Multi‐Information Fuzzy Fusion for Position and Orientation Prediction in Robot‐Assisted Vascular Intervention Surgery

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.