TY - CHAP A1 - Bilous, Vadym A1 - Porsch, Ronny A1 - Spanoudakis, Konstantinos ED - Madsen, Ole ED - Berger, Ulrich ED - Møller, Charles ED - Heidemann Lassen, Astrid ED - Vejrum Wæhrens, Brian ED - Shou, Casper T1 - Augmented Reality: Increasing Availability and Its Implication for SMEs T2 - The Future of Smart Production for SMEs: A Methodological and Practical Approach Towards Digitalization in SMEs N2 - The Augmented reality (AR) technologies have been first discovered in the third quarter of the twentieth century. However, the wider development of them has taken place only in the last two decades. By now, the research has shown that AR can be used in various areas of human activity. In industry, AR simplifies humanmachine communication and improves human-machine interfaces (HMI) for fast and feedback-provided retrieval of training/guidance information for operation pattern study, error correction, machine maintenance, assembly assistance, etc. In spite of that, the broad practical implementation of AR in industry, including small and medium-sized enterprises (SMEs), has faced considerable problems. As a result, the following controversy emerged: the comprehensive study of AR is combined with a rather narrow practical use primarily for advertising and demonstration tasks. This chapter attempts not only to overview the current state of AR in the industry, but also demonstrate the current challenges the AR is facing, as well as to analyse their respective causes and suggest solution ideas. It is also intended to assess the prospects for further development of AR and its continued integration into the industry. For this purpose, several examples of AR projects, their development, practical use and upgrading (performed by the authors of this study as well) are presented. Y1 - 2022 SN - 978-3-031-15427-0 SP - 349 EP - 356 PB - Springer International Publishing CY - Cham ET - 1. Auflage ER - TY - GEN A1 - Bilous, Vadym A1 - Sarachuk, Kirill T1 - Can small and medium enterprises benefit from AR technology? Current challenges and trends T2 - Human Interaction & Emerging Technologies N2 - Albeit augmented reality (AR) technologies first have been discovered in the third quarter of the 20th century, their widespread use began just two decades ago. Existing paper trails show that AR has a wide range of industrial application: it simplifies human-machine communication, improves human-machine interfaces (HMI) for quick information exchange in training (including feedback to study the workflow), correction of errors, machine maintenance, assembly assistance etc. However, broader industrial acceptance of AR, prior to all by small and medium-sized enterprises (SMEs), recently faced considerable problems and the expansion of AR solutions does not match the high potential it has demonstrated. That results in a limited practical use, mainly for demonstration and advertising purposes. This short review is to present the state of the art of the industry, challenges that SMEs face in adopting AR technologies, and several practical examples of a (commercial) adoption of AR. Some prospects for further development of AR and its ongoing integration into industry are briefly discussed in the summary. Y1 - 2023 UR - https://openaccess.cms-conferences.org/publications/book/978-1-958651-87-2/article/978-1-958651-87-2_37 U6 - https://doi.org/10.54941/ahfe1004040 VL - 111 / 2023 SP - 333 EP - 348 PB - IHIET 2023 ER - TY - GEN A1 - Bilous, Vadym A1 - Sarachuk, Kirill T1 - Are SMEs Ready for AI Embedded Mobile Robots? T2 - Human Interaction and Emerging Technologies N2 - The introduction of both mobile robots (MR) and AI-embedded mobile robots (AIMR) into the industry is very slow compared to other types of industrial robots (IR) and automation systems. Many scientific articles and studies are focused on the programming and design of MR. At the same time, integration issues, topical problems and related obstacles are almost entirely absent from the scientific literature. The authors of this paper acknowledge that the complete analysis of this area is a very challenging task. Therefore, for the purposes of this study, we focus on the local problem of analysing the introduction of MR and AIMR in small and medium-sized enterprises (SMEs). The authors offer the analysis of the current challenges and trends in the introduction of mobile robots into SMEs. They also propose solutions to these problems based on their own as well as external experience in the design, programming and implementation of mobile robots. Y1 - 2023 UR - https://openaccess.cms-conferences.org/publications/book/978-1-958651-87-2/article/978-1-958651-87-2_32 U6 - https://doi.org/10.54941/ahfe1004035 VL - 111 / 2023 SP - 285 EP - 296 PB - IHIET 2023 ER - TY - CHAP A1 - Berger, Ulrich A1 - Bilous, Vadym