Learning-based Force Sensing and Impedance Matching for Safe Haptic Feedback in Robot-assisted Laparoscopic Surgery

TL;DR

This paper introduces NIMA, a nonlinear impedance matching method that enhances haptic feedback accuracy and safety in robot-assisted laparoscopic surgery by modeling complex interactions in real-time.

Contribution

NIMA extends previous impedance matching techniques by incorporating nonlinear dynamics, reducing force error, and eliminating haptic kickback for safer surgical teleoperation.

Findings

Achieves 95% reduction in force error compared to previous methods.

Eliminates haptic kickback, ensuring zero force when handle is released.

Improves force fidelity and responsiveness across surgical conditions.

Abstract

Integrating accurate haptic feedback into robot-assisted minimally invasive surgery (RAMIS) remains challenging due to difficulties in precise force rendering and ensuring system safety during teleoperation. We present a Nonlinear Impedance Matching Approach (NIMA) that extends our previously validated Impedance Matching Approach (IMA) by incorporating nonlinear dynamics to accurately model and render complex tool-tissue interactions in real-time. NIMA achieves a mean absolute error of 0.01 (std 0.02 N), representing a 95% reduction compared to IMA. Additionally, NIMA eliminates haptic "kickback" by ensuring zero force is applied to the user's hand when they release the handle, enhancing both patient safety and operator comfort. By accounting for nonlinearities in tool-tissue interactions, NIMA significantly improves force fidelity, responsiveness, and precision across various surgical conditions, advancing haptic feedback systems for reliable robot-assisted surgical procedures.