The robot-assisted insertion surgery plays a crucial role in biopsy and therapy. This study focuses on determining the optimum puncture pattern for robot-assisted insertion, aiming at the matching problem of needle insertion parameters, thereby to reduce the pain for patients and to improve the reachability to the lesion point. First, a 6-degrees of freedom (DOFs) Computed Tomography (CT)-guided surgical robotic system for minimally invasive percutaneous lung is developed and used to perform puncture experiments. The effects of four main insertion factors on the robotic puncture are verified by designing the orthogonal test, where the inserting object is the artificial skin-like specimen with high transparent property and a digital image processing method is used to analyze the needle tip deflection. Next, the various phases of puncture process are divided and analyzed in detail in view of the tissue deformation and puncture force. Then, short discussion on the comparison of puncture force with different effect factors for the same beveled needle is presented. The same pattern can be observed for all of the cases. Finally, based on the experimental data, the formulations of the puncture force and needle deflection which depends on Gauge size, insertion velocity, insertion angle, and insertion depth are developed using the multiple regression method, which can be used to get an optimum puncture pattern under the constrains of minimum peak force and minimum needle tip deflection. The developed models have the effectiveness and applicability on determining the optimum puncture pattern for one puncture event, and which can also provide insights useful for the setting of insertion parameters in clinical practice.
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