A Novel Calibration Method for Robot Kinematic Parameters Based on Improved Manta Ray Foraging Optimization Algorithm

Abstract

The positioning accuracy has a significant impact on the application of the robotic arm. In this study, a novel method based on improved manta ray foraging optimization (MRFO) algorithm is presented for kinematic parameters calibration, which can reduce the absolute positioning error of the manipulator. The error model of the robot is established according to the Denavit–Hartenberg (D–H) method. The redundancy of the model is analyzed, and the redundant parameters are eliminated. The kinematic parameter identification of the robot is transformed into a nonlinear optimization problem. The original MRFO algorithm is improved by using the natural selection strategy and adaptive parameter control strategy to improve searching performance and convergence speed. The calibration experiment of the self-designed 6-DOF robot arm is carried out. A measurement method for measuring the position of the robot end using a 3-D scanner is introduced. The improved MRFO (IMRFO) algorithm is used to identify the kinematic parameter errors of the robot. The experimental results show that by using the IMRFO algorithm to calibrate the parameter error, the position error of the robot end is reduced from 10.2357 to 0.5483 mm or by 94.6%. The results show that the IMRFO algorithm proposed in this article has high identification accuracy and fast convergence speed, which can effectively identify the kinematic parameter error of the robot.