Enhancing Robot Calibration through Reliable High-Order Hermite Polynomials Model and SSA-BP Optimization

Various sources of error can lead to the position accuracy of the robot being orders of magnitude worse than its repeatability. For the accuracy of drilling in the aviation field, high-precision assembly, and other fields depend on the industrial robot's absolute positioning accuracy, it is essential to improve the accuracy of absolute positioning by calibration. In the present paper, an error model of the robot is established considering both constant and joint-dependent kinematic errors, and the robot model is modified by the Hermite polynomial. To identify joint-dependent kinematic errors, a robot calibration method based on back-propagation neural network(BP) optimized by Sparrow Search Algorithm (SSA-BP) is proposed, which optimize the uncertainty of weights and thresholds in the BP algorithm . To validate the efficiency of the proposed method, experiments on an EFORT ECR5 robot were implemented. The positioning error is reduced from 3.1704 mm to 0.2798 mm, and the positioning accuracy is improved by 91.27%. With the new calibration method using SSA-BP, robot positioning errors can be effectively compensated for and the robot positioning accuracy can be improved significantly.