When drones, autonomous forklifts, and stationary grippers work together in logistics or production, the collaboration usually ends in incompatible software architectures. Classic interfaces fail to connect heterogeneous systems flexibly. Large AI models for language, image processing, and logic fill this gap: they enable machines to interpret work orders in the context of the respective business environment. However, developers still need to resolve fundamental questions regarding functional reliability before the system can be put into production.<\/p>\n
The main points in summary:<\/strong><\/p>\n When a drone explores a factory site while a driverless transport system simultaneously delivers components, the direct exchange often fails due to the data format. The working group of the platform Lernende Systeme points out that different IT architectures regularly hinder collaboration in industrial practice.<\/p>\n True interoperability requires four steps:<\/p>\n While the technical and syntactic foundations are manageable today, rigid regulations reach their limits at the semantic and pragmatic level.<\/p>\n Modern AI architectures address this gap in understanding. A white paper from the platform Lernende Systeme (Learning Systems) illustrates how generative systems can control heterogeneous machine fleets. J\u00fcrgen Beyerer from Fraunhofer IOSB illustrates the principle: Since the models bring enormous amounts of world knowledge, they dynamically adapt sensor and order data during operation. They act like a superior planner in the background, analyzing changing situations and distributing work orders appropriately to the available robots.<\/p>\n The transition from the test lab to the production hall requires strict proofing. The current basic models often still lack the specific process and domain knowledge of industrial plants. If a control system misinterprets physical limits or incorrectly interprets measurement values, the chain breaks down \u2013 with direct dangers for people and material.<\/p>\n Pure language or image data are not sufficient for security proofs. What is required are multimodal systems that countermand commands before execution in virtual environments and physically verify them. Johannes Hinckeldeyn from the KION Group sees the responsibility primarily in collaboration between science, manufacturers, and industrial users: Only when research teams and plant practitioners feed real operational data into the simulation models can robust industrial products be created from theoretical approaches.<\/p>","protected":false},"excerpt":{"rendered":" Heterogene Systeme: Wie KI die Verst\u00e4ndnisl\u00fccke schlie\u00dft Wenn Drohnen, autonome Flurf\u00f6rderzeuge und station\u00e4re Greifarme in Logistik oder Produktion zusammenarbeiten, endet die Kooperation meist an inkompatiblen Softwarearchitekturen. Klassische Schnittstellen scheitern daran, heterogene Systeme flexibel miteinander zu vernetzen. Gro\u00dfe KI-Modelle f\u00fcr Sprache, Bildverarbeitung und Logik schlie\u00dfen diese L\u00fccke: Sie versetzen Maschinen in die Lage, Arbeitsauftr\u00e4ge im jeweiligen […]<\/p>\n","protected":false},"author":8,"featured_media":27322,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[88],"tags":[],"referenzkategorie":[],"class_list":["post-27321","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-supply-chain-news"],"acf":[],"yoast_head":"\n\n
Why classic interfaces encounter limitations<\/h3>\n
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AI as translator and coordinator in the machine park<\/h3>\n
Functional safety: Barriers before field deployment<\/h3>\n