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Bimanual Manipulation of Steady Hand Eye Robots with Adaptive Sclera Force Control: Cooperative vs. Teleoperation Strategies

Mojtaba Esfandiari, Peter Gehlbach, Russell H. Taylor, Iulian Iordachita

TL;DR

The paper tackles the safety challenges of retinal vein cannulation under surgeon tremor by proposing a bimanual adaptive teleoperation framework (BMAT) that combines AFC with SHER 2.0/2.1 and force-sensing tools. It compares BMAT to a bimanual cooperative mode (BMAC) across sitting and standing postures using a vessel-following eye phantom, demonstrating that AFC maintains scleral forces within safe limits and that BMAT can match or exceed BMAC performance in many safety metrics. The work shows that safe bimanual eye manipulation is achievable without inter-robot registration, highlighting potential improvements in surgeon safety, dexterity, and workflow, while acknowledging limitations and outlining future enhancements such as haptic feedback and learning-based approaches.

Abstract

Performing retinal vein cannulation (RVC) as a potential treatment for retinal vein occlusion (RVO) without the assistance of a surgical robotic system is very challenging to do safely. The main limitation is the physiological hand tremor of surgeons. Robot-assisted eye surgery technology may resolve the problems of hand tremors and fatigue and improve the safety and precision of RVC. The Steady-Hand Eye Robot (SHER) is an admittance-based robotic system that can filter out hand tremors and enables ophthalmologists to manipulate a surgical instrument inside the eye cooperatively. However, the admittance-based cooperative control mode does not safely minimize the contact force between the surgical instrument and the sclera to prevent tissue damage. Additionally, features like haptic feedback or hand motion scaling, which can improve the safety and precision of surgery, require a teleoperation control framework. This work presents a bimanual adaptive teleoperation (BMAT) control framework using SHER 2.0 and SHER 2.1 robotic systems. We integrate them with an adaptive force control (AFC) algorithm to automatically minimize the tool-sclera interaction force. The scleral forces are measured using two fiber Bragg grating (FBG)-based force-sensing tools. We compare the performance of the BMAT mode with a bimanual adaptive cooperative (BMAC) mode in a vessel-following experiment under a surgical microscope. Experimental results demonstrate the effectiveness of the proposed BMAT control framework in performing a safe bimanual telemanipulation of the eye without over-stretching it, even in the absence of registration between the two robots.

Bimanual Manipulation of Steady Hand Eye Robots with Adaptive Sclera Force Control: Cooperative vs. Teleoperation Strategies

TL;DR

The paper tackles the safety challenges of retinal vein cannulation under surgeon tremor by proposing a bimanual adaptive teleoperation framework (BMAT) that combines AFC with SHER 2.0/2.1 and force-sensing tools. It compares BMAT to a bimanual cooperative mode (BMAC) across sitting and standing postures using a vessel-following eye phantom, demonstrating that AFC maintains scleral forces within safe limits and that BMAT can match or exceed BMAC performance in many safety metrics. The work shows that safe bimanual eye manipulation is achievable without inter-robot registration, highlighting potential improvements in surgeon safety, dexterity, and workflow, while acknowledging limitations and outlining future enhancements such as haptic feedback and learning-based approaches.

Abstract

Performing retinal vein cannulation (RVC) as a potential treatment for retinal vein occlusion (RVO) without the assistance of a surgical robotic system is very challenging to do safely. The main limitation is the physiological hand tremor of surgeons. Robot-assisted eye surgery technology may resolve the problems of hand tremors and fatigue and improve the safety and precision of RVC. The Steady-Hand Eye Robot (SHER) is an admittance-based robotic system that can filter out hand tremors and enables ophthalmologists to manipulate a surgical instrument inside the eye cooperatively. However, the admittance-based cooperative control mode does not safely minimize the contact force between the surgical instrument and the sclera to prevent tissue damage. Additionally, features like haptic feedback or hand motion scaling, which can improve the safety and precision of surgery, require a teleoperation control framework. This work presents a bimanual adaptive teleoperation (BMAT) control framework using SHER 2.0 and SHER 2.1 robotic systems. We integrate them with an adaptive force control (AFC) algorithm to automatically minimize the tool-sclera interaction force. The scleral forces are measured using two fiber Bragg grating (FBG)-based force-sensing tools. We compare the performance of the BMAT mode with a bimanual adaptive cooperative (BMAC) mode in a vessel-following experiment under a surgical microscope. Experimental results demonstrate the effectiveness of the proposed BMAT control framework in performing a safe bimanual telemanipulation of the eye without over-stretching it, even in the absence of registration between the two robots.
Paper Structure (14 sections, 10 equations, 8 figures, 4 tables, 1 algorithm)

This paper contains 14 sections, 10 equations, 8 figures, 4 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. Methods
  3. Robot Kinematics
  4. Adaptive Force Control of the SHER
  5. Experiments
  6. Experimental Setup
  7. Experimental Procedure
  8. Experimental Results
  9. Sclera Force Control Performance
  10. Standing BMAC vs. Sitting BMAC
  11. Standing BMAT vs. Sitting BMAT
  12. Sitting BMAC vs. Sitting BMAT
  13. Discussion and Future Work
  14. Conclusion

Figures (8)

  • Figure 1: Bimanual teleoperation architecture with adaptive force control algorithm. The system consists of two Phantom Omni robots, the SHER 2.0 and SHER 2.1, a surgical microscope, two FBG-based force-sensing surgical instruments attached to the SHERs' end-effector to measure the tip force and sclera force, and two foot-pedals to activate and control the SHERs impedance. The figure shows the high-level adaptive force controller, a mid-level optimizer, and a low-level Galil joint velocity controller for SHER 2.0 and SHER 2.1.
  • Figure 2: A diagram of the FBG-based force-sensing surgical instruments connected to the eye robots. These instruments measure the scleral force, tip forces, and insertion depth. The body coordinate, ${B}$, is attached to the robot end-effector, and the spatial coordinate system, ${S}$, is fixed to the robot base.
  • Figure 3: Control diagram of the bimanual teleoperation modality integrated with adaptive sclera force control algorithm.
  • Figure 4: The experiment setup includes the SHER 2.1 and the SHER 2.0, two PHANTOM Omni haptic interfaces, two FBG interrogators, a surgical microscope, and an armrest (left); an eye phantom and two ATI force/torque sensors (top right); two FBG-based force-sensing tools attached to the SHERs' handles (bottom right).
  • Figure 5: Vessel-following experiment procedure for four combinations of control modes and user posture including (a) standing BMAC, (b) standing BMAT, (c) sitting BMAC, and (d) sitting BMAT. (e) The user activates each robot using a foot pedal and follows a random order of colored vessels by the dominant (right) hand, touches a target pin at the end of each vessel by the tooltip of the dominant hand, and re-orients the eye phantom using the non-dominant hand under a surgical microscope.
  • ...and 3 more figures