Improving Position Accuracy of Robot Manipulators Using Neural Networks

In this paper, a neural network based method was presented to estimate the position errors in a robot manipulator calibration process. We use a set of measurement within a window to form the input and output pairs of training data. The utilization of a window of data helps to exploit the local feature of the underline mapping function and also reduce the complexity of the neural network model. After training is completed, the neural network model could be used to approximate the mapping between the location on the board and the position errors of manipulator within the calibration space. The proposed algorithm improves the accuracy of the error estimation in comparison with traditional analytical methods. The experiment result demonstrates the effective of the proposed method