A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network
Robot position accuracy plays an important role in advanced industrial applications. In this paper, a new calibration method for enhancing robot position accuracy is proposed. In order to improve robot accuracy, the method first models and identifies its geometric parameters using an extended Kalman filtering (EKF) algorithm. Because the non-geometric error sources (such as the link deflection errors, joint compliance errors, gear backlash, and so on) are either difficult or impossible to model correctly and completely, an artificial neural network (ANN) will be applied to compensate for these un-modeled errors. The combination of model-based identification of the robot geometric errors using EKF and a compensation technique using the ANN could be an effective solution for the correction of all robot error sources. In order to demonstrate the effectiveness and correctness of the proposed method, simulated and experimental studies are carried out on serial PUMA and HH800 manipulators, respectively. The enhanced position accuracy of the robots after calibration confirms the practical effectiveness and correctness of the method.
extended kalman filter artificial neural network geometric parameter robot calibration non-geometric parameter kinematic identification.