Abstract
In interventional surgery, the manual operation of the catheter is not accurate. It is necessary to operate the catheter skillfully and effectively to protect the surgeon from radiation injury. The purpose of this paper is to design a new robot catheter operating system, which can help surgeons to complete the operation with high mechanical precision. On the basis of the original mechanical structure—real catheter, the operation information of the main end operator is collected. After the information is collected, the control algorithm of the system is improved, and the BP neural network is combined with the traditional PID controller to adjust the PID control parameters more effectively and intelligently so that the motor can reflect the output of the controller better and faster. The feasibility and superiority of the BP neural network PID controller are verified by simulation experiments.
Highlights
Vascular interventional surgery can be used for preoperative diagnosis and for practical surgical treatment, which is expected to get more applications in future medical practice
The mechanical structure design of the surgical robot system is divided into two parts: the main end and the slave end
It is an organic combination of the traditional PID controller and BP neural network
Summary
Vascular interventional surgery can be used for preoperative diagnosis and for practical surgical treatment, which is expected to get more applications in future medical practice. In interventional surgery, the catheter is inserted through the patient’s blood vessel In this process, any small mistake will hurt the patient and cause vascular damage. Compared with the human hand, there is no robot-assisted system that can meet all the technical requirements of vascular interventional therapy. In this respect, there are many similar application products and research results on the market. The da Vinci system (see Figure 1) provides doctors with more system stability and greater force support during catheter pushing compared with manual tools and can prevent unnecessary radiation exposure to doctors during more precise operations [2]. The BP neural network PID control algorithm is used to improve the accuracy and real-time manual control operation in the system
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