Data-driven calibration of RAVEN-II surgical robot with ground truth joint positions

Accurate joint position estimation is crucial for the control of cable-driven laparoscopic surgical robots like the RAVEN-II. However, any slack and stretch in the cable can lead to errors in kinematic estimation, complicating precise control. This work proposes an efficient data-driven calibration method, requiring no additional sensors post-training. This dataset was collected from a RAVEN-II surgical robot, including different calibration trajectories, 6-hour continuous idle, unloaded, and loaded operating. Ground truth joint positions for positional joints are also collected from high-resolution optical encoders.