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Learning Task Strategies in Robotic Assembly Systems

Published online by Cambridge University Press:  09 March 2009

D. S. Ahn ,
H. S. Cho ,
K. Ide ,
F. Miyazaki  and
S. Arimoto
D. S. Ahn
Affiliation:
Korea Advanced Institute of Science & Technology, P.O. Box 150, Chongryangri, Seoul (Korea)
H. S. Cho
Affiliation:
Korea Advanced Institute of Science & Technology, P.O. Box 150, Chongryangri, Seoul (Korea)
K. Ide
Affiliation:
Faculty of Engineering Science, Osaka University, Osaka (Japan)
F. Miyazaki
Affiliation:
Faculty of Engineering Science, Osaka University, Osaka (Japan)
S. Arimoto
Affiliation:
Faculty of Engineering, University of Tokyo, Tokyo (Japan)
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Summary

This paper presents a practical method for generating task strategies applicable to chamferless and high-precision assembly. The difficulties in devising reliable assembly strategies result from various forms of uncertainty such as imperfect knowledge on the parts being assembled and functional limitations of the assembly devices.

In order to cope with these problems, the robot is provided with the capability of learning the corrective motion in response to the force signal through iterative task execution. The strategy is realized by adopting a learning algorithm and is represented in a binary tree-type database. To verify the effectiveness of the proposed algorithm, a series of experiments are carried out under simulated real production conditions. The experimental results show that sensory signal-to-robot action mapping can be acquired effectively and, consequently, the assembly task can be performed successfully.

Keywords

Chamferless assemblyTask strategyLearningMapping relation
Type
Article
Information
Robotica , Volume 10 , Issue 5 , September 1992 , pp. 409 - 418
Copyright
Copyright © Cambridge University Press 1992

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