Real-Time Non-Contact Force Compensation for Wrist-Mounted Force/Torque Sensors in Haptic-Enabled Robotic Surgery Training

TL;DR

This paper presents a real-time, low-cost force compensation method for wrist-mounted sensors in robotic surgery training, significantly improving measurement accuracy for haptic feedback.

Contribution

It introduces a recursive least squares-based compensation technique that eliminates the need for dataset collection and recalibration, enhancing robustness and practicality.

Findings

Achieves over 95% error reduction in force measurement

Attains more than 91% error reduction in torque measurement

Outperforms existing compensation methods in accuracy

Abstract

Haptic feedback has been a long-missed feature in robotic-assisted surgery, one that would allow surgeons to perceive tissue properties and apply controlled forces during delicate procedures. Although commercial robotic systems have begun to integrate haptic technologies, their high costs limit accessibility for training and research purposes. To address this gap, we extend our previously developed low-cost robotic surgery training setup, RoboScope, by incorporating a wrist-mounted force/torque (F/T) sensor for haptic feedback training. Wrist-mounted sensing avoids many challenges associated with tip-mounted sensors but introduces additional non-contact forces, such as gravity, sensor bias, installation offsets, and associated torques, which compromise measurement accuracy. In this paper, we propose a robust real-time compensation method based on recursive least squares (RLS). This method eliminates the need for dataset collection and frequent recalibration while adapting to changing operating conditions. Experimental validation demonstrates that the proposed approach achieves over 95% error reduction in non-contact force compensation and more than 91% in non-contact torque compensation, significantly outperforming existing methods. These results highlight the potential of our approach for providing reliable haptic feedback in robotic surgery training and research.