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Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation
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High-Precision Autonomous Control of Flexible Needles via Real-Time Finite Element Simulation and Cross-Entropy

Yanzhou Wang1, Chang Chang1, Junling Mei1

  • 1Department of Mechanical Engineering and the Laboratory of Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.

IEEE Robotics and Automation Letters
|September 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for autonomous flexible needle control in soft tissues. Real-time simulation and optimization achieve precise needle insertion with sub-millimeter accuracy in phantom experiments.

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Area of Science:

  • Robotics
  • Medical Devices
  • Biomedical Engineering

Background:

  • Accurate flexible needle insertion is crucial for minimally invasive surgery.
  • Existing methods face challenges in real-time control and model uncertainties.

Purpose of the Study:

  • To develop a unified framework for autonomous flexible needle control in soft tissues.
  • To achieve high targeting accuracy using real-time simulation and optimization.

Main Methods:

  • Utilized real-time finite element (FE) simulation and cross-entropy (CE) optimization.
  • Combined sampling-based model predictive control (MPC) with a kinematic-based bang-bang strategy.
  • Employed sparse electromagnetic (EM) tracking for state reconstruction and uncertainty compensation.

Main Results:

  • Demonstrated sub-millimeter targeting accuracy in plastisol and ex vivo chicken breast phantoms.
  • Achieved targeting errors of 0.16 ± 0.29 mm and 0.22 ± 0.78 mm, respectively.
  • The framework effectively coordinated needle insertion, lateral adjustments, and bevel rotations.

Conclusions:

  • The proposed framework enables precise autonomous control of flexible needles in soft tissues.
  • Real-time FE simulation and CE optimization are effective for complex needle manipulation.
  • The method shows significant potential for improving robotic-assisted surgical procedures.