The problem of inverse kinematics is fundamental in robot control. Many traditional inverse kinematics solutions, such as geometry, iteration, and algebraic methods, are inadequate in high-speed solutions and accurate positioning. In recent years, the problem of robot inverse kinematics based on neural networks has received extensive attention, but its precision control is convenient and needs to be improved. This paper studies a particle swarm optimization (PSO) back propagation (BP) neural network algorithm to solve the inverse kinematics problem of a UR3 robot based on six degrees of freedom, overcoming some disadvantages of BP neural networks. The BP neural network improves the convergence precision, convergence speed, and generalization ability. The results show that the position error is solved by the research method with respect to the UR3 robot inverse kinematics with the joint angle less than 0.1 degrees and the output end tool less than 0.1 mm, achieving the required positioning for medical puncture surgery, which demands precise positioning of the robot to less than 1 mm. Aiming at the precise application of the puncturing robot, the preliminary experiment has been conducted and the preliminary results have been obtained, which lays the foundation for the popularization of the robot in the medical field.
Percutaneous image‐guided interventions are increasing in number in clinical practice because they are minimally invasive. Needle positioning placement is crucial and highly dependent on the physician's skills and experience, it is often the longest part of the intervention. Medical robotics and computer‐assisted surgery are hotspots in the field of robotics and medicine, changing the essence of traditional surgery using a combination of robotic, image processing, and computer technologies. The present paper aimed to study the auxiliary puncture procedure using a robot based on optical positioning technology that can be used to mark points in puncturing operation. Binocular camera is used for image acquisition, and Zhang's calibration method is used to establish the binocular camera model. In addition, the circular markers are identified by the least square method detection circle, and the coordinate information of the markers in three‐dimensional space is solved by using the visual depth information of binocular phases. This paper studies the verification of the three‐dimensional bone model of the human body, which lays a foundation for the application of the assistant puncture robot.
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