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 areas depending on the industrial robot's absolute positioning accuracy, it is essential to improve the accuracy of absolute positioning through calibration. In this paper, an error model of the robot considering both constant and high-order joint-dependent kinematic errors is established, and the model is modified by the Hermite polynomial, thereby mitigating the occurrence of the Runge phenomenon. To identify high-order joint-dependent kinematic errors, a robot calibration method based on the back-propagation neural network (BP) optimized by the sparrow search algorithm (SSA-BP) is proposed, which optimizes the uncertainty of weights and thresholds in the BP algorithm. Experiments on an EFORT ECR5 robot were implemented to validate the efficiency of the proposed method. The positioning error is reduced from 3.1704 mm to 0.2798 mm, and the error decrease rate reaches 42.92% (compared with BP calibration) and 21.09% (compared with particle swarm optimization back-propagation calibration). 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.