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[Kinematics parameter identification and accuracy evaluation method for neurosurgical robot].

Liang Li1, Hui Ding1, Guangzhi Wang2

  • 1Department of Biomedical Engineering, Tsinghua University, Beijing 100084, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|December 26, 2019
PubMed
Summary

This study presents an automated method to identify and evaluate kinematic model parameters for neurosurgical robots, significantly improving positioning accuracy. The new approach reduces robot relative distance error by 33.96% and inverse kinematics consistency error by 67.30%.

Keywords:
absolute positioning accuracyparameter calibrationrobotic armstereotactic

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Area of Science:

  • Robotics
  • Medical Engineering
  • Control Systems

Background:

  • Kinematic model parameter deviation critically impacts neurosurgical robot positioning accuracy.
  • Accurate kinematic parameters are essential for precise robotic-assisted surgeries.

Purpose of the Study:

  • To develop an automated method for identifying and evaluating kinematic model parameters in neurosurgical robots.
  • To enhance the positioning accuracy of neurosurgical robots through improved parameter identification.

Main Methods:

  • Established an identification equation encompassing all robot kinematic parameters.
  • Implemented a multiple-pivot strategy to determine the relationship between the end-effector and tracking marker.
  • Designed relative distance error and inverse kinematic coincidence error metrics for accuracy evaluation.

Main Results:

  • Developed and tested an automatic neurosurgical robot parameter identification and accuracy evaluation system.
  • The method demonstrated stable and rapid identification and evaluation of kinematic model parameters.
  • Robot control using identified parameters reduced relative distance error by 33.96% and inverse kinematics consistency error by 67.30%.

Conclusions:

  • The proposed automated method effectively identifies and evaluates neurosurgical robot kinematic parameters.
  • This approach significantly enhances the positioning accuracy of neurosurgical robots, crucial for surgical precision.