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Supervised by Ministry of Industry and Information Technology of The People''s Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Jiandong Dai,Zhijiang Du,Zhiyuan Yan,Yunlei Liang.Least Squares Support Vector Machine Kalman Filter for PhysiologicalTremor Suppression in Minimally Invasive Surgical Robot[J].Journal of Harbin Institute Of Technology(New Series),2020,27(5):22-28.DOI:10.11916/j.issn.1005-9113.18139.
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Least Squares Support Vector Machine Kalman Filter for PhysiologicalTremor Suppression in Minimally Invasive Surgical Robot
Author NameAffiliation
Jiandong Dai State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China 
Zhijiang Du State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China 
Zhiyuan Yan State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China 
Yunlei Liang State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China 
Abstract:
This paper studies the physiological tremor filtering in minimally invasive surgical robot. The surgeon's physiological tremor of the hand can cause the vibration of the tip of the surgical instrument, which may reduce operative accuracy and limit the application of surgical robots. Aiming at the vibration caused by physiological tremor of hand, we propose a Least Squares Support Vector Machine Kalman Filter (LSSVMKF), which can filter the tremor by estimating and modeling the tremor signal by Kalman filter and then superimposing it reversely in the control signal. When estimating and modeling the tremor signal, the filter uses the Least Squares Support Vector Machine (LS-SVM) to build the regression model of the constant parameters (Process Noise Covariance and Measurement Noise Covariance) of the traditional Kalman filter, which can dynamically adjust these parameters during the operation and improve the accuracy of Kalman filter. The simulation results show that the LSSVMKF can effectively filter out the tremor signal, thereby improving the accuracy of surgery.
Key words:  physiological tremor suppression  minimally invasive surgical robot  Kalman filter  support vector machine
DOI:10.11916/j.issn.1005-9113.18139
CLC NUMBER:TP241.3
Fund:
Descriptions in Chinese:
  

一种用于微创外科手术机器人的最小二乘支持向量机卡尔曼震颤滤除滤波器

代剑东,杜志江,闫志远,梁云雷

(哈尔滨工业大学机器人技术与系统国家重点实验室,哈尔滨 150001)

摘要:

本文研究了微创外科手术机器人的生理震颤滤波方法。外科医生手部的生理震颤会引起手术器械末端的振动,降低手术精度,限制手术机器人的应用。针对手部生理震颤引起的振动,本文提出一种最小二乘支持向量机卡尔曼滤波。该滤波算法利用卡尔曼滤波算法对震颤信号进行估计和建模,然后将其反向叠加到控制信号中,从而在不引起时间延迟的前提下对震颤信号进行滤波。估计震颤信号时用最小二乘支持向量机建立了过程噪声协方差和测量噪声协方差的回归模型,替换传统的卡尔曼滤波器中的这两个常数,使得滤波器在手术过程中可以动态地调整这些参数以提高卡尔曼滤波的精度。仿真结果表明,本文提出的算法能够有效地滤除震颤信号,克服了低通滤波带来时间延迟的问题,同时提高了卡尔曼滤波的精度,从而提高了微创外科手术机器人的操作精度。

关键词:生理震颤滤除,微创外科手术机器人,卡尔曼滤波,支持向量机

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