吉林大学学报(信息科学版) ›› 2026, Vol. 44 ›› Issue (3): 543-550.

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基于压电薄膜传感器的触觉信息采集系统

辛 毅,刘 宁,翟芸生,宋金洋   

  1. 吉林大学仪器科学与电气工程学院,长春130061
  • 收稿日期:2025-04-01 出版日期:2026-06-02 发布日期:2026-06-02
  • 作者简介:辛毅(1981— ), 女, 长春人, 吉林大学教授,博士生导师,主要从事传感器与微弱信号检测研究,(Tel)86-13154303263 (E-mail)yixin@ jlu. edu. cn。
  • 基金资助:
    国家重点研发计划基金资助项目(2022YFE0103800); 吉林大学大学生创新基金资助项目(202310183259)

Tactile Information Acquisition System Based on Piezoelectric Film 

XIN Yi, LIU Ning, ZHAI Yunsheng, SONG Jinyang   

  1. College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
  • Received:2025-04-01 Online:2026-06-02 Published:2026-06-02

摘要: 为了使仿生机械模拟人类利用触觉感知物体表面信息依据PVDF(Polyvinylidene Fluoride)压电薄膜的压电效应与热释电效应设计并搭建了一套多触觉信息采集系统, 实现了对物体硬度、粘度、湿度、粗糙度以及温度等多种触觉信息的采集和识别。该采集系统将PVDF压电薄膜粘贴在橡胶半球表面制作触觉传感器, 使用单片机控制升降台和丝杠滑台带动物体按压和摩擦触觉传感器有效采集到物体的触觉信息。 通过对采集信号的特征值进行分析, 建立了粘度和温度的数学模型, 粘度和温度灵敏度分别为-2 730 V/m2 0. 984 mV/°C。 使用方差有效表征了物体的硬度, 通过建立BP(Back Propagation)神经网络, 实现对湿度和粗糙度90%以上预测成功率。

关键词: PVDF压电薄膜, 触觉智能, 触觉信息采集, BP神经网络, ADAM算法

Abstract: In order to make bionic machine simulate human sense of touch to perceive the surface information of objects, according to the piezoelectric effect and pyroelectric effect of PVDF ( Polyvinylidene Fluoride) piezoelectric film, a set of multi-tactile information acquisition system is designed and built, realizing the collection and recognition of various tactile information such as hardness, viscosity, humidity, roughness and temperature of objects. The PVDF piezoelectric film is pasted on the surface of the rubber hemisphere to make a tactile sensor, a single-chip microcomputer is used to control the lifting table and the lead screw slide table to drive the object to press and friction the tactile sensor, effectively collecting the tactile information of the object, and establisheing a mathematical model of viscosity and temperature by using the eigenvalues of the collected signal for analysis. The viscosity and temperature sensitivity are-2 730 V/ m2 and 0. 984 mV/ °C respectively, the hardness of the object is effectively characterized by variance, and the success rate of predicting humidity and roughness of more than 90% is achieved by establishing a BP(Back Propagation) neural network.

Key words: polyvinylidene fluoride ( PVDF) piezoelectric film, tactile intelligence, tactile information acquisition, back propagation (BP) neural network, adaptive moment estimation

中图分类号: 

  • TN304