Low cost solution for calibration in absolute accuracy of an industrial robot for iCPS applications

Abstract

The development of industrial cyber-physical systems (iCPS) and its extended application, many of the functions and applications of industrial robots will require self-control improvements. Due to the need in the industry, this paper presents a calibration method to resolve the absolute accuracy of robot TCP movement in a limited workspace problem. We implement this low-cost method with camera tracking which in a much lower cost fashion than general purpose laser tracking device. We use Rectified Linear Unit (ReLU) method based on deep neural network (DNN) which can be more efficient than traditional empirical compensation method in improving the error offset calculation and calibration accuracy. In addition, the justification for selecting training data, the methods for designing DNN models and the schemes for improving DNN models are also described in details.