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

Content Provider	IEEE Xplore Digital Library
Author	Hollmann, R. Rost, A. Hagele, M. Verl, A.
Copyright Year	2010
Description	Author affiliation: Fraunhofer Institute Manufacturing Engineering and Automation,70569 Stuttgart, Germany (Hollmann, R.; Rost, A.; Hagele, M.; Verl, A.)
Abstract	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.
Starting Page	2965
Ending Page	2970
File Size	584540
Page Count	6
File Format	PDF
ISBN	9781424450381
ISSN	10504729
DOI	10.1109/ROBOT.2010.5509888
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Robot programming Service robots Robotics and automation Humans Manufacturing industries Welding Production USA Councils Automatic programming
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software

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