A MEMS Tactile Sensor with Fingerprint-Like Array of Contactors for High Resolution Visualization of Surface Distribution of Tactile Information

Abstract

In the present report, we have developed a tactile sensor with fingerprint-like array of contactors for obtaining the surface distribution of tactile information in high spatial resolutions. Six high resolution sensing modules of contactors with biaxial detectors were integrated in line at a pitch of 500 μm, the typical pitch of fingerprint ridges. Each sensing module independently detected the micro surface shape and locally generated frictional force on the object surfaces. Mechanical analysis of the fabricated sensors showed good sensitivities and highly linear responses. Consequently, the measured detection resolutions of surface shape and frictional force were 0.17 μm and 9.9 μN, respectively. The experimental performance evaluation of fabricated sensor was measured in the distribution of tactile information by sweeping the sensor with a yaw angle. Additionally, the 3D surface shape of weave structure and surface distribution of frictional force in a woven fabric with 0.4 mm pitch of threads in high spatial resolution was clearly visualized/observed. Moreover, the directionality of tactile information of the fabric surface distribution was successfully realized using the tactile sensor with the array of contactors by sweeping in different directions.

Improved Reproducibility of Deflection Control Process for Cantilever‐Type MEMS Tactile Sensors

“…With these backgrounds, many studies related to tactile sensors are currently being conducted [6,7]. Various sensing methods have been studied for tactile sensors, typically strain gauge type [8,9], piezoresistive type [10,11], capacitive type [12,13], and optical type [14,15].…”

“…Each method has its advantages and disadvantages in terms of sensing performance and manufacturing. In terms of the structure of the sensing element, the cantilever type [9,10], beam type [6,7], and diaphragm type [11,13] have also been developed, each having different characteristics.…”

Neural-Network-Based Tactile Perception System Using Ultrahigh-Resolution Tactile Sensor

“…Since the core of the neural network is feature extraction and both regression and classification models are realized with almost identical architectures, the above methods are considered effective approaches for tactile perception as regression problems too. Under these circumstances, our group has developed a tactile sensor that mimics a single fingerprint on a human fingertip [12], [13]. The tactile sensor can measure surface shape and frictional force at the same point at the same time from contacting object and has an ultrahigh resolution for force, displacement, and horizontal spatial, which are more than that of a human fingertip [14], [15].…”