Shape Sensing for Single-Port Continuum Surgical Robot Using Few Multicore Fiber Bragg Grating Sensors

Abstract

We proposed a method for shape sensing using a few multicore fiber Bragg grating (FBG) sensors in a single-port continuum surgical robot (CSR). The traditional method of utilizing a forward kinematic model to calculate the shape of a single-port CSR is limited by the accuracy of the model. If FBG sensors are used for shape sensing, their accuracy will be affected by their number, especially in long and flexible CSRs. A fusion method based on an extended Kalman filter (EKF) was proposed to solve this problem. Shape reconstruction was performed using the CSR forward kinematic model and FBG sensors, and the two results were fused using an EKF. The CSR reconstruction method adopted the incremental form of the forward kinematic model, while the FBG sensor method adopted the discrete arc-segment assumption method. The fusion method can eliminate the inaccuracy of the kinematic model and obtain more accurate shape reconstruction results using only a small number of FBG sensors. We validated our algorithm through experiments on multiple bending shapes under different load conditions. The results show that our method significantly outperformed the traditional methods in terms of robustness and effectiveness.

摘要

本文提出了一种在单孔连续体手术机器人(CSR)中使用少量多核光纤光栅(FBG)传感器进行形状感知的方法。传统的方法利用正运动学模型计算单孔CSR的形状,其受到模型精度的限制。如果将FBG传感器用于形状传感,其精度将受到传感器数量的影响,特别是在长而灵活的CSR中。为了解决这一问题,提出了一种基于扩展卡尔曼滤波(EKF)的融合方法。分别使用CSR正运动学模型和FBG传感器进行形状重建,并用EKF将两个结果融合。CSR运动模型的方法采用了正运动学模型的增量形式进行形状重建,FBG传感器的方法则采用了离散弧段假设进行形状重建。融合方法可以消除运动学模型的不准确性,仅使用少量FBG传感器即可获得更精确的形状重建结果。通过不同载荷条件下多种弯曲形状的实验验证了该算法的有效性。结果表明,该方法在鲁棒性和有效性方面明显优于传统方法。