Geometric parameter measurement method for soft robotic arms based on binocular vision

doi:10.3785/j.issn.1006-754X.2025.04.156

Chinese Journal of Engineering Design

2025, Vol. 32

Issue (5): 613-622 DOI: 10.3785/j.issn.1006-754X.2025.04.156

Theory and Method of Mechanical Design

Geometric parameter measurement method for soft robotic arms based on binocular vision

Shen SHU(

),Jialiang WANG,Junfeng HU(

),Yu ZHANG,Kai CHU,Hao ZHOU,Mingwei CAI

School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China

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Abstract

Due to the inherent compliance and low stiffness of soft robotic arms, they exhibit complex morphological changes during spatial motion, making existing measurement methods inadequate for pose measurement of such robotic arms. To solve the above problems, a novel method combining binocular vision technology and B-spline curve fitting is proposed to accurately measure the geometric parameters of soft robotic arms. This method extracted the three-dimensional information of the central skeleton of the soft robotic arm through a binocular vision system and employed the cubic B-spline curve for three-dimensional shape reconstruction, thereby obtaining the key geometric parameters of the soft robotic arm. To validate the effectiveness of the proposed method, geometric parameter measurements were conducted on soft robotic arms with arc-shaped, S-shaped and L-shaped bends. The measurement results showed that the average bending curvature error of the robotic arm moving on a plane was 0.198%, and the average bending angle error was 0.159%. The average bending angle error of the robotic arm moving in three-dimensional space was 1.340%. In addition, this measurement method could be extended to dynamic measurements and accurately measure the geometric parameters of soft robotic arms even in the presence of visual occlusions. The measurement method based on binocular vision and B-spline curve fitting can provide a new idea for the parameter measurement of soft robotic arms.

Key words： soft robotic arm binocular vision B-spline curve geometric parameter measurement

Received: 08 July 2024 Published: 31 October 2025

CLC:

TP 391.4

Corresponding Authors: Junfeng HU E-mail: 1414401390@qq.com;hjfsuper@126.com

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	Shen SHU
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	Hao ZHOU
	Mingwei CAI

Cite this article:

Shen SHU,Jialiang WANG,Junfeng HU,Yu ZHANG,Kai CHU,Hao ZHOU,Mingwei CAI. Geometric parameter measurement method for soft robotic arms based on binocular vision. Chinese Journal of Engineering Design, 2025, 32(5): 613-622.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2025.04.156 OR https://www.zjujournals.com/gcsjxb/Y2025/V32/I5/613

基于双目视觉的软体机械臂几何参数测量方法

由于软体机械臂固有的柔顺性与低刚度，其在空间运动中展现出复杂的形态变化，现有测量方法难以实现这类机械臂的位姿测量。为解决上述问题，提出了一种结合双目视觉技术和B样条曲线拟合的新方法，旨在准确测量软体机械臂的几何参数。该方法通过双目视觉系统提取软体机械臂中心骨架的三维信息，并采用三次B样条曲线进行三维形状重构，进而获得软体机械臂的关键几何参数。为了验证所提出方法的有效性，对圆弧形、S形弯曲、L形弯曲的软体机械臂进行几何参数测量。测量结果表明，在平面上运动的机械臂的平均弯曲曲率误差为0.198%，平均弯曲角度误差为0.159%；在三维空间中运动的机械臂的平均弯曲角度误差为1.340%。此外，该测量方法能扩展为动态测量方法，在视觉有遮挡的情况下也能准确地测量软体机械臂的几何参数。基于双目视觉和B样条曲线拟合的测量方法可为软体机械臂的参数测量提供一种新思路。

关键词： 软体机械臂, 双目视觉, B样条曲线, 几何参数测量

Fig.1 Schematic diagram of central skeleton curve of soft robotic arm

Fig.2 Calibration priciple of binocular camera

Fig.3 Calculation result of reprojection error

Fig.4 Comparison of shape reconstruction results based on three fitting methods

Fig.5 Description of geometric parameters of soft robotic arm

Fig.6 Binocular vision measurement device

Fig.7 Shape reconstruction result of semi-circular arc soft tube

Fig.8 Measurement results of geometric parameters of semi-circular arc soft tube

Fig.9 Shape reconstruction result of S-shaped soft tube

Fig.10 Measurement results of bending curvature of S-shaped soft tube

Table 1 Measurement error of bending angle of S-shaped soft tube

Fig.11 Postures of L-shaped soft robotic arm at different times

Fig.12 Shape reconstruction result of L-shaped soft robotic arm

Fig.13 Bending curvatures at different positions of L-shaped soft robotic arm

Fig.14 Postures of soft tube at different times

Fig.15 Shape reconstruction result of partially occluded soft tube

Fig.16 Bending curvatures at different positions of partially occluded soft tube


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