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001-es BibID:	BIBFORM121684
035-os BibID:	(WoS)001257212500001 (Scopus)85197315831
Első szerző:	Erdei Timotei István (mechatronikai mérnök)
Cím:	Image-to-Image translation-based deep learning application to object identification in industrial robot systems / Timotei István Erdei, Tibor Péter Kapusi, András Hajdu, Géza Husi
Dátum:	2024
ISSN:	2218-6581
Megjegyzések:	Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition are not possible with the machinery we have. We therefore propose a novel deep learning approach for existing robotic devices that can be applied to future robots without modification. In the implementation, 3D CAD models of the PCB relay modules to be recognized are also designed for the implantation machine. Alternatively, we developed and manufactured parts for the assembly of aluminum profiles using FDM 3D printing technology, specifically for sorting purposes. We also apply deep learning algorithms based on the 3D CAD models to generate a dataset of objects for categorization using CGI rendering. We generate two datasets and apply image-to-image translation techniques to train deep learning algorithms. The synthesis achieved sufficient information content and quality in the synthesized images to train deep learning algorithms efficiently with them. As a result, we propose a dataset translation method that is suitable for situations in which regenerating the original dataset can be challenging. The results obtained are analyzed and evaluated for the dataset.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk deep learning cyber-physical neural networks industry 4.0 image-to-image dataset translation
Megjelenés:	Robotics. - 13 : 6 (2024), p. 1-21. -
További szerzők:	Kapusi Tibor Péter (1993-) (mérnökinformatikus, villamosmérnök) Hajdu András (1973-) (matematikus, informatikus) Husi Géza (1962-) (gépészmérnök, mechatronikai mérnök, számítógépes tervezőmérnök)
Pályázati támogatás:	TKP2020-NKA-04 Egyéb
Internet cím:	DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM134007
Első szerző:	Kapusi Tibor Péter (mérnökinformatikus, villamosmérnök)
Cím:	SCARA Assembly AI : The Synthetic Learning-Based Method of Component-to-Slot Assignment with Permutation-Invariant Transformers for SCARA Robot Assembly / Kapusi Tibor Péter, Erdei Timotei István, Abdullah Masuk, Husi Géza, Hajdu András
Dátum:	2025
ISSN:	2218-6581
Megjegyzések:	This paper presents a novel synthetic learning-based approach for solving the component-to-slot assignment problem in robotics using a SCARA robot. The method uses a fully simulated environment that generates and annotates scenes based on rules and visual features. Within this environment, we train a permutation-invariant neural model to predict correct assignments between detected components and predefi ned target slots. Set Transformer-based encoders are combined with a self-attention MLP scoring head. Assignment prediction is optimized using an improved soft Hungarian loss function. To increase data realism and generalizability, we implement a synthetic dataset generation module on the NVIDIA Omniverse platform. This setup enables precise control over scene composition and component placement. The resulting model achieves high matching accuracy on complex layouts with variable numbers of components and demonstrates strong generalization across multiple confi gurations. Our results validate the feasibility of learning bijective mappings in simulated assembly scenarios, providing a foundation for scalable real-world robotic pick-and-place tasks. Tests were also conducted on actual robot units.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk SCARA robot permutation invariant transformers set transformers synthetic learning pick-and-place NVIDIA Omniverse bijective mapping
Megjelenés:	Robotics. - 14 : 12 (2025), p. 1-36. -
További szerzők:	Erdei Timotei István (1990-) (mechatronikai mérnök) Abdullah, Masuk (1997-) (mechatronics) (tanszéki mérnök) Husi Géza (1962-) (gépészmérnök, mechatronikai mérnök, számítógépes tervezőmérnök) Hajdu András (1973-) (matematikus, informatikus)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM102588
035-os BibID:	(WoS)000845243900001 (Scopus)85133535530
Első szerző:	Kapusi Tibor Péter (mérnökinformatikus, villamosmérnök)
Cím:	Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection / Tibor Péter Kapusi, Timotei István Erdei, Géza Husi, András Hajdu
Dátum:	2022
ISSN:	2218-6581
Megjegyzések:	In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.
Tárgyszavak:	Műszaki tudományok Gépészeti tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk cyber-physical systems Industry 4.0 SCARA robot deep learning YOLO
Megjelenés:	Robotics. - 11 : 4 (2022), p. 1-20. -
További szerzők:	Erdei Timotei István (1990-) (mechatronikai mérnök) Husi Géza (1962-) (gépészmérnök, mechatronikai mérnök, számítógépes tervezőmérnök) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:	TKP2020-NKA-04 Egyéb
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat DOI Szerző által megadott URL
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001-es BibID:	BIBFORM105830
035-os BibID:	(Scopus)85147571708
Első szerző:	Maaruf, Muhammad
Cím:	Neural Network-based Finite-time Control of Nonlinear Systems with Unknown Dead-zones : Application to Quadrotors / Muhammad Maaruf, Aminu Babangida, Husam A. Almusawi, Peter Szemes Tamas
Dátum:	2022
ISSN:	2715-5072 2715-5056
Megjegyzések:	Over the years, researchers have addressed several control problems of various classes of nonlinear systems. This article considers a class of uncertain strict feedback nonlinear system with unknown external disturbances and asymmetric input dead-zone. Designing a tracking controller for such system is very complex and challenging. This article aims to design a finite-time adaptive neural network backstepping tracking control for the nonlinear system under consideration. In addition, all unknown disturbances and nonlinear functions are lumped together and approximated by radial basis function neural network (RBFNN). Moreover, no prior information about the boundedness of the dead-zone parameters is required in the controller design. With the aid of a Lyapunov candidate function, it has been shown that the tracking errors converge near the origin in finite-time. Simulation results testify that the proposed control approach can force the output to follow the reference trajectory in a short time despite the presence of asymmetric input dead-zone and external disturbances. At last, in order to highlight the effectiveness of the proposed control method, it is applied to a quadrotor unmanned aerial vehicle (UAV).
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Quadrotor unmanned aerial vehicle backstepping control radial basis function neural network dead-zone nonlinear systems
Megjelenés:	Journal of Robotics and Control (JRC). - 3 : 6 (2022), p. 735-742. -
További szerzők:	Babangida, Aminu (1988-) (Informatics)(mérnök) Neamah, Husam A. (1990-) (mérnök) Szemes Péter Tamás (1976-) (gépészmérnök, villamosmérnök)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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