, and the use of integrated data should be considered for single tactile sensing.

Simultaneous Velocity and Texture Classification from a Neuromorphic Tactile Sensor Using Spiking Neural Networks

“…The integration of our texture and Vel_Pro f classifiers into a combined tactile system is a logical future step for this work. Prior studies have demonstrated how combining both proprioceptive and tactile senses using Neural Networks (NNs) generally leads to an increase in texture classification performance [22,28]. Alternative approaches using reservoir computing paradigms could also be explored due to their largely interconnected neuronal structure mimicking areas of the brain [55].…”

“…This methodology creates detailed tactile images of the texture surface but does not examine the surface during movement or the application shear forces, an important effect during tactile interactions that we seek to investigate here. Yang et al [22] highlights the importance of these shear forces when identifying texture using tactile sensors.…”

Healing Function for Abraded Fingerprint Ridges in Tactile Texture Sensors

“…Referring to this standard, as shown in Figure 8, the SNR dropped below 20 dB after 500 scans for λ = 1 mm, whereas the SNR remained above 20 dB even after 1000 scans for λ = 2 mm. One potential application of the tactile texture sensor is the autonomous classification of textures [43][44][45]. Therefore, the necessary sensitivity levels should be discussed in the context of textural classification in future studies.…”

Recent Advances in Self-Powered Tactile Sensing for Wearable Electronics

“…In the Information Era, data acquisition and processing are inseparable from the participation of sensors. They are widely used in robotics [ 2 , 3 , 4 , 5 ], motion monitoring [ 6 , 7 , 8 , 9 ], medical, human–machine interaction [ 10 ], and other fields [ 11 , 12 , 13 ]. However, traditional tactile sensors rely on external power sources for their power supply, which can lead to problems such as large system size, high cost, and difficult maintenance.…”