Data-Driven Modeling the Nonlinear Backlash of Steerable Endoscope Under a Large Deflection Cannulation in ERCP Surgery

摘要

Unexpected nonlinear backlash performances of two degrees of freedom (DoFs) tendon sheath mechanism (TSM) have serious impacts on the precise cannulation in ERCP (Endoscopic Retrograde Cholangio-Pancreatography) surgery. We proposed to model backlash performance of two DoFs steerable endoscope by using LSTM (Long Short-Term Memory) network. Trajectory following and orientation tasks are performed after the backlash model has been considered into the control strategy. Experimental results show that the LSTM backlash model can accurately describe the non-linearities of backlash, and significantly reduce the positioning and orientation deviation of the steerable endoscope under large deflection. The proposed backlash modeling method can be extended to be applied to bronchial, urethral and other natural orifice interventional tasks that work under a large deflection.