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Design and Implementation of a Bespoke Robotic Manipulator for Extra-corporeal Ultrasound

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Modelling the Dynamics of the Remote Centre Mechanism in Single-Port Minimally Invasive Robot.

Bainan Liu¹, Dongsheng Li¹, Bo Pan²

¹State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS

|September 20, 2025

Summary

This study presents a new dynamic model for single-port surgical robots, improving force control accuracy and reducing noise for better surgical outcomes.

Keywords:

adaptive Kalman filter dynamic model identification friction model single‐port surgical robot

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Published on: October 14, 2017

Area of Science:

Robotics
Surgical Technology
Control Systems

Background:

Single-port surgical robots offer minimally invasive benefits like smaller incisions and faster recovery.
Accurate dynamic modeling is crucial for effective force control in these robots.
The remote centre mechanism (RCM) is a key component requiring precise modeling.

Purpose of the Study:

To develop and validate a dynamic model identification method for the RCM in single-port surgical robots.
To enhance the force control capabilities of these robotic systems.

Main Methods:

A minimal parameter set derived from the RCM's tree structure was used for dynamic model identification.
A nonlinear friction model for prismatic joints was incorporated and identified.
Iterative reweighted least squares (IRLS), sequential quadratic programming (SQP), and outlier detection refined the parameter set.
An adaptive Kalman filter (AKF) was employed for noise suppression in position differentiation.

Main Results:

The proposed method demonstrated improved fitting accuracy for the dynamic model.
Low-deviation predictions were achieved for cross-validation trajectory data.
Effective noise suppression was confirmed, ensuring smooth velocity and acceleration data.

Conclusions:

The developed dynamic model identification method enhances the overall modeling accuracy of single-port surgical robots.
The approach effectively suppresses noise, leading to more reliable control signals.
This contributes to the advancement of precise and safe robotic surgery.