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Multi-Information Fuzzy Fusion for Position and Orientation Prediction in Robot-Assisted Vascular Intervention
Zhi Hu1, Wenan Zhang2, Shaozong Xin1
1Laboratory of Intelligent Control and Robotics, Shanghai University of Engineering Science, Shanghai, China.
Background:
The absence of force feedback limits efficiency and operational safety in robot-assisted vascular surgery. Precise modelling of the contact between catheter and vascular wall is important for achieving accurate force feedback. The flexible catheter forms dynamic multi-point line contact with the vascular wall and undergoes continuous bending and torsion, making accurate modelling and high-precision registration challenging. In addition, system time delay affects the transparency of the system.
Methods:
This study proposes a multi-information fuzzy fusion prediction method that incorporates prior surgical experience to enable real-time estimation of catheter position and orientation. More specifically, an extended formulation of Fitts' law in a dual-motion collaborative mode is developed to estimate surgical movement time.
Results:
Experimental results demonstrate that the proposed method exhibits greater system transparency than that of the traditional extrapolation and Kalman prediction method. It can enhance force feedback fidelity.
Conclusions:
The proposed method can improve surgical safety.
