Distributed fiber grating puncture needle force-shape sensing sensor

LI Tianliang; SONG Zhenzhen; CHEN Fayin; SU Yifei; TAN Yuegang

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Distributed fiber grating puncture needle force-shape sensing sensor

更新时间：2022-10-24

- Distributed fiber grating puncture needle force-shape sensing sensor
- Optics and Precision Engineering Pages: 1-8(2022)
- 作者机构：
  
  武汉理工大学机电工程学院，湖北武汉 430070
- 作者简介：
  
  E-mail： tianliangli@whut.edu.cn
- 基金信息：
- DOI：
  CLC： TN253
- Received：08 June 2022，
  
  Revised：01 August 2022，
  
  Published Online：24 October 2022，
- 稿件说明：
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李天梁,宋珍珍,陈发银等.光纤光栅与人工智能融合的形状自感知穿刺针[J].光学精密工程,

LI Tianliang,SONG Zhenzhen,CHEN Fayin,et al.Distributed fiber grating puncture needle force-shape sensing sensor[J].Optics and Precision Engineering,
李天梁,宋珍珍,陈发银等.光纤光栅与人工智能融合的形状自感知穿刺针[J].光学精密工程, DOI：10.37188/OPE.XXXXXXXX.0001

LI Tianliang,SONG Zhenzhen,CHEN Fayin,et al.Distributed fiber grating puncture needle force-shape sensing sensor[J].Optics and Precision Engineering, DOI：10.37188/OPE.XXXXXXXX.0001

摘要

手术机器人可以通过术前路径规划避免重要组织受到损伤，且具有较高的操作精度，因而被广泛应用于穿刺活检手术之中。目前手术机器人在穿刺针形状信息准确实时获取方面存在不足，无法实现穿刺过程中自主避障以及精准的靶点操作。因此研究穿刺针形状感知方法，可为手术机器人自主/主从穿刺提供形状信息反馈，对于提高穿刺手术的安全性与准确性有着重大意义。鉴于此，提出了一种融合分布式光纤光栅传感技术和人工智能的形状自感知穿刺针。利用机器学习算法，对静态标定实验获取的不同弯曲状态下光纤光栅的中心波长数据与形状数据进行训练，得到了光纤光栅中心波长漂移量与形状函数及弯曲方向角之间的神经网络模型，进一步调用该神经网络模型，实现穿刺针的三维形状重构；测试结果表明，穿刺针针体的形状重构最大误差值为0.90 mm，弯曲方向角的最大误差为5.03°。在动态性能验证实验中，形状重构最大误差值为0.84 mm，弯曲方向角的最大误差为1.02°，进一步验证了模型的可靠性。因此所提出的形状自感知穿刺针能够精准实现形状信息的实时获取，从而在手术机器人自主穿刺和主从操作过程中针体形状感知与调控方面具有广阔的应用前景。

Abstract

Surgical robots can avoid damage to important tissues through preoperative path planning before surgery. They have high operational accuracy， so they are widely used in needle biopsy operation. However， surgical robots cannot achieve the accurate and real-time acquisition of puncture needle shape information recently. This problem makes them difficult to avoid the obstacles autonomously and prevents the needle from achieving its target precisely for the puncture procedures. Therefore， studying the shape perception method of needles to provide shape information feedback for autonomous/master-slave puncture of surgical robots， is important to improve the safety and accuracy of puncture surgery. Given this， this paper proposed a shape self-sensing puncture needle that integrates distributed optical fiber sensing technology and artificial intelligence. The machine learning algorithm was used to train the data， and the training data contained the center wavelength data and shape data of the fiber grating under different bending states， both were obtained from the static calibration experiment. The neural network model between the center wavelength shift data of the fiber Bragg grating and the needle shape was obtained. Then， the neural network model was called to realize the three-dimensional shape reconstruction of the puncture needle. Experimental results show that the maximum error of the needle shape reconstruction is 0.90mm， and the maximum error of the bending directional angle is 5.03°. As for the dynamic experiment， the maximum error of shape reconstruction is 0.84mm， and the maximum error of bending directional angle is 1.02°， which further validates the reliability of the model. Therefore， the proposed shape self-sensing puncture needle can accurately realize the real-time acquisition of the shape， and it has broad application prospects in the aspect of needle shape perception and regulation during autonomous puncture and master-slave operation of surgical robots.

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references

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