Abstract
Background: The single-port surgical robot can reduce incision size and accelerate postoperative recovery. This paper analyses the dynamic model of the remote centre mechanism (RCM) of the proposed single-port robot for force control. Methods: This paper proposes a dynamic model identification method for the RCM with a minimal parameter set derived from its tree structure. A nonlinear friction model for the prismatic joints and corresponding identification method are introduced, and the parameter set is iteratively refined using iterative reweighted least squares (IRLS), sequential quadratic programming (SQP) and an outlier detection algorithm. An adaptive Kalman filter (AKF) is applied to suppress noise in position differentiation, ensuring smooth velocity and acceleration. Results: The proposed method improves fitting accuracy and provides low-deviation predictions for cross-validation trajectory data. Conclusions: The proposed method enhances modelling accuracy and noise suppression in single-port surgical robots. Clinical Trial Registration: The authors declare that this research is not a clinical trial and is not registered with any clinical trial registry.
| Original language | English |
|---|---|
| Article number | e70105 |
| Journal | International Journal of Medical Robotics and Computer Assisted Surgery |
| Volume | 21 |
| Issue number | 5 |
| DOIs | |
| State | Published - Oct 2025 |
Keywords
- adaptive Kalman filter
- dynamic model identification
- friction model
- single-port surgical robot
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