Neural Network Control of Industrial Robots Using ROS

Abstract

Neural networks (NNs) are able to model nonlinear systems with increasing accuracy. Further developments towards explainable artificial intelligence or the integration of already existing physical knowledge promote their acceptance and transparency. For these reasons, they are suitable for application in real systems, especially for modeling highly dynamic relationships. One possible application of NNs is the accuracy optimization of robot-based machining processes. Due to their flexibility and comparatively low investment costs, industrial robots (IR) are suitable for the machining of large components. However, due to their design characteristics, IRs show deficiencies with respect to their stiffness compared to traditional machine tools. One way to counteract these problems is to compensate for the compliance by means of model-based control. For this purpose, NNs can be used that predict the drive torques required in the axes. Compared to conventional analytical dynamics models, no complex identification of model parameters is necessary. In addition, NNs can take complex, nonlinear influences such as friction into account. In this work, NNs will be applied for a real-time model-based control of an IR using the Robot Operating System.