A skill-based approach towards hybrid assembly

  • Authors:

  • Frank Wallhoff;Jürgen Blume;Alexander Bannat;Wolfgang Rösel;Claus Lenz;Alois Knoll

  • Affiliations:

  • Human-Machine Communication, Technische Universität München, Theresienstraíe 90, 80333 München, Germany;Human-Machine Communication, Technische Universität München, Theresienstraíe 90, 80333 München, Germany;Human-Machine Communication, Technische Universität München, Theresienstraíe 90, 80333 München, Germany;Institute for Machine Tools and Industrial Management, Technische Universität München, Boltzmannstraíe 15, 85748 Garching, Germany;Robotics and Embedded Systems, Technische Universität München, Boltzmannstraíe 3, 85748 Garching, Germany;Robotics and Embedded Systems, Technische Universität München, Boltzmannstraíe 3, 85748 Garching, Germany

  • Venue:
  • Advanced Engineering Informatics
  • Year:
  • 2010

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Abstract

Efficient cooperation of humans and industrial robots is based on a common understanding of the task as well as the perception and understanding of the partner's action in the next step. In this article, a hybrid assembly station is presented, in which an industrial robot can learn new tasks from worker instructions. The learned task is performed by both the robot and the human worker together in a shared workspace. This workspace is monitored using multi-sensory perception for detecting persons as well as objects. The environmental data are processed within the collision avoidance module to provide safety for persons and equipment. The real-time capable software architecture and the orchestration of the involved modules using a knowledge-based system controller is presented. Finally, the functionality is demonstrated within an experimental cell in a real-world production scenario.