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Design and Visual Servoing Control of a Hybrid Dual-Segment Flexible Neurosurgical Robot for Intraventricular Biopsy

Jian Chen, Mingcong Chen, Qingxiang Zhao, Shuai Wang, Yihe Wang, Ying Xiao, Jian Hu, Danny Tat Ming Chan, Kam Tong Leo Yeung, David Yuen Chung Chan, Hongbin Liu

TL;DR

A novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain, is introduced, which substantiates its considerable potential for deployment in neurosurgery.

Abstract

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot's ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery.

Design and Visual Servoing Control of a Hybrid Dual-Segment Flexible Neurosurgical Robot for Intraventricular Biopsy

TL;DR

A novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain, is introduced, which substantiates its considerable potential for deployment in neurosurgery.

Abstract

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot's ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery.
Paper Structure (20 sections, 14 equations, 9 figures)

This paper contains 20 sections, 14 equations, 9 figures.

Table of Contents

  1. Introduction
  2. Mechanical Design
  3. Design Goals
  4. System Overview
  5. Hybrid Dual-Segment Flexible Endoscope Design
  6. Modelling
  7. Kinematics of MicroNeuro
  8. Visual Servoing Modeling
  9. Jacobian Matrix Estimation
  10. Visual Model Predictive Controller
  11. Predictive Model
  12. Constraints
  13. Optimization Objective
  14. Experiment and Validation
  15. Static Target Tracking
  16. ...and 5 more sections

Figures (9)

  • Figure 1: (a) Traditional rigid intraventricular endoscopes can only move forward and backward along the axis. (b) The MicroNeuro flexible robot system reach with one burr hole.
  • Figure 2: Overview of The robot system. (a) The MicroNeuro system and MicroNeuro surgical robot. (b) Steering mode 1 without insertion of the distal. (c) Steering mode 2 with $S$ shape. (d) Endoscopic view of inner endscope. (e) Endoscopic view of outer sheath. (f) Endoscope features.
  • Figure 3: Mechanical design of the MicroNeuro robot. (a) Axial section view of outer sheath with cables distribution diagram. (b) Nitinol backbone of the outer sheath. (c) Axial section view of inner endoscope. (d) Nitinol backbone of the inner endoscope. (e) Illustration of coordinate frames.
  • Figure 4: The visual MPC controller using an IMC scheme.
  • Figure 5: Tracking the static object on a plane. (a) Experiment setup. (b) Tags movement trajectories in the image plane. (c) Tracking errors.
  • ...and 4 more figures