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Teachless teach-repeat: Toward Vision-Based Programming of Industrial Robots

Abstract : Modern programming of industrial robots is often based on the teach-repeat paradigm: a human operator places the robot in many key positions, for teaching its task. Then the robot can repeat a path defined by these key positions. This paper proposes a vision-based approach for the automation of the teach stage. The approach relies on a constant autocalibration of the system. Therefore, the only requirement is a precise geometrical description of the part to process. The realism of the approach is demonstrated through the emulation of a glue application process with an industrial robot. Results in terms of precision are very promising.
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Submitted on : Wednesday, May 16, 2012 - 7:00:08 AM
Last modification on : Thursday, October 21, 2021 - 3:45:20 AM
Long-term archiving on: : Wednesday, December 14, 2016 - 6:37:17 AM


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  • HAL Id : hal-00671209, version 1



Mathias Perrollaz, Sami Khorbotly, Amber Cool, John-David Yoder, Eric Baumgartner. Teachless teach-repeat: Toward Vision-Based Programming of Industrial Robots. IEEE International Conference on Robotics and Automation, May 2012, St Paul, Minnesota, United States. ⟨hal-00671209⟩



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