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

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 identificatio...

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Veröffentlicht in:The international journal of medical robotics + computer assisted surgery Jg. 21; H. 5; S. e70105 - n/a
Hauptverfasser: Liu, Bainan, Li, Dongsheng, Pan, Bo, Ai, Yue, Gao, Wenpeng, Fu, Yili
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Wiley Subscription Services, Inc 01.10.2025
Schlagworte:
Accuracy
Adaptive algorithms
adaptive Kalman filter
Algorithms
Clinical trials
dynamic model identification
Dynamic models
Equipment Design
friction model
Humans
Identification methods
Kalman filters
Laparoscopy - methods
Least-Squares Analysis
Minimally Invasive Surgical Procedures - instrumentation
Minimally Invasive Surgical Procedures - methods
Models, Theoretical
Noise reduction
Nonlinear Dynamics
Outliers (statistics)
Parameter identification
Quadratic programming
Robot control
Robotic Surgical Procedures - instrumentation
Robotic Surgical Procedures - methods
Robots
single‐port surgical robot
single‐port surgical robot
adaptive Kalman filter
dynamic model identification
friction model
ISSN:1478-5951, 1478-596X, 1478-596X
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Zusammenfassung: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
Bibliographie:This work was financially supported by Zhejiang Province Key Research and Development Program: development of core components and system of surgical robot (Grant No. 2023C03010) and the National Nature Science Foundation of China (Grant No. 92048202).
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ISSN:1478-5951
1478-596X
1478-596X
DOI:10.1002/rcs.70105