Vascular Interventional Surgery Robot System Design And Guidewire Tracking Methods Research

Vascular interventional surgery is the main method for the treatment of cardiovascular diseases at present,but the traditional interventional surgery is more harmful to the life and health of doctors,and the existing vascular interventional surgery robot can only deliver the guide wire or catheter alone,unable to complete the complex vascular interventional operation.In addition,the lack of intraoperative information and the low degree of intelligence are also another important reason affecting the surgical effect.Therefore,this paper designed a vascular interventional surgical robot for bifurcated lesions,which can realize multi-instrument cooperative delivery,and proposes a guide wire tracking method.First of all,through the analysis of the movement of vascular interventional surgery instrument,the design index of the vascular interventional surgery robot was determined,and the main end and secondary end of the surgical robot were designed.The motion principle of the master mechanism and the way to realize the cooperative delivery of multiple instruments from the slave end were expounded,and realized the force interaction between the master and slave through the master force feedback component and the slave force detection mechanism,provided the real feedback force,and completed the prototype construction.Secondly,the traditional PID control system was built by analyzing the dynamic model of the axial transmission motion of the slave actuator.In view of the non-deterministic interference factors existing in practice,fuzzy control was used to optimize PID system.Considering that the selection of fuzzy control rules is subjective and arbitrary,genetic algorithm was used to optimize the formulation of fuzzy rules to improve the response speed and anti-interference ability of the system.Thirdly,in view of medical image is not easy to identify,intraoperative problem of lack of useful information,an image segmentation method combining deep learning and attention mechanism was designed to improve the effect of image segmentation.Due to the time series nature of intraoperative image data,a method of extracting the coordinates of the center point of the guide wire tip was proposed by comparing the images in different time frames.The cubic spline interpolation method was used for fitting,and the motion trajectory of the guide wire was reconstructed to realize the tracking of the guide wire.Finally,a master-slave experimental platform was built to verify the performance of the vascular interventional surgical robot.The master-slave control precision of the robot system was verified by the master-slave propulsion displacement following experiment.The effectiveness of the method of drag extraction and force compensation and the safety of the robot system were verified by the experiment of resistance detection and force feedback.The feasibility of multi-instrument cooperative delivery by surgical robot was verified by blood vessel model experiment.