Mazidi, Mohammad (2025) Robust Haptics with Nonlinear Impedance Matching for Robot-assisted Laparoscopic Surgery. Masters thesis, Concordia University.
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Abstract
The integration of haptic feedback into robot-assisted minimally invasive surgery (RAMIS) has been constrained by challenges in accurately rendering forces while maintaining system stability and safety. Addressing these limitations, this research introduces the Nonlinear Impedance Matching Approach (NIMA), a novel force-rendering method designed to accurately model complex tool-tissue interactions. Building on the Impedance Matching Approach (IMA), NIMA incorporates nonlinear dynamics to enhance the precision and reliability of force feedback systems. The experimental results demonstrate that NIMA achieves a mean absolute error (MAE) of 0.01 ± 0.02 N, representing a 95% reduction in error compared to IMA. Notably, NIMA eliminates haptic ”kickback” by ensuring that no residual force is applied to the user’s hand when releasing the haptic device, significantly improving both user comfort and patient safety. Furthermore, its ability to account for the nonlinearities of tool-tissue interactions allows for high fidelity, responsiveness, and precision across diverse surgical conditions. This research advances the development of robust, high-performance haptic systems, offering a transformative solution to the challenges of force rendering in teleoperated surgical robotics. By providing a realistic and reliable interface for robotic-assisted surgical procedures, NIMA has the potential to enhance surgical precision, optimize patient outcomes, and set new standards for haptic feedback in RAMIS.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Mazidi, Mohammad |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Mechanical Engineering |
| Date: | 20 January 2025 |
| Thesis Supervisor(s): | Dargahi, Javad and Hooshiar, Amir |
| ID Code: | 995035 |
| Deposited By: | Mohammad Mazidi |
| Deposited On: | 17 Jun 2025 17:20 |
| Last Modified: | 17 Jun 2025 17:20 |
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