FRAMEWORK FOR CRISIS-RESISTANT ENGINEERING PRODUCT DEVELOPMENT COURSES

This paper proposes a framework for the systematic adaptation and digitalisation of engineering product development courses in the event of a crisis. Applicants can use resources of the framework to identify crisis-related boundary conditions that impact the delivery of education and are assisted in determining the necessary level of course digitalisation to respond to the crisis. Furthermore, the framework comprehends a review of modern educational teaching objectives, as well as a table containing tools and methodologies linked to educational targets. These can be used to enhance course design to keep students independently of their learning profiles engaged in study activities and to uphold an excellent knowledge acquisition in a volatile environment. An exemplary application of the framework on a CAD course in a higher education context guides the educator through the processes

Object-Independent Human-to-Robot Handovers using Real Time Robotic Vision

We present an approach for safe and object-independent human-to-robot handovers using real time robotic vision and manipulation. We aim for general applicability with a generic object detector, a fast grasp selection algorithm and by using a single gripper-mounted RGB-D camera, hence not relying on external sensors. The robot is controlled via visual servoing towards the object of interest. Putting a high emphasis on safety, we use two perception modules: human body part segmentation and hand/finger segmentation. Pixels that are deemed to belong to the human are filtered out from candidate grasp poses, hence ensuring that the robot safely picks the object without colliding with the human partner. The grasp selection and perception modules run concurrently in real-time, which allows monitoring of the progress. In experiments with 13 objects, the robot was able to successfully take the object from the human in 81.9% of the trials.Comment: IEEE Robotics and Automation Letters (RA-L). Preprint Version. Accepted September, 2020. The code and videos can be found at https://patrosat.github.io/h2r_handovers

Object-Independent Human-to-Robot Handovers Using Real Time Robotic Vision

We present an approach for safe, and object-independent human-to-robot handovers using real time robotic vision, and manipulation. We aim for general applicability with a generic object detector, a fast grasp selection algorithm, and by using a single gripper-mounted RGB-D camera, hence not relying on external sensors. The robot is controlled via visual servoing towards the object of interest. Putting a high emphasis on safety, we use two perception modules: human body part segmentation, and hand/finger segmentation. Pixels that are deemed to belong to the human are filtered out from candidate grasp poses, hence ensuring that the robot safely picks the object without colliding with the human partner. The grasp selection, and perception modules run concurrently in real-time, which allows monitoring of the progress. In experiments with 13 objects, the robot was able to successfully take the object from the human in 81.9% of the trials.</p

EuProGigant – A Concept Towards an Industrial System Architecture for Data-Driven Production Systems

Today, most IoT solutions for the production ecosystem stem from trends that first established at the consumer market. Although these concepts have been adapted well in the industrial environment, it led to fragmented solutions that require complex interfaces. With the recent introduction of GAIA-X, it becomes possible to develop platform independent IoT solutions tailored to the needs of manufacturers. Until now, GAIA-X is only a concept proposed by governments and economic advisory boards. This paper extends the concept into an industrial system architecture that enables the reliable exchange of information among the supply chain of a highly distributed production network

EuProGigant Resilience Approach: A Concept for Strengthening Resilience in the Manufacturing Industry on the Shop Floor

Crises lead to adverse effects in the value creation ecosystem. In the long term, they lead to uncertainties that can destabilize the system. Resilience is getting more and more critical in connected, value-added ecosystems. Crises such as the Corona pandemic, the Suez Canal blockage, the chip crisis, and rising energy prices can cause sudden change in the market demand-supply equilibrium. Those can be expressed as calamities. This concept aims to create calamity-avoiding mechanisms in the manufacturing industry based on a common data infrastructure and smart use of data and services. Calamity-avoiding mechanisms are essential for unplannable and unknown disturbance factors connecting enterprise value creation networks in multiple layers. Resilience mechanisms must be distributed, decentralized, and interoperable to reduce the effect of self-reinforcement of calamities and enable self-orchestration functionalities. Gaia-X, the European initiative for creating a common and sovereign data infrastructure, offers data exchange based on the EU legal framework and is crucial for the (inter-)operability of the mechanisms. This paper presents the concept of such resilience mechanisms, the processing of data in the vertical plane (within a company), and the benefits at the horizontal level (across supply chains of companies). The concept is developed in the context of the EuProGigant project. It follows a bottom-up approach and starts with the Self-Descriptions (SD) of all assets on the shop floor. The resilience approach includes five key points: SDs, stress scenarios and stress mechanisms, system theory and control, anomaly detection services, and self-orchestration

Object-Independent Human-to-Robot Handovers Using Real Time Robotic Vision

Object-Independent Human-to-Robot Handovers Using Real Time Robotic Visio

Edge-Computing im Projekt EuProGigant

Im Rahmen des österreichisch-deutschen Leitprojekts für Gaia-X in der produzierenden Industrie namens EuProGigant wird eine gemeinsame Dateninfrastruktur nach den Prinzipien von Gaia-X für das Wertschöpfungsökosystem konzipiert und umgesetzt. Das Ziel des Projekts ist die Demonstration und Skalierung eines standortübergreifenden, digital vernetzten Produktionsökosystems mit resilienter, datengetriebener und nachhaltiger Wertschöpfung zur Stärkung der europäischen Vorreiterrolle in der Industrie. Der Fokus im Projekt liegt auf der Anbindung diverser Maschinen und Anlagen unabhängig von Herstellern und Software- bzw. Firmwareständen der Steuerungskomponenten. Neben Anforderungen an eine gemeinsame Dateninfrastruktur hinsichtlich IT-security, Safety, Zuverlässigkeit,Schnittstellenkonfiguration zur Interoperabilität und ein funktionierendes Update-Management sind die Anforderungen zu integrierender digitaler Funktionen (Services) heterogener Herkunft zu nennen, welche in der Gaia-X Architektur über die Federation Services aus dem Daten-Ökosystem bezogen werden. Hohes Potenzial zur Umsetzung von industriellen Anwendungsfällen besteht in der Nutzung von Daten aus Produktionsprozessen. Insbesondere die hochfrequente, zeitsynchrone Datenerfassung und -verarbeitung mittels Services im Edge-Computing auf dem Shop Floor wird als Treiber für digitale datengetriebene Geschäftsmodelle in der Beschreibung von Datenwertschöpfungsketten gesehen. Das White Paper legt die begriffliche Basis für das Edge-Computing im Projekt EuProGigant und soll über das Projekt hinaus das Verständnis für die vielfältige Nutzung von Edge-Systemen in der Produktion im Zusammenhang mit Gaia-X schärfen