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Skill-based programming of complex robotic assembly tasks for industrial application

Skill-basierte Programmierung von komplexen Roboter-Montageaufgaben für die industrielle Applikation

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Abstract

In recent years, a paradigm shift is underway as robots leave their typical application field and move into domains that have been untouched by robotic automation. These new kinds of automation systems allow more product variations, smaller life cycles, smaller batch sizes and pave the way from mass production to mass customization. This is due to completely new breed of safe robot technology but also novel ways of setting up new applications like e.g. kinesthetic programming. However, the topic of reducing the programming effort for complex tasks using natural modes of communication is still open. This paper addresses the key developments in this field, shows different ways of programming, and gives relevant use cases in industrial assembly. The technology coverage starts with an online workflow editor called XROB that allows easy-to-use setup of process workflows and related skill parameters. However, in order to reduce the programming effort, a novel way to demonstrate process trajectories by using instrumented hand guided process tools is presented. Finally, the paper gives an overview of a promising approach that allows programming without touching the robot just by demonstrating the process by an expert. The semantic relations between activities executed by the human and robot skills are captured to learn the task sequence of the assembly process. The acquired process knowledge is refined to execute robotic tasks with the help of an interactive graphical user interface (GUI). The system queries the user for feedback, asking for specific information to help the robot complete the task at hand. The given examples show the usability of flexible programming tools in the automation chain and the presented results provide strong evidence of the technological potential in the field.

Zusammenfassung

Die produzierende Industrie erfährt einen Paradigmenwechsel, da Roboter ihr typisches Anwendungsfeld verlassen und in Bereiche vordringen, die von der robotergestützten Automatisierung bisher unberührt geblieben sind. Diese neuartigen Automatisierungssysteme ermöglichen mehr Produktvariationen, kleinere Lebenszyklen und kleinere Losgrößen und ebnen den Weg von der Massenproduktion zur Produktindividualität. Dies ist auf eine völlig neue Art sicherer Robotertechnologie zurückzuführen, aber auch auf neue Wege zur Realisierung neuer Anwendungen wie z.B. der kinästhetischen Programmierung. Ein weiterhin offenes Thema ist den Programmieraufwand für komplexe Aufgaben mit natürlichen Kommunikationswegen zu reduzieren. Diese Publikation befasst sich mit den wichtigsten Entwicklungen in diesem Bereich, zeigt verschiedene Möglichkeiten der Programmierung auf und beschreibt relevante Anwendungsfälle in der industriellen Montage. Das betrachtete Technologieportfolio beinhaltet einen Online-Workflow-Editor namens XRob, welcher die einfache Einrichtung von Prozessabläufen und damit verbundenen Qualifikationsparametern ermöglicht. Um den Programmieraufwand zu reduzieren, wird eine neuartige Möglichkeit zur Demonstration von Prozessverläufen, unter Verwendung von instrumentierten, handgeführten Prozesswerkzeugen, vorgestellt. Schließlich gibt das Paper einen Überblick über einen vielversprechenden Ansatz, der die Programmierung ohne Berührung des Roboters ermöglicht, nur durch die Demonstration des Prozesses durch einen Experten. Die semantische Beziehung zwischen den vom Menschen ausgeführten Tätigkeiten wird erfasst, um die Aufgabenreihenfolge des Montageprozesses zu erlernen. Das erworbene Prozesswissen wird mit Hilfe der interaktiven grafischen Benutzeroberfläche (GUI) zu Roboteraufgaben verfeinert. Das System fragt den Benutzer nach Feedback und fragt nach spezifischen Informationen, die dem Roboter helfen, die anstehende Aufgabe zu erfüllen. Die angeführten Beispiele belegen die Benutzerfreundlichkeit flexibler Programmierwerkzeuge in der Automatisierungskette, und die vorgestellten Ergebnisse zeigen das technologische Potenzial in diesem Bereich auf.

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Notes

  1. Vive.com. (2019). VIVE™ – VIVE Tracker. [online] Available at: https://www.vive.com/eu/vive-tracker [Accessed 10 Aug. 2019].

  2. Ati-ia.com. (2019). ATI Industrial Automation: F/T Sensor Delta. [online] Available at: https://www.ati-ia.com [Accessed 10 Aug. 2019].

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Acknowledgements

This research is funded by the projects ASKRob, LERN4MRK_II (Austrian Institute of Technology), MMAssist_II (FFG, 858623), Smart Factory Lab and DigiManu (funded by the State of Upper Austria).

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  1. Profactor GmbH, Im Stadtgut A2, Steyr-Gleink, 4407, Austria

    Sharath Chandra Akkaladevi, Andreas Pichler, Matthias Plasch, Markus Ikeda & Michael Hofmann

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  1. Sharath Chandra Akkaladevi
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  2. Andreas Pichler
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  3. Matthias Plasch
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  5. Michael Hofmann
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Correspondence to Sharath Chandra Akkaladevi.

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Akkaladevi, S.C., Pichler, A., Plasch, M. et al. Skill-based programming of complex robotic assembly tasks for industrial application. Elektrotech. Inftech. 136, 326–333 (2019). https://doi.org/10.1007/s00502-019-00741-4

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