Dynamics Modeling of Industrial Robotic Manipulators: A Machine Learning Approach Based on Synthetic Data

Sandi Baressi Šegota, Nikola Anđelić, Mario Šercer and Hrvoje Meštrić
Additional contact information
Sandi Baressi Šegota: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Nikola Anđelić: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Mario Šercer: Development and Educational Centre for the Metal Industry—Metal Centre Čakovec, Bana Josipa Jelačića 22 D, 40000 Čakovec, Croatia
Hrvoje Meštrić: Ministry of Science and Education, Donje Svetice 38, 10000 Zagreb, Croatia

Mathematics, 2022, vol. 10, issue 7, 1-17

Abstract: 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 ( R 2 ) and mean absolute percentage error (MAPE), with 10-fold cross-validation applied. With these settings, all individual joint torque models achieved R 2 scores higher than 0.9, with the models for first four joints achieving scores above 0.95. Furthermore, all models for all individual joints achieve MAPE lower than 2%. The model for the total torque of all joints of the robotic manipulator achieves weaker regression scores, with the R 2 score of 0.89 and MAPE slightly higher than 2%. The results show that the torsion models of each individual joint, and of the entire manipulator, can be regressed using the described method, with satisfactory accuracy.

Keywords: industrial robot dynamics; machine learning; synthetic dataset generation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/7/1174/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/7/1174/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:7:p:1174-:d:786739

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().