A1 - Andulkar, Mayur ED - Bullinger, Angelika C. T1 - Anwendung von der Augmented Reality für die Mensch-Roboter Interaktion bei der Fehlerbeseitigung und bei der Maschinenbedienung T2 - 3D SENSATION - transdisziplinäre Perspektiven N2 - Die in diesem Artikel beschriebene, praktische, Verwendung einer Augmented Reality Umgebung findet statt für die Fehlerbeseitigung und Fehlerkorrektur bei der Arbeit in einer Laboranlage, die verschiedene Fehler simuliert kann. Innerhalb der Anlage sind alle wesentlichen Komponenten mit Sensorik ausgestattet, so dass jederzeit Informationen über den Zustand des Systems in Echtzeit bereitliegen. Sobald sich einen Fehler innerhalb der Anlage befindet, kann das Steuerungssystem durch die vorliegenden Sensordaten dieses Problem identifizieren. Die verschiedenen Teile der Anlage sind mit Positions-Markern zur Identifizierung eines Anlagenbereiches ausgerüstet. Wird die Anlage durch einen Fehler gestoppt ist, muss der Mitarbeiter die Anlage mit Hilfe des Kamerasystems eines mobilen Endgerätes (mit dem Tablet) erfassen. Die erfassten Daten aus diesem Scan werden mit Hilfe der Software verarbeitet. Durch die visuelle Rückmeldung (z.B., auf dem Tablet) erhält der Anwender die Information über den aufgetretenen Fehler innerhalb der Anlage und zur Behebung des Zustandes. Auf Grundlager der erkannten Marker können positionsgenau Objekte eingeblendet werden die dem Anwender den Ort des Fehlers anzeigen und Hinweise zur Bedienerführung zur Aufnahme des ordnungsgemäßen Betriebs der Anlage geben. Der Anlagenführer wird in die Lage versetzt, die Fehlerbeseitigung und Fehlerkorrektur schnell und erfolgreich zu realisieren. Ein Einsatz des Systems für Schulungsprozesse ist vorgesehen, da sich dies positiv auf die Verringerung von Stillstandszeiten auswirkt. So demonstriert das beschriebene, realisierte Verfahren wie die praktische Verwendung der AR zur Reduzierung des zeitlichen Aufwandes für die Fehlerbeseitigung und die Fehlerkorrektur der Maschinenumgebung bei der Mensch-Maschinen-Integration in Industrie 4.0 Umgebungen beiträgt und den gesamten Integrationsprozess zwischen Mensch und Maschine vertiefet. Y1 - 2016 UR - http://www.innteract2016.de/Herausgeberband_innteract2016.pdf SN - 978-3-944192-07-9 SP - 37 EP - 38 PB - aw&I Wissenschaft und Praxis CY - Chemnitz ER - TY - CHAP A1 - Bilous, Vadym A1 - Städter, Philipp A1 - Gebauer, Marc A1 - Berger, Ulrich ED - Schüppstuhl, Thorsten ED - Tracht, Kirsten ED - Raatz, Annika T1 - Usage of Augmented Reality for Improved Human-Machine Interaction and Real-Time Error Correction of Laboratory Units T2 - Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021 KW - Augmented reality KW - Error correction KW - Human-machine interaction KW - Industry 4.0 KW - Technical assistance systems KW - Data transfer Y1 - 2022 SN - 978-3-030-74031-3 U6 - https://doi.org/10.1007/978-3-030-74032-0_22 SP - 263 EP - 274 PB - Springer CY - Cham ER - TY - GEN A1 - Hodapp, Jan A1 - Schiemann, Markus A1 - Bilous, Vadym A1 - Arcidiacono, Claudio Salvatore A1 - Reichenbach, Matthias T1 - Advances in Automated Generation of Convolutional Neural Networks from Synthetic Data in Industrial Environment T2 - 53rd Hawaii International Conference on System Sciences: Maui, Hawaii, United States of America, Jan 07 - 10, 2020 N2 - The usage of convolutional neural networks has revolutionized data processing and its application in the industry during the last few years. Especially detection in images, a historically hard task to automate is now available on every smart phone. Nonetheless, this technology has not yet spread in the industry of car production, where lots of visual tests and quality checks are still performed manually. Even though the vision capabilities convolutional neural networks can give machines are already respectable, they still need well prepared training data that is costly and time-consuming to produce. This paper describes our effort to test and improve a system to automatically synthesize training images. This existing system renders computer aided design models into scenes and out of that produces realistic images and corresponding labels. Two new models, Single Shot Detector and RetinaNet are retrained under the use of distractors and then tested against each other. The better performing RetinaNet is then tested for performance under training with a variety of datasets from different domains in order to observe the models strength and weakness under domain shifts. These domains are real photographs, rendered models and images of objects cut and pasted into different backgrounds. The results show that the model trained with a mixture of all domains performs best. Y1 - 2020 UR - https://hdl.handle.net/10125/64307 SN - 978-0-9981331-3-3 U6 - https://doi.org/10.24251/HICSS.2020.565 ER -