Performing intricate eye microsurgery, such as 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 has to do with 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 address crucial safety considerations, such as minimizing contact force between the surgical instrument and the sclera surface to prevent tissue damage. An adaptive sclera force control algorithm was proposed to address this limitation using an FBG-based force-sensing tool to measure and minimize the tool-sclera interaction force. Additionally, features like haptic feedback or hand motion scaling, which can improve the safety and precision of surgery, require a teleoperation control framework. We implemented a bimanual adaptive teleoperation (BMAT) control mode using SHER 2.0 and SHER 2.1 and compared its performance with a bimanual adaptive cooperative (BMAC) mode. Both BMAT and BMAC modes were tested in sitting and standing postures during a vessel-following experiment under a surgical microscope. It is shown, for the first time to the best of our knowledge in robot-assisted retinal surgery, that integrating the adaptive sclera force control algorithm with the bimanual teleoperation framework enables surgeons to safely perform bimanual telemanipulation of the eye without over-stretching it, even in the absence of registration between the two robots.

A surgeon's physiological hand tremor can significantly impact the outcome of delicate and precise retinal surgery, such as retinal vein cannulation (RVC) and epiretinal membrane peeling. Robot-assisted eye surgery technology provides ophthalmologists with advanced capabilities such as hand tremor cancellation, hand motion scaling, and safety constraints that enable them to perform these otherwise challenging and high-risk surgeries with high precision and safety. Steady-Hand Eye Robot (SHER) with cooperative control mode can filter out surgeon's hand tremor, yet another important safety feature, that is, minimizing the contact force between the surgical instrument and sclera surface for avoiding tissue damage cannot be met in this control mode. Also, other capabilities, such as hand motion scaling and haptic feedback, require a teleoperation control framework. In this work, for the first time, we implemented a teleoperation control mode incorporated with an adaptive sclera force control algorithm using a PHANTOM Omni haptic device and a force-sensing surgical instrument equipped with Fiber Bragg Grating (FBG) sensors attached to the SHER 2.1 end-effector. This adaptive sclera force control algorithm allows the robot to dynamically minimize the tool-sclera contact force. Moreover, for the first time, we compared the performance of the proposed adaptive teleoperation mode with the cooperative mode by conducting a vessel-following experiment inside an eye phantom under a microscope.