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

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.