A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm

Guanwu Jiang,Keqiang Bai,Minzhou Luo,Saixuan Chen

doi:10.3390/app7100969

Abstract

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.

Highlights

Robots are currently used in industrial and medical applications where high accuracy, repeatability, and stability of the operations are required [1]
Combined with the back propagation (BP) neural network optimized by the particle swarm optimization (PSO) algorithm, the algorithm is trained by using the sample data [21]
The samples of the UR3 robot end position coordinates and Euler angles were used as the input nodes of the BP neural network, and the UR3 robot’s six joint angles for the 50 sets of data output the forecast sample; using the PSO algorithm to optimize the convergence, the BP neural network weights and thresholds repeatedly trained the six robot joint angles, providing 50 sets of data for the output prediction prediction samples

Summary

Introduction

Robots are currently used in industrial and medical applications where high accuracy, repeatability, and stability of the operations are required [1]. With the development of modern control technology, robot technology has been widely used in new fields, such as in medical robots. A surgical robot operating system is a collection of a number of modern, complex, high technologies, and the doctor, through the robot system, can perform surgical operations without touching patients. A minimally-invasive surgical robot is a combination of medical image processing technology and the operation of the mechanical arm to perform puncture surgery on the patient, to achieve minimal invasiveness, accuracy, efficiency, and stability. The most important problem of the serial robot, which is the solution of the kinematics of the manipulator, can be successfully implemented. Robot kinematics handles the mapping between joint

Objectives

Methods

Results

Conclusion