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

Mathias Perrollaz 1 Sami Khorbotly 2 Amber Cool 2 John-David Yoder 2 Eric Baumgartner 2
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
2 Ohio Northern University
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.
Type de document :
Communication dans un congrès
IEEE. IEEE International Conference on Robotics and Automation, May 2012, St Paul, Minnesota, United States. 2012
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Mathias Perrollaz, Sami Khorbotly, Amber Cool, John-David Yoder, Eric Baumgartner. Teachless teach-repeat: Toward Vision-Based Programming of Industrial Robots. IEEE. IEEE International Conference on Robotics and Automation, May 2012, St Paul, Minnesota, United States. 2012. 〈hal-00671209〉

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