Das volle Potenzial von Industrie 4.0 (I4.0) zu entfalten, erfordert eine nahtlose Synergie zwischen hochmodernen speicherprogrammierbaren Steuerungen (PLCs) und der transformierenden Datenstrukturierung der I4.0-Digitalzwillinge in ihrer interoperablen Form der Asset Administration Shell (AAS). Diese Integration ist nicht nur ein technologischer Fortschritt, sondern ein entscheidender Schritt hin zu einem effizienteren, reaktionsfähigeren und intelligenteren industriellen Ökosystem.

In diesem Beitrag tauchen wir tief in die Feinheiten der Softwareentwicklung innerhalb moderner PLC-Umgebungen ein, die speziell für I4.0-Digitalzwillinge konzipiert sind. Unsere qualitative Analyse konzentriert sich darauf, wie AAS-Instanzen als digitale Zwillinge in PLCs effektiv implementiert werden können, wobei wir wesentliche Aspekte der Softwaretechnik wie Leistung, Entwicklungsaufwand, Korrektheit, Wartbarkeit und Weiterentwicklungsfähigkeit berücksichtigen.

Wir untersuchen auch den aktuellen Stand der praktischen Lösungen zur Implementierung einer AAS in spezifischen PLC-Entwicklungsumgebungen. Unsere Ergebnisse bieten wertvolle Einblicke und praktische Lösungswege, die das volle Potenzial von I4.0 durch die Integration von digitalen Zwillingen und PLCs auszuschöpfen.

Die Kombination von fortschrittlicher PLC-Programmierung und dem robusten Rahmenwerk der digitalen Zwillinge verbessert nicht nur die Betriebseffizienz, sondern fördert auch Innovation und Anpassungsfähigkeit in der sich ständig weiterentwickelnden industriellen Landschaft.

Mobile robots are becoming indispensable assets in modern manufacturing environments. With advancements in sensor technologies such as LiDAR, odometry, and vision, these robots are increasingly capable of perceiving and navigating through complex, dynamic spaces. A core requirement for autonomous operation is the ability to estimate their position while simultaneously mapping the environment commonly known as Simultaneous Localization and Mapping (SLAM).

Smart factories, with their movable machinery, varying light conditions, and high operational complexity, present a fitting use case for advanced SLAM systems. However, these conditions often challenge traditional SLAM techniques, leading to degraded map quality and unreliable navigation. Our recent study on the comparative performance of LiDAR-based SLAM in the SmartFactoryOWL environment revealed that relying solely on LiDAR data, such as when using GMapping on the Clearpath Jackal, results in incomplete or distorted maps.

Driven by recent advancements in Artificial Intelligence, Machine Learning, and Deep Learning, several approaches are now emerging to optimize SLAM pipelines. These include reinforcement learning-based policy training, sensor fusion strategies, and vision-augmented mapping techniques. Together, they hold significant potential to improve SLAM robustness, efficiency, and scalability in future-ready smart manufacturing environments.

Data spaces are emerging as a key paradigm for sovereign, secure, and decentralized data sharing, particularly in industrial contexts. While foundational technologies like the Eclipse Data Connector (EDC) provide a common protocol, the landscape is fragmenting into large, domain-specific ecosystems such as Catena-X, which, to ensure security and trust, have developed highly structured frameworks. This necessary standardization, however, presents challenges for interoperability with external systems. This paper addresses the critical challenge of interoperability between these disparate data space implementations. We present a practical analysis based on experiments from the ROBOT-X project, detailing efforts to connect a bespoke EDC instance with both another custom connector and the highly-structured Tractus-X ecosystem. The findings highlight that protocol alignment is insufficient; true interoperability hinges on solving challenges in identity management, credential verification, and governance. We propose a methodical, tool-driven approach using resources like the Tractus-X SDK and its compatibility test suite to bridge these gaps, providing a tangible roadmap for achieving a federated "network of data spaces."