A Tri-Axial FBG-Based Force Sensor at the Tool Tip of a Continuum Manipulator for Single-Port Access Surgery
Jiading Zhang, Lijun Hao, siqi liu, Jiangran Zhao, Jiamiao GONG, Kai Xu, Sheng Liu, Zhe Zheng, Tangwen Yang
AI summary
Problem
Robotic single-port surgery lacks reliable force feedback, leading to reduced control precision and higher tissue damage risk, while existing sensors struggle with miniaturization and nonlinear signal coupling.
Approach
The authors developed a 6 mm diameter triaxial FBG sensor with a dumbbell-grooved nickel-titanium elastomer and implemented a Whale Migration Algorithm-optimized Kernel Extreme Learning Machine to accurately decouple nonlinear force signals.
Key results
- 6.0 mm diameter sensor seamlessly integrates with continuum manipulator tool tips
- Maximum full-scale error under 1% across axial [0–5 N] and radial [±2.5 N] ranges
- Maximum RMSE of 0.0308 N and repeatability within ±0.24%
- WMA-KELM decoupling outperforms traditional linear and neural network methods in accuracy and robustness
Why it matters
Provides a critical tactile feedback solution for minimally invasive surgical robots, enhancing procedural safety and surgeon skill acquisition.
Abstract
The absence of force feedback remains a major bottleneck in the development of robotic laparoendoscopic single-site (R-LESS) surgery, reducing the control precision of surgical instruments and increasing the risk of tissue damage. To address this challenge, we propose a miniature triaxial force sensor based on Fiber Bragg Grating (FBG), featuring high precision, nonlinear decoupling capability, and seamless inte- gration with the tool tip of a continuum manipulator for single- port access surgery. The sensor features a monolithic elastic body with a dumbbell-shaped groove, where four FBGs are symmetrically arranged at 90◦intervals around the circumfer- ence to form a redundant measurement unit, thereby enhancing sensing accuracy. A novel Whale Migration Algorithm Based Kernel Extreme Learning Machine (WMA-KELM) is intro- duced to address the nonlinear coupling influences arising from manipulator integration, demonstrating superior accuracy and robustness compared to conventional methods. Experimental results show that within the ranges of axial force [0 N, 5 N] and radial force [-2.5 N, 2.5 N], the maximum full-scale (FS) error is less than 1% in all dimensions, the maximum RMSE is 0.0308 N, and the maximum repeatability error is within ±0.24%. These results validate the force sensor integrated with the continuum manipulator, and the proposed algorithm is effective and reliable.