Mathematics | |
Dynamics Modeling of Industrial Robotic Manipulators: A Machine Learning Approach Based on Synthetic Data | |
Mario Šercer1  Nikola Anđelić2  Sandi Baressi Šegota2  Hrvoje Meštrić3  | |
[1] Development and Educational Centre for the Metal Industry—Metal Centre Čakovec, Bana Josipa Jelačića 22 D, 40000 Čakovec, Croatia;Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia;Ministry of Science and Education, Donje Svetice 38, 10000 Zagreb, Croatia; | |
关键词: industrial robot dynamics; machine learning; synthetic dataset generation; | |
DOI : 10.3390/math10071174 | |
来源: DOAJ |
【 摘 要 】
Obtaining a dynamic model of the robotic manipulator is a complex task. With the growing application of machine learning (ML) approaches in modern robotics, a question arises of using ML for dynamic modeling. Still, due to the large amounts of data necessary for this approach, data collection may be time and resource-intensive. For this reason, this paper aims to research the possibility of synthetic dataset creation by using pre-existing dynamic models to test the possibilities of both applications of such synthetic datasets, as well as modeling the dynamics of an industrial manipulator using ML. Authors generate the dataset consisting of 20,000 data points and train seven separate multilayer perceptron (MLP) artificial neural networks (ANN)—one for each joint of the manipulator and one for the total torque—using randomized search (RS) for hyperparameter tuning. Additional MLP is trained for the total torsion of the entire manipulator using the same approach. Each model is evaluated using the coefficient of determination (
【 授权许可】
Unknown