Method for improving positioning accuracy of robot based on support vector regression

To further improve the absolute positioning accuracy of a robot, a method for realizing the error prediction based on support vector regression (SVR) was proposed. First, an MDH model was used to establish a kinematic robot model, and SVR was used to establish the prediction model of the rotation angle and position error of a robot. Second, the grid division was controlled based on the spatial accuracy, and the relationship between the sampling points and the calibration accuracy was analyzed to establish an appropriate mode for the area division. Finally, the differences between the values of the theoretical and real position coordinates of the robot measured with a laser tracker were used to train the SVR model and compensate the single-point position errors. The experimental results indicate that the arithmetic mean error of the robot at the center, and the edge positions, are reduced from 2.107 mm and 2.182 mm to 0.103 mm and 0.123 mm, respectively. The correctness and effectiveness of the SVR for the absolute positioning error compensation of a robot are also verified. © 2020, Science Press. All right reserved.