  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.
