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Improving Needle Penetration via Precise Rotational Insertion Using Iterative Learning Control

Yasamin Foroutani, Yasamin Mousavi-Motlagh, Aya Barzelay, Tsu-Chin Tsao

TL;DR

The paper tackles the challenge of precise rotational needle insertion in intraocular robotic surgery, where misalignment and unmodeled dynamics hinder performance. It introduces a dual-loop Iterative Learning Control framework that first corrects kinematic errors using OCT-derived end-effector feedback and then compensates motor dynamics through data-driven, phase-only inversion of joint dynamics. The method demonstrates convergence of the kinematic loop to sub-50 micrometer accuracy and substantial improvements in trajectory tracking after the dynamics loop, leading to higher subretinal injection success rates (9/10 with rotation vs 5/10 without) in ex vivo pig eyes. This data-driven, model-light approach enables safe, repeatable high-precision insertions and can extend to other repetitive surgical tasks requiring controlled insertions.

Abstract

Achieving precise control of robotic tool paths is often challenged by inherent system misalignments, unmodeled dynamics, and actuation inaccuracies. This work introduces an Iterative Learning Control (ILC) strategy to enable precise rotational insertion of a tool during robotic surgery, improving penetration efficacy and safety compared to straight insertion tested in subretinal injection. A 4 degree of freedom (DOF) robot manipulator is used, where misalignment of the fourth joint complicates the simple application of needle rotation, motivating an ILC approach that iteratively adjusts joint commands based on positional feedback. The process begins with calibrating the forward kinematics for the chosen surgical tool to achieve higher accuracy, followed by successive ILC iterations guided by Optical Coherence Tomography (OCT) volume scans to measure the error and refine control inputs. Experimental results, tested on subretinal injection tasks on ex vivo pig eyes, show that the optimized trajectory resulted in higher success rates in tissue penetration and subretinal injection compared to straight insertion, demonstrating the effectiveness of ILC in overcoming misalignment challenges. This approach offers potential applications for other high precision robot tasks requiring controlled insertions as well.

Improving Needle Penetration via Precise Rotational Insertion Using Iterative Learning Control

TL;DR

The paper tackles the challenge of precise rotational needle insertion in intraocular robotic surgery, where misalignment and unmodeled dynamics hinder performance. It introduces a dual-loop Iterative Learning Control framework that first corrects kinematic errors using OCT-derived end-effector feedback and then compensates motor dynamics through data-driven, phase-only inversion of joint dynamics. The method demonstrates convergence of the kinematic loop to sub-50 micrometer accuracy and substantial improvements in trajectory tracking after the dynamics loop, leading to higher subretinal injection success rates (9/10 with rotation vs 5/10 without) in ex vivo pig eyes. This data-driven, model-light approach enables safe, repeatable high-precision insertions and can extend to other repetitive surgical tasks requiring controlled insertions.

Abstract

Achieving precise control of robotic tool paths is often challenged by inherent system misalignments, unmodeled dynamics, and actuation inaccuracies. This work introduces an Iterative Learning Control (ILC) strategy to enable precise rotational insertion of a tool during robotic surgery, improving penetration efficacy and safety compared to straight insertion tested in subretinal injection. A 4 degree of freedom (DOF) robot manipulator is used, where misalignment of the fourth joint complicates the simple application of needle rotation, motivating an ILC approach that iteratively adjusts joint commands based on positional feedback. The process begins with calibrating the forward kinematics for the chosen surgical tool to achieve higher accuracy, followed by successive ILC iterations guided by Optical Coherence Tomography (OCT) volume scans to measure the error and refine control inputs. Experimental results, tested on subretinal injection tasks on ex vivo pig eyes, show that the optimized trajectory resulted in higher success rates in tissue penetration and subretinal injection compared to straight insertion, demonstrating the effectiveness of ILC in overcoming misalignment challenges. This approach offers potential applications for other high precision robot tasks requiring controlled insertions as well.

Paper Structure

This paper contains 13 sections, 10 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Robotic System
  4. Iterative Learning Control
  5. Compensating Kinematic Errors
  6. Compensating Motor Dynamic
  7. Experimental Setup and Results
  8. Problem Statement
  9. Controller Design
  10. Kinematic Loop Implementation
  11. Dynamics Loop Implementation
  12. Surgical Evaluation
  13. Conclusion

Figures (10)

  • Figure 1: System overview showing the experimental setup, using Thorlabs OCT, IRISS robot, and an open sky pig eye to perform injection.
  • Figure 2: Learning structure for the two step ILC workflow. The kinematic ILC updates the joint values to minimize tooltip position error, and the dynamic ILC updates the derived smooth trajectory to ensure correct tracking.
  • Figure 3: Initial ILC loop for correcting kinematic errors.
  • Figure 4: Second ILC loop for correcting tracking errors due to joint dynamics.
  • Figure 5: (a) Tooltip path before ILC (b) Tooltip path after ILC (c) Zoomed in view of tooltip path after ILC. All steps were performed 5 times to show repeatability and deviation.
  • ...and 5 more figures