The integration of industrial robot systems into the manufacturing environments of small and medium sized enterprises is a key requirement to guarantee competitiveness and productivity. Due to the still complex and time-consuming procedure of robot path definition, novel programming strategies are needed, converting the robotic system into a flexible coworker that actively supports its operator. In this paper, a learning-from-demonstration strategy based on Hidden Markov Models is presented, which permits the robot system to adapt to user- as well as process-specific features. To evaluate the suitability of this approach for smalI-lot production, the learning strategy has been implemented for an arc welding robot and has been evaluated on-site at a medium sized metal-working company
This paper reviews the state of the art of models in robotic manufacturing applications. Moreover, a model architecture is proposed that is adequate for capturing knowledge of manufacturing processes in small and medium sized enterprises. The proposed architecture improves existing approaches by introducing a technology model that allows modeling of specific knowledge of manufacturing processes. This enables automatic generation of robot programs and supports reusability of manufacturing knowledge in industrial production scenarios. The usage of the presented model architecture is shown for a robotic welding application
ZusammenfassungAssistenzsysteme, seien sie mobil oder stationär, unterstützen den Werker im Produktionsprozess, beispielsweise bei Schweißarbeiten in kleinen Losgrößen. Für die Programmierung dieser Prozesse existiert keine etablierte Lösung, da die Vorgehensweisen der Industrierobotik nicht übertragbar sind. Das Konzept des Programmierens durch Vormachen ist ein Lösungsansatz, der mit der Programmierumgebung InTeach verfolgt wird. Die Komponenten des Systems und die damit mögliche schnelle und intuitive Programmierung werden am Beispiel von zwei Assistenzsystemen vorgestellt.
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