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We are seeing a boom in the use of real-time position data to automate and optimize tasks in the field of production and logistics. Here we consider the reasons for this, show what has already proven to be industrially viable and give an overview of six current research efforts. We show which use cases have been automated by RTLS and where RTLS could play a role to further optimize production processes or material flows in the future.

Zukunft mit RTLS: Wir stehen am Anfang eines Booms bei der Nutzung von Echtzeit-Positionsdaten für Automatisierung- und Optimierungszwecke im Bereich der Produktion und Logistik. In dieser Publikation geben wir eine Antwort auf das Warum,zeigen, was sich bereits als industriell machbar erwiesen hat und geben einen Überblick über sechs aktuelle Ansätze aus der Forschung. Wir zeigen, welche Anwendungsfälle durch RTLS bereits automatisiert werden können und wo RTLS in Zukunft eine Rolle spielen könnte, um Produktionsprozesse oder Materialflüsse weiter zu optimieren.

Data-science-driven development projects are increasingly gaining the attention of small and medium sized enterprises. Since SME are often lacking the necessary competencies in data science, cooperation with other companies or universities is required. The efficient handling of data is one of the main challenges in joint cross-enterprise development projects. Actual cost driver is the development of data by labeling and classifying the data by domain experts, which is very time-consuming and labor-intensive with large amounts of data. Furthermore, clearance processes also have a high potential to cause delays before data can be shared with project partners. Moreover, before the actual work can begin, it is often necessary to clean up and repair incomplete or noisy data. The concept of Data Administration Shell presented in this paper addresses the challenge of structured information sharing and information management in joint cross-enterprise engineering. The Data Administration Shell links data sets to information regarding data origin and already performed analyses including their results and program scripts. Adding relations and documentation facilitates the reuse of data sets for subsequent projects. For this purpose, the Data Administration Shell adapts the concepts serving the information sharing in the research field of manufacturing and Digital Twin. The evaluation of the Data Administration Shell was based on time-series measurement data from a production process optimization scenario. Here, the Data Administration Shell manages the data sets of time series data and facilitates the joint cross-enterprise engineering of data-driven solutions.

Developing consistently well performing visual recognition applications based on convolutional neural networks, e.g. for autonomous driving, is very challenging. One of the obstacles during the development is the opaqueness of their cognitive behaviour. A considerable amount of literature has been published which describes irrational behaviour of trained CNNs showcasing gaps in their cognition. In this paper, a methodology is presented that creates worstcase images using image augmentation techniques. If the CNN's cognitive performance on such images is weak while the augmentation techniques are supposedly harmless, a potential gap in the cognition has been found. The presented worst-case image generator is using adversarial search approaches to efficiently identify the most challenging image. This is evaluated with the well-known AlexNet CNN using images depicting a typical driving scenario., to be published in 16th CASE 2021: Lyon

Zusammenfassung Durch zunehmende Agilität im Entwicklungsprozess, kürzere Lebenszyklen und sich ändernde Kunden- und Gesetzgeberanforderungen müssen Produktionssysteme wandlungsfähig sein. Aber jede Veränderung des Systemverhaltens muss anschließend auch erneut abgesichert werden. Zur Absicherung sicherheitskritischer Funktionen eignen sich formale Verifikationsmethoden. Allerdings ist hierzu ein hoher Modellierungsaufwand notwendig, was den Einsatz formaler Verifikationsmethoden in der Praxis hemmt. Zudem empfiehlt sich eine Überprüfung der zur formalen Verifikation eingesetzten Modelle. Zur Bewältigung der Herausforderungen für die Absicherung, die eine Rekonfiguration während des Betriebs nach sich zieht, werden in diesem Beitrag zwei vielversprechende und sich gegenseitig ergänzende Ansätze für die lebenszyklusübergreifende Absicherung von Produktionssystemen vorgestellt.

Real-time monitoring and optimization of production and logistics processes significantly improve the efficiency of production systems. Advanced production management solutions require real-time information about the status of products, production, and resources. As real-time locating systems (also referred to as indoor positioning systems) can enrich the available information, these systems started to gain attention in industrial environments in recent years. This paper provides a review of the possible technologies and applications related to production control and logistics, quality management, safety, and efficiency monitoring. This work also provides a workflow to clarify the steps of a typical real-time locating system project, including the cleaning, pre-processing, and analysis of the data to provide a guideline and reference for research and development of indoor positioning-based manufacturing solutions.

Digital Twins will change how systems and products are engineered and operated. Individual virtual representations of assets help to develop, maintain and change single components or whole factories. Aerospace engineering, product design and intelligent manufacturing are hot spots for the use of Digital Twins. Simultaneously, globalized markets lead to a growing awareness of dependability and quality, which increases the importance of verification and validation. The Digital Twin could prove to be key enabler for efficient verification and validation processes. This paper presents the results of the literature review of approaches that use Digital Twins for verification and validation purposes. Many solutions have been found for a wide range of challenges in various fields of application. This survey discusses the underlying methods and the elements of Digital Twins already in use. Most research approaches focus on simulations and three methodological clusters of approaches sharing similar ideas were identified.

Flexible intralogistics systems use automated guided vehicles (AGV) to transport goods. In assembly and warehouses, AGVs and human workers often work side by side. For optimal navigation, AGVs must consider human movement and estimate future positions of workers. Using real-time locating systems (RTLS) to improve human-robot collaboration enables more energy-efficient and safer AGV wayfinding strategies. This paper gives a summary on the topics RTLS, AGV wayfinding and trajectory prediction and introduces the momentum-based approach to predicting future worker positions in factories and warehouses. The results show that ultra-wideband-based RTLS are very well suited for trajectory prediction in the production sector.

Manufacturing individual products requires individual production. To implement the production of unique products with traditional engineering approaches is thoroughly complex. Therefore, decentralized production systems on the basis of cyber physical systems (CPS) promise a realization schema and thereby, the reduction of the overall systems’ complexity. Cyber physical systems are rather more complex in communication, time requirements and coordination, but are less complex from a modularization point of view and enable new ways of diagnosis and maintenance. The digitalization of production and new concepts of Industry 4.0 enable direct machine to machine communication, exchange of information and making of common decisions. An approach for future manufacturing in which individual products are scheduled incrementally is presented in this paper. In the presented approach, the product invites itself production resources to offer process steps in an auction. The product automatically ranks the offers according to requirements which are predefined by the customer. The best bids are asked to optimize their offers within a negotiation process. To evaluate our approach, a software-agent based demonstrator was implemented.

This paper presents a model for the deviation of distances measured by radar and by optical sensors (3D point clouds). The measured 3D point clouds are typically clustered to objects and the cluster centers are then associated with the radar targets. However, the physical extent and the object geometry cause a divergence of the measured radar range from the cluster centers of the 3D point cloud. Existing solutions either ignore this deviation and compare radar ranges and cluster centers directly without any correction or use object class specific models for the correction. On the contrary, the presented parametric model is independent of the object class. All necessary properties (i.e. object size, flatness and aspect angle) can be obtained online from the covariance matrix of the 3D-point cloud. The model weights are fitted offline using a representative dataset of indoor objects with known radar and cluster correspondences. Furthermore, it is described how this model can be used to localize radar reflections in thermal infrared (TIR) images for a firefighting application. In this particular application, structure-from-motion (SfM) is used to obtain the 3D point cloud from a single moving camera. Experiments with measured data show that the proposed model allows a better data association compared to a direct association.