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A HMM-based approach to learning probability models of programming strategies for industrial robots

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4 Author(s)
Hollmann, R. ; Fraunhofer Inst. Manuf. Eng. & Autom., Stuttgart, Germany ; Rost, A. ; Hägele, M. ; Verl, A.

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 small-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.

Published in:

Robotics and Automation (ICRA), 2010 IEEE International Conference on

Date of Conference:

3-7 May 2010