Andreas Löcklin

Zusammenfassung

The demand for reconfigurations of production systems is increasing, driven by shorter innovation and product life cycles and economic volatility. Another trend in the domain of industrial automation is the emergence of cyber-physical production systems, which offer promising potentials, for example, self-organization capabilities. A suitable cyber-physical production system architecture that incorporates knowledge modelling and management concerns, plus a reconfiguration management methodology, is crucial for realizing self-organized reconfiguration management. In this paper, first reference architectures, architectural patterns, and basic principles, as well as knowledge modelling and management approaches, are discussed in general. Afterwards, these are examined concerning the reconfiguration management use case focusing on UML/XML-based and ontology-based approaches. A novel approach comprising a multi-agent system and the MAPE-K concept for reconfiguration management is presented. In addition, the approach contains a service-oriented architecture for a deterministic plant control within a layered architecture. The knowledge modelling is realized through a UML information model, which can be integrated into the system utilizing XML files. Furthermore, the provided tool support is described. It enables a user to describe system components in an effort-reduced manner and conform to the schema defined by the information model and its restrictions via a GUI.

Zusammenfassung

Indoor positioning systems are an enabling technology for many current developments in the manufacturing field like digital twins and robot fleet management. Utilizing 5G for positioning promises high accuracy, reliability, and costefficiency due to shared hardware usage for communication and positioning. Which positioning technique suits 5G-bases positioning best for manufacturing is still an open research question. This paper presents a deep learning approach for 5Gbased positioning. The first results of our research work in progress obtained at the research factory ARENA 2036 indicate a positioning accuracy in the centimeter range.

Zusammenfassung

In partially automated manufacturing, humans work together with mobile robots. Trajectory prediction, i.e. predicting future positions of human workers, improves collaboration and coexistence between humans and robots on the shop floor. In this paper, we discuss the interrelated research questions of how human motion trajectories can be predicted and how mobile robots such as Autonomous Mobile Robots and Automated Guided Vehicles can take such predictions into account in their pathfinding and navigation. On the robot side, advanced D* pathfinding algorithms allow robots to take dynamic obstacles into account. For trajectory prediction, the position of human workers is determined by an Ultra-Wideband-based Real-Time Locating System. A trajectory prediction framework is introduced to support the implementation and use of pattern- and planning-based trajectory prediction algorithms. The evaluation is based on scenarios from the addressed problem area of manufacturing.

Zusammenfassung

In semi-automated manufacturing, an increasing amount of intelligent mobile robots operate in close proximity to human workers. Considering future positions of humans allows to further improve the efficiency in terms of throughput of Autonomous Mobile Robots (AMR) and Automated Guided Vehicles (AGV). The longer the prediction horizon, i.e. the more position values of humans can be predicted in the near and distant future, the more a robot can adjust its route accordingly and optimize the process. This paper discusses the challenges of human motion trajectory prediction in manufacturing and presents a schedule-based approach that uses real-time schedule data obtained from Manufacturing Execution Systems (MES). Schedule-awareness in human motion trajectory prediction extends semantic mapping approaches and effectively reduces the number of probable destinations by considering which process steps are next for the currently produced goods. With a reduced set of destinations, the performance of forward-planning trajectory prediction can be improved. For evaluation, a commercial MES is used together with an Ultra-wideband-based Real-Time Locating System (RTLS) for obtaining position data of humans. On this basis, a naive Bayes classifier utilizes MES-schedule and real-time position data to predict human motion intentions. Abstract activity modeling ensures that only a few training data sets are required for deployment, thus making this approach suitable for rapidly changing manufacturing environments such as in flexible manufacturing.

Zusammenfassung

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 worst-case 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 (worst) image. This is evaluated with the well-known AlexNet CNN using images depicting a typical driving scenario.

