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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.
Intelligente Assistenzsysteme unterstützen die Mitarbeiter in der Produktion und erhöhen die Effizienz durch das Einblenden von situationsbasierten Aufgabeninformationen. Im Projekt Advanced Robot Assistance Solution (ARAS) im Rahmen des Kuka Innovation Awards 2021 wurde eine Assistenztechnologie entwickelt, um automatisiert Roboterprogramme für Montageabläufe zu generieren. Durch innovative Mensch-Maschine-Schnittstellen werden Montageschritte per maschinellem Lernen erkannt und in ausführbare Programme für Industrieroboter übersetzt. Dadurch können roboterbasierte Montageprozesse innerhalb von Minuten auf neue Produkte angepasst werden, ohne dass die Mitarbeiter über Kenntnisse des Programmierens oder der Robotik verfügen müssen. Ein Mitarbeiter muss den Montageprozess nur einmal vormachen. Das ARAS-System ermöglicht die kosten- und zeiteffiziente Integration und Adaption von Industrierobotern in der Montage für große und mittelständische Unternehmen.
Evaluation of Marker-Based AR-Tracking with Vuforia in the Context of Rail Vehicle Maintenance
(2023)
Maintenance and repair jobs present a multitude of challenges. Digital assistance systems (DAS) can be used to support workers in tackling these challenges. One way of achieving this is by using a tablet with AR-technology to display necessary information seemingly in the real world. In order to place information at the correct positions, the DAS needs to know exactly where it is currently located. Multiple approaches are available to achieve this. One of those approaches is to use a marker which is placed somewhere in the real world as an anchor point for the system. In the context of rail vehicle maintenance, the workspace is inherently large with rail wagons having lengths of more than 10 m. This means that a marker that is placed somewhere on the wagon will not always be in the field of view of the DAS’s camera, resulting in a possible reduction of the precision with which virtual objects can be displayed. This paper examines the viability of the marker-based approach under realistic circumstances. It was found that with a distance of multiple meters between marker and a spot that is to be highlighted, the precision will decrease significantly.
Abstract. As product specifications change, manufacturing processes have to adapt. In manual production tasks, the human worker is forced to adapt at the same pace. Fast-changing work tasks lead to high stress and therefore increase failures. Digital assistance systems aim to support the human workforce by providing assembly instructions at the right time and in the right place to reduce the cognitive load. The latest digital assistance systems provide multimodal humanmachine interfaces, such as augmented reality, haptic feedback, and voice control to provide information or react to the user’s input.However, those digital assistance systems require the manufacturing information themselves, which are mostly provided through text-based or graphical programming. Both manufacturing experts and programmers are needed to create a digital assistance system workflow or adapt it to changes. This process is costly, time-consuming, and inflexible. This work presents a gesture recognition based approach for a self-learning digital assistance system. Therefore, assembly gestures are classified based on anatomical grip descriptions. Assembly sequences are recognized and learned by the digital assistance system using machine learning techniques. The learned procedures are used to automatically generate work instructions and guide the worker through the assembly task.