Research on load recognition of FBG tactile 
sensing array based on CNN

SHEN Zhihui; XU Yiming; LU Guan

Chinese

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Research on load recognition of FBG tactile  sensing array based on CNN

Research & Approach | 更新时间：2026-06-24

- Research on load recognition of FBG tactile  sensing array based on CNN
- Vol. 45, Issue 6, Pages: 35-39(2026)
- 作者机构：
  
  1. 南通大学机械工程学院,江苏,南通,226019
  2. 南通大学电气与自动化学院,江苏,南通,226019
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP212
- Published：2026
- 稿件说明：
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SHEN Zhihui, XU Yiming, LU Guan. Research on load recognition of FBG tactile  sensing array based on CNN[J]. 2026, 45(6): 35-39.
DOI：

SHEN Zhihui, XU Yiming, LU Guan. Research on load recognition of FBG tactile  sensing array based on CNN[J]. 2026, 45(6): 35-39. DOI：

摘要

针对应用于柔性皮肤的光纤触觉传感器对载荷识别精度不高、效率低的问题

提出了一种基于卷积神经网络(CNN)的光纤布拉格光栅(FBG)传感阵列的载荷识别方法。首先

利用Flexible树脂和3D打印技术制作了传感阵列实物；其次

划分点位并采集数据；最后

利用CNN进行识别并与反向传播神经网络(BPNN)、随机森林(RF)对比。结果表明:传感阵列的灵敏度和线性度较好

CNN具有良好的自适应性、泛化能力

在载荷位置和大小的识别中

评价指标均方根误差(RMSE)和决定系数(

)分别为0.4869

0.99477

高于其余两种算法。该方法可应用在柔性触觉传感器载荷感知。

Abstract

In order to solve the problems of low precision and low efficiency in load recognition of fiber-optic tactile sensors applied to flexible skin

a load recognition method for fiber Bragg grating(FBG)sensing array based on convolutional neural network(CNN)is proposed.Firstly

the sensing array is manufactured using Flexible resin and 3D printing technology.Secondly

points are divided and data are collected.Finally

CNN is used for recognition and compared with back propagation neural network(BPNN)and random forest(RF).The results show that the sensing array has good sensitivity and linearity

and CNN has good adaptability and generalization ability.In the recogniti

on of load position and magnitude

the evaluation indices root mean square error(RMSE)and coefficient of determination(

)are 0.4869 and 0.99477

respectively

which are better than those of the other two algorithms.This method can be applied to load sensing of flexible tactile sensors.

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