Zusammenfassung

The progress of digitalization and Internet of Things enables more and more complex, networked and powerful Cyber-physical Systems (CPS) operating in uncertain environments. This complexity and uncertainty, however, makes it unfeasible to model every aspect in advance. This causes the models to leave their scope and reach their capability limits. Specifically, in safe maintenance planning for highly-automated trucks, this fact causes waste of valuable resource, since maintenance models are often more rule-of-thumb (e.g. operation hours) than precise. In order to counteract this issue, we propose extending the digital twin concept by artificial intelligence such that the models become dynamic and adaptive. Having described the general approach and its architecture, we showcase and evaluate the approach in a highly-automated truck scenario.

Zusammenfassung

At collaborative workspaces, humans and robots share the shop floor and work closely together. Operator 4.0 is a wide research topic and its solutions aim at the creation of Human-centered Cyber-Physical Systems that improve operators’ capabilities. Such applications require a bi directional flow of information and need data, models and simulations of machines as well as humans. To realize a common interface for information, the concept of Digital Twin is promising. This paper therefore discusses the adaption of conventional Digital Twin architectures and presents a derived Human-centered Digital Twin (H-DT) architecture designed for operators in production and intralogistics.

Zusammenfassung

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.

Zusammenfassung

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.

Zusammenfassung

Die Flexibilisierung von Produktionsanlagen ist notwendig, um in einem volatilen Marktumfeld erfolgreich sein zu können. Durch die Bereitstellung und Pflege geeigneter Schnittstellen und Betriebsmodi, sowie dem Umbau von Anlagen, kann auf geänderte Anforderungen an die Produktion reagiert werden. Für die Realisierung derartiger hochflexibler und rekonfigurierbarer Anlagen ist ein erhöhter Engineering- und Testaufwand notwendig. Jede zusätzliche Funktion samt aller damit verbundener Abhängigkeiten müssen vor Inbetriebnahme mehrfach von Herstellern, Integratoren und Betreibern überprüft werden. Damit der resultierende erhöhte Testaufwand auch zukünftig handhabbar bleibt, werden in dieser Veröffentlichung folgende Ansätze vorgestellt, um ein effizientes und wirkungsvolles Testen im Bereich der flexiblen Produktion zu ermöglichen: Zum einen lässt sich durch eine auf Richtlinien basierende Modularisierung von Anlagen und Komponenten der Testaufwand reduzieren. Auch sorgen Agile Teams für eine verbesserte Kommunikation zwischen Entwicklungs- und Testabteilungen. Überdies ermöglicht die Anwendung von Digitalen Zwillingen einen effektiveren Informationsaustausch zwischen den Stakeholdern und Modellbasiertes Testen ermöglicht Vorteile bei der Testautomatisierung. Alle Ansätze sind jeweils eigenständig wirksam, aber zusammengenommen ergeben sich zusätzliche Synergieeffekte.

Zusammenfassung

Reconfiguration demand is increasing due to frequent requirement changes for manufacturing systems. Recent approaches aim at investigating feasible configuration alternatives from which they select the optimal one. This relies on processes whose behavior is not reliant on e.g. the production sequence. However, when machine learning is used, components’ behavior depends on the process’ specifics, requiring additional concepts to successfully conduct reconfiguration management. Therefore, we propose the enhancement of the comprehensive reconfiguration management with transfer learning. This provides the ability to assess the machine learning dependent behavior of the different CPPS configurations with reduced effort and further assists the recommissioning of the chosen one. A real cyber-physical production system from the discrete manufacturing domain is utilized to demonstrate the aforementioned proposal.

Zusammenfassung

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.

Zusammenfassung

Due to the increasing agility in the development process, shorter life cycles, and changing customer and legislator requirements, production systems must be adaptable. In addition to the ability to react flexibly to changing production requirements, the behavior of the production system must be reassured after a change. Formal verification methods are suitable for safeguarding safety-critical functions. However, this requires a high modeling effort, which inhibits the use of formal verification methods in practice. For reliable verification results, the quality and correctness of the models is decisive. Therefore, it is reasonable to analyze the underlying models. This paper presents two promising and mutually complementing approaches for the lifecycle-spanning safeguarding of production systems to overcome the challenges for the verification that a reconfiguration of the production systems brings with it during operation.

Zusammenfassung

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.

Zusammenfassung

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.