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

Maira E. Mughal, Muhammad Rehan, Muhammad Mubasher Saleem, Masood Ur Rehman, Hamid Jabbar, Rebecca Cheung

发表年份
2025
引用次数
9

摘要

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${d}_{{33}}$ </tex-math></inline-formula> 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.

关键词

Capacitive sensingTactile sensorTexture (cosmology)PiezoelectricityArtificial neural networkAcousticsMaterials scienceComputer scienceArtificial intelligenceElectrical engineering

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