Abstract. In the Building Mechatronic Research Centre we started to develop our cyberphysical system. The Department provided us all necessary equipment to realize the first cyberphysical system. The main core of the project was to create an augmented reality based navigation system in our robot laboratory. In that aspect we also built an internet of things ready automated guided vehicle prototype. It must be said that the internet of things has induced a new paradigm shift in the socio-economic world. Nowadays, augmented reality and virtual reality are industrial processes development tools. In recent years, these technologies demonstrated significant improvements in real-time industrial technology.
The Cyber-Physical and Intelligent Robotics Laboratory has been digitally recreated, and it includes all the key elements that allow 6-axis industrial robots to perform PTP, LIN, and CIRC motions. Furthermore, the user can create a program with these motion types. The human–machine interface is also integrated into our system. It can also assist SMEs in developing their in-house training. After all, training on an industrial robot unit does not entail installation costs within the facility. Nor are there any maintenance and servicing costs. Since the lab is digital, additional robot units can be added or removed. Thus, areas for training or production can be pre-configured within each facility. Because of the customizability and virtual education format, there is no room capacity problem, and trainees can participate in the exercises in parallel. Exercises were also conducted to evaluate the program’s impact on teaching, and the results showed that using machine units can improve teaching. Even today’s digital labs cannot physically convey the sense of space or the relative weights of different elements in virtual space. Even with these features, individuals can operate a machine more effectively than relying solely on traditional, non-interactive demonstration materials.
<p>Maintenance activities are integral within an industrial setting. The efficiency of these activities is associated with the total productivity of an industrial process/machine. A highly efficient maintenance policy/strategy usually results in relatively high levels of plant productivity. AR and VR may be incorporated into a maintenance strategy. AR and VR technology would enhance maintenance activities, and facilitate somewhat complex tasks.</p><p>A maintenance strategy includes a range of activities and may be categorized into administrative, technical and management processes. AR technology contains digital data, as well as other technical details, and is able to provide information about industrial machinery-equipment, without the need for equipment disassembling.</p><p>In this regard, we employed AR technology in developing a unique navigation system to replace/reduce the installation costs of traditional AGV navigation systems. The proposed AR system consists of a camera, which observes the QR-Code/Markers, and a processing unit.</p><p>Augmented Reality enables visualisation of any data and information, as well as control of a running process. It means it is possible to read various data within any equipment, during its operation and in real-time. This facilitates analysis of “black box” systems.</p>
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.
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