A Robot Calibration Method Based on Joint Angle Division and an Artificial Neural Network

Robot calibration is used to improve the accuracy of the kinematic model to achieve the higher positioning accuracy within the workspace. Due to some nongeometrical reasons such as joint and link flexibility, the errors are unevenly distributed in the workspace. In this case, it is difficult for the existing methods used to improve the absolute positioning accuracy to achieve good results in each region, especially for robots with large self-weights. In this paper, a novel calibration method is proposed, which deals with joint deflection dependent errors to enhance the robot positioning accuracy in the whole workspace. Firstly, the joint angle workspace is divided into several local regions according to the mass distribution of the robot. Then, its geometric parameters are modeled and identified using the Denavit-Hartenberg (DH) model in each region and in the whole workspace separately. Since the nongeometric error sources are difficult to model correctly, an artificial neural network (ANN) is applied to compensate for the nongeometric errors. Finally, the experiments using an 8 degree-of-freedom (DOF) engineering robot are conducted to demonstrate the validity of the proposed method. The combination of the joint angle division and ANN could be an effective solution for the robot calibration, especially for one with a large self-weight.