Laparoscopic Field of View (FOV) control is one of the most fundamental andimportant components in Minimally Invasive Surgery (MIS) We present adata-driven framework to realize an automated laparoscopic optimal FOV control . We offline learn a motion strategy of laparoscoperelative to the surgeon’s hand-held surgical tool from our in-house surgicalvideos . To eliminate misorientation of FOVcaused by Remote Center of Motion (RCM) constraints when moving thelaparoscope, we propose a novel distortion constraint using an affine map to minimize the visual warping problem . A null-space controller is also embedded into the framework to optimize all types of errors in a unified anddecoupled manner . Experiments are conducted using Universal Robot (UR) and KarlStorz Laparoscope/Instruments . We prove the feasibility of our domainknowledge and learning enabled framework for automated camera control. Experiments were conducted using U.S. and Karl Storz laparoscope and U.N. Laparosc

Author(s) : Bin Li, Bo Lu, Yiang Lu, Qi Dou, Yun-Hui Liu

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Keywords : control - framework - laparoscope - automated - motion -

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