# Real-time Data Driven Precision Estimator for RAVEN-II Surgical Robot End Effector Position

arXiv: https://arxiv.org/abs/1910.06425 · HTML: https://mlsanity.com/paper/1910.06425/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives](https://mlsanity.com/paper/2403.04971/) (2024)
- [Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation](https://mlsanity.com/paper/2212.07567/) (2022)
- [Improving the realism of robotic surgery simulation through injection of learning-based estimated errors](https://mlsanity.com/paper/2406.07375/) (2024)

## Abstract (arXiv)

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 calculation. Since the reported end effector position of surgical robots like RAVEN-II 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.