Separation method of bending and torsion in shape sensing based on FBG sensors array

Area of Science:

• * Mechanical Engineering
• * Sensor Technology
• * Optical Fiber Sensing

Background:

• * Accurate 3D shape sensing of flexible structures is crucial for robotics, medical devices, and aerospace.
• * Simultaneous bending and torsion complicate strain analysis in shape sensing sensors.
• * Existing methods often struggle to independently quantify bending and torsion effects.

Purpose of the Study:

• * To develop a theoretical and experimental method for decoupling bending and torsion in fiber Bragg grating (FBG) based shape sensing.
• * To improve the accuracy of 3D shape reconstruction by precisely measuring curvature and torsion.
• * To validate the proposed method through experimental measurements and shape reconstruction.

Main Methods:

• * Design of a shape sensing sensor with a triangular FBG array encapsulated on a flexible rod.
• * Derivation of relationships between bending curvature, bending strain, and torsion-induced strain based on FBG array configuration.
• * Establishment of a nonlinear matrix equation to solve for strain, bending direction, and torsion parameters.
• * Utilizing Frenet-Serret formulas for 3D shape reconstruction from curvature and torsion data.

Main Results:

• * Experimental validation shows low relative average errors: 2.65% for maximum strain and 0.86% for bending direction.
• * Successful application to 2D and 3D shape reconstruction with absolute spatial position errors of 3.79mm (2D arc) and 11.10mm (3D helix).
• * The method effectively separates bending and torsion, providing accurate curvature and torsion measurements.

Conclusions:

• * The proposed theoretical method successfully separates bending and torsion in FBG-based shape sensing sensors.
• * Experimental results confirm the feasibility and accuracy of the developed technique.
• * This method offers crucial parameters for precise 3D shape reconstruction models of flexible sensing elements.