An Approximation Model Based on Kernel Ridge Regression for Robot Kinematics Simulation

Cloud computing technologies have enabled a new paradigm for intelligent manufacturing system which is powered by utilizing distributed resources, such as collaborative robots, simulation engines, advanced algorithms and human resources. As one of the key issues, the mechanism for online kinematics control of serial robotic manipulator presents speed challenge in the cloud-based system. In this research, a kinematics approximation model based on kernel ridge regression is developed for cloud manufacturing environment. To begin with, the model input is generated using trigonometric functions of rotation angles with permutation tricks which significantly reduces statistical error. Then, the approximation model is trained using kernel ridge regression with radial basis function, where both regularization and bandwidth of kernel have been optimized using grid-search. In addition, Universal Robot 10 is adapted as a collaborative robot example in simulation comparison experiments in order to evaluate the performance of the kinematics approximation model. As demonstrated in the experiment results, the proposed modelling approach can effectively support the cloud simulation paradigm and efficiently meet the real-time speed requirement in a distributed manufacturing environment.