Deformable Soft Material Shape Sensing Based on Fiber Bragg Grating Array

The utilization of soft materials is increasingly prevalent in fields such as robotics and actuators, so the shape sensing of these materials becomes particularly significant. We have designed a deformable soft material shape sensor based on a fiber Bragg grating array. Experimental results demonstrate that the sensor exhibits high robustness and excellent repeatability. Additionally, in order to achieve more accurate demodulation of spectral data and shape sensing, we propose the utilization of neural network algorithms to integrate spectral data with shape curve parameters, which are obtained through image recognition.

I. Introduction

Fiber Bragg gratings (FBGs) are significant optical components based on fiber optic technology [1]. By monitoring and analyzing specific wavelengths in the reflection spectrum of FBGs, sensing physical quantities such as shape, temperature, and pressure can be achieved [2]–[3]. In terms of shape sensing, FBGs offer a remarkable advantage due to their fast response speed, enabling real-time measurement for shape characterization. Traditional FBG of single-point structures are subject to certain limitations in terms of measurement range and resolution. Measuring shape variations over large or continuous areas significantly increases the experimental challenges and costs involved [4]. Instead, fiber Bragg grating arrays (FBGAs) enable distributed real-time monitoring of multiple points, exhibiting advantages in perceiving shape variations over continuous areas [5]. As a result, FBGAs have been extensively applied in shape sensing, particularly in the perception of morphology in soft materials.