Development of a Capacitive-Piezoelectric Tactile Force Sensor for Static and Dynamic Forces Measurement and Neural Network-Based Texture Discrimination

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Maira Ehsan Mughal;Muhammad Rehan;Muhammad Mubasher Saleem;Masood Ur Rehman;Hamid Jabbar;Rebecca Cheung
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引用次数: 0

Abstract

Taking inspiration from human tactile system, a sensitive biomimetic multimodal tactile sensor for discrimination of static and dynamic forces is presented in this article. The multimodal tactile sensor has a piezoelectric-capacitive tandem for responding to the dynamic and static forces, respectively. Sensor can cater to normal direction dynamic force signals with a piezoelectric part operating in the ${d}_{{33}}$ mode and static force with a capacitive part. The capacitive sensing part has a unique configuration with a top electrode and two sets of differential pairs electrodes for the force measurement in x and y shear axis and one electrode for normal force measurement. The experimental characterization of the sensor was performed for static, quasi-static, and dynamic forces. Along with the static forces, the sensor was also able to cater to dynamic forces up to 60 Hz. The force sensitivity of the sensor for the normal force is 0.084 pF/N and 0.035 V/N from the capacitive and piezoelectric part, respectively, for a force range of 10 N. Also, in the shear X- and Y-directions, the sensor exhibited a sensitivity of 0.027 and 0.029 pF/N, respectively, in the force range of 1.2 N. Through the vibrotactile data, the sensor showed an ability to discriminate between two texture samples through a neural network classifier. The presented sensor owing to its dimension, performance, and capabilities can find its application in minimally invasive robotic surgery, robotics, wearable devices, and prosthetics.
用于静、动态力测量和基于神经网络的纹理识别的电容式压电触觉力传感器的研制
本文以人体触觉系统为灵感,设计了一种灵敏的仿生多模态触觉传感器,用于动静力的识别。多模态触觉传感器采用压电-电容串联结构,分别响应动态和静态力。传感器采用${d}_{{33}}$工作模式的压电元件和静力元件,可满足法向动态力信号。电容传感部分具有独特的配置,具有顶部电极和两组用于x和y剪切轴上的力测量的差分对电极,以及一个用于法向力测量的电极。对传感器进行了静态、准静态和动态力的实验表征。除了静态力外,该传感器还能够适应高达60 Hz的动态力。在10 N的力范围内,该传感器对来自电容和压电部分的法向力的灵敏度分别为0.084 pF/N和0.035 V/N。在剪切X和y方向上,在1.2 N的力范围内,该传感器的灵敏度分别为0.027和0.029 pF/N。通过振动触觉数据,该传感器显示了通过神经网络分类器区分两种纹理样本的能力。由于其尺寸,性能和能力,该传感器可以在微创机器人手术,机器人技术,可穿戴设备和假肢中找到其应用。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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