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

2026-01-20

AI Summary

This study addresses the critical limitation of current robotic-assisted minimally invasive surgery systems, which lack high-fidelity and safe force feedback to accurately convey instrument–tissue interaction forces. To overcome this challenge, the authors propose a Nonlinear Impedance Matching Approach (NIMA), which, for the first time, integrates nonlinear dynamic modeling into an impedance matching framework. By combining real-time force computation with safety-aware control strategies, NIMA achieves highly accurate and stable haptic rendering. The method substantially enhances force feedback fidelity, completely eliminating the undesirable “snap-back” effect when the user releases the interface. Experimental results demonstrate a mean absolute force error of 0.01 N (standard deviation: 0.02), representing a 95% reduction compared to conventional Improved Impedance Matching (IMA) methods, while simultaneously ensuring patient safety and operator comfort.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Interaction Techniques and Devices
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• haptic rendering
• robotic surgery
• force feedback
• tool-tissue interaction
• system safety

Innovation

• Nonlinear Impedance Matching
• Haptic Rendering
• Robot-Assisted Surgery
• Force Feedback
• Tool-Tissue Interaction