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Surgical robots have been introduced to operating
rooms over the past few decades due to their high sensitivity,
small size, and remote controllability. The cable-driven nature
of many surgical robots allows the systems to be dexterous and
lightweight, with diameters as low as 5mm. However, due to the
slack and stretch of the cables and the backlash of the gears,
inevitable uncertainties are brought into the kinematics calcu-
lation [1]. Since the reported end effector position of surgical
robots like RAVEN-II [2] is directly calculated using the motor
encoder measurements and forward kinematics, it may contain
relatively large error up to 10mm, whereas semi-autonomous
functions being introduced into abdominal surgeries require
position inaccuracy of at most 1mm. To resolve the problem, a
cost-effective, real-time and data-driven pipeline for robot end
effector position precision estimation is proposed and tested on
RAVEN-II. Analysis shows an improved end effector position
error of around 1mm RMS traversing through the entire robot
workspace without high-resolution motion tracker. The open
source code, data sets, videos, and user guide can be found at
//github.com/HaonanPeng/RAVEN Neural Network Estimator.
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