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• Students: Ali Ebrahimi
• Mentor(s): Prof. Marin Kobilarov, Prof. Iulian Iordachita, Prof. Russell Taylor and Dr. Niravkumar Patel

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<fs larger>Background and Signiicance</fs>

One of the major yet little recognized challenges in robotic vitreoretinal surgery is the matter of tool forces applied to the sclera. Tissue safety, coordinated tool use and interactions between tool tip and shaft forces are little studied. The introduction of robotic assist has further diminished the surgeon’s ability to perceive scleral forces. Microsurgical tools capable of measuring such small forces integrated with robot-manipulators may therefore improve functionality and safety by providing sclera force feedback to the surgeon. In this project, first we are going to develop two different standards for safe and adept eye manipulation based on an expert behavior. Then, using a force-sensing tool, we are going to implement two control algorithms on Eye-robot 2.1 based on the developed criteria to boost the sclera safety and decrease the scleral forces during eye manipulation. Finally, we want to do the multi user experiments with the setup shown below.

<fs larger>Specific aims</fs>
1. Applying a simple force control to increase the robot resistance
2. Obtaining the surgeon's behavior for manipulating the eye
3. Applying an adaptive force control to increase the sclera safety
4. Performing multi user experiments

• <fs larger>Minimum</fs>
• Force data accuracy and reliability
  
• Implementing the VAC algorithm and tuning
  
• Experiments with 10 subjects based on VAC with the linear stage
  

• <fs larger>Expected</fs>
• Obtaining the f-d relationship for an expert surgeon
  
• Implementing the new VAC algorithm and tuning
  
• Experiments with 5 subjects based on the new VAC with the linear stage
  

• <fs larger>Maximum</fs>
• Performing the validation experiments with clinicians

<fs larger>Based on the discussions we have had with mentors, we can have three different technical approaches.</fs>

1. Applying an exponential gain to the robot to increase the robot resistance to movement. This method is not a very complex one and really decreases the velocity of the robot when the sclera force increases. The decreased velocity of the robot is one of the biggest disadvantages of this method.

The results after applying this controller is shown below. It can be observed that the sclera forces are well below the safety limit.

2. Using an adaptive force control method. This method can estimate the environment stiffness and command the robot to comply with that.

The SIMULINK block diagram for adaptive force control

Simulation of 1DOF eyeball in SIMULINK

3. Using a RCM-based control method to prevent the robot from colliding the sclera. This method also can be a very safe and simple method which will work practically.

1 Complete understanding of the problem and survey for possible approaches, based on feedback from mentors. (done)

2 Procure eye-robot codebase (done)

3 Stiffer and more accurate force-sensing tools (done)

4 Accurate real-time force sensing information, restore the full setup to state. (done)

5 Microscope (done)

6 Moving stage for eye phantom (done)

7 Doing preliminary experiments with surgeons (done)

• 02/18 – Preparing the setup (<fs larger>DONE</fs>)
  
• 02/28 – Deciding about the control approach by talking to the mentors (<fs larger>DONE</fs>)
  

<fs larger>Minimum</fs>

• 03/05 - Force data accuracy and reliability (<fs larger>DONE</fs>)
  
• 03/18 - Implementing the VAC algorithm and tuning (<fs larger>DONE</fs>)
  
• 03/28 - Experiments with 10 subjects based on VAC with the linear stage (<fs larger>PENDING</fs>)
  

<fs larger>Expected</fs>

• 04/06 – Obtaining the f-d relationship for an expert surgeon (<fs larger>DONE</fs>)
  
• 04/18 - Implementing the new VAC algorithm and tuning (<fs larger>PENDING</fs>)
  
• 04/29 - Experiments with 5 subjects based on the new VAC with the linear stage (<fs larger>PENDING</fs>)
  

<fs larger>Maximum</fs>

• 05/10 – Performing the validation experiments with clinicians (<fs larger>PENDING</fs>)
  

For the remaiming part of the semester we have changed our plan a little

• Project Plan
  • Project plan presentation
  • project_plan_proposal.pdf
• Project Background Reading
  • paper_seminar_presentation.pdf
• Project Checkpoint
  • project_checkpoint_presentation.pdf
• Paper Seminar Presentations
  • here provide links to all seminar presentations
• Project Final Presentation
  • group07_poster.pdf
• Project Final Report
  • group07_report.pdf
  • links to any appendices or other material

[1] R. Taylor, P. Jensen, L. Whitcomb, A. Barnes, R. Kumar, D. Stoianovici, P. Gupta, Z. Wang, E. Dejuan, and L. Kavoussi, “A steady-hand robotic system for microsurgical augmentation,” The International Journal of Robotics Research, vol. 18, no. 12, pp. 1201– 1210, 1999.

[2] X. He, M. Balicki, P. Gehlbach, J. Handa, R. Taylor, and I. Iordachita, “A multi-function force sensing instrument for variable admittance robot control in retinal microsurgery,” in Robotics and Automation (ICRA), 2014 IEEE International Conference on. IEEE, 2014, pp. 1411–1418.

[3] Adaptive filtering, prediction and control, Graham C. Goodwin, Englewood Cliffs, N.J.: Prentice Hall, 2009

[4] A. Gijbels, E. B. Vander Poorten, P. Stalmans, and D. Reynaerts, “Development and experimental validation of a force sensing needle for robotically assisted retinal vein cannulations,” in Robotics and Automation (ICRA), 2015 IEEE International Conference on. IEEE,2015, pp. 2270–2276.

[5] J. T. Wilson, M. J. Gerber, S. W. Prince, C.-W. Chen, S. D. Schwartz, J.-P. Hubschman, and T.-C. Tsao, “Intraocular robotic interventional surgical system (iriss): Mechanical design, evaluation, and master–slave manipulation,” The International Journal of Medical Robotics and Computer Assisted Surgery, 2017.

[6] K. Willekens, A. Gijbels, L. Schoevaerdts, L. Esteveny, T. Janssens, B. Jonckx, J. H. Feyen, C. Meers, D. Reynaerts, E. Vander Poorten et al., “Robot-assisted retinal vein cannulation in an in vivo porcine retinal vein occlusion model,” Acta ophthalmologica, vol. 95, no. 3, pp. 270–275, 2017.

[7] S. Tanaka, K. Harada, Y. Ida, K. Tomita, I. Kato, F. Arai, T. Ueta, Y. Noda, N. Sugita, and M. Mitsuishi, “Quantitative assessment of manual and robotic microcannulation for eye surgery using new eye model,” The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 11, no. 2, pp. 210–217, 2015.

Here give list of other project files (e.g., source code) associated with the project. If these are online give a link to an appropriate external repository or to uploaded media files under this name space.2018-07