Abstract
This paper presents a general recurrent neural network (RNN) model for online control of time-varying robot manipulators. The robot manipulators with different setting parameters work cooperatively on an unknown curve tracing. Each joint of the manipulator is respectively provided a learning method to optimize trajectory by the training RNN model. In this paper, the proposed RNN model shortens the period of learning and improves the cooperative accuracy of the existing neural networks for solving problems such as cutting or welding special types of products. A More complicated construction is to fit it for online cooperation. Simulation results show the effectiveness of this approach and that the proposed RNN model can successfully learn the inverse dynamics of robot manipulators as well as perform accurate tracking for a general trajectory. It is also shown that the proposed method is better than the conventional method due to its improved evolution functions.
| Original language | English |
|---|---|
| Pages (from-to) | 937-952 |
| Number of pages | 16 |
| Journal | International Journal of Innovative Computing, Information and Control |
| Volume | 3 |
| Issue number | 4 |
| Publication status | Published - Aug 2007 |
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Keywords
- Collision avoidance
- Cooperative motion control
- Kobot arm
- Recurrent neural network (KNN)
- Trajectory generation
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Information Systems
- Software
- Theoretical Computer Science
Cite this
RNN-Based cooperative motion control of 2-dof Robot Arms. / Dai, Yingda; Konishi, Masami; Imai, Jun.
In: International Journal of Innovative Computing, Information and Control, Vol. 3, No. 4, 08.2007, p. 937-952.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - RNN-Based cooperative motion control of 2-dof Robot Arms
AU - Dai, Yingda
AU - Konishi, Masami
AU - Imai, Jun
PY - 2007/8
Y1 - 2007/8
N2 - This paper presents a general recurrent neural network (RNN) model for online control of time-varying robot manipulators. The robot manipulators with different setting parameters work cooperatively on an unknown curve tracing. Each joint of the manipulator is respectively provided a learning method to optimize trajectory by the training RNN model. In this paper, the proposed RNN model shortens the period of learning and improves the cooperative accuracy of the existing neural networks for solving problems such as cutting or welding special types of products. A More complicated construction is to fit it for online cooperation. Simulation results show the effectiveness of this approach and that the proposed RNN model can successfully learn the inverse dynamics of robot manipulators as well as perform accurate tracking for a general trajectory. It is also shown that the proposed method is better than the conventional method due to its improved evolution functions.
AB - This paper presents a general recurrent neural network (RNN) model for online control of time-varying robot manipulators. The robot manipulators with different setting parameters work cooperatively on an unknown curve tracing. Each joint of the manipulator is respectively provided a learning method to optimize trajectory by the training RNN model. In this paper, the proposed RNN model shortens the period of learning and improves the cooperative accuracy of the existing neural networks for solving problems such as cutting or welding special types of products. A More complicated construction is to fit it for online cooperation. Simulation results show the effectiveness of this approach and that the proposed RNN model can successfully learn the inverse dynamics of robot manipulators as well as perform accurate tracking for a general trajectory. It is also shown that the proposed method is better than the conventional method due to its improved evolution functions.
KW - Collision avoidance
KW - Cooperative motion control
KW - Kobot arm
KW - Recurrent neural network (KNN)
KW - Trajectory generation
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UR - http://www.scopus.com/inward/citedby.url?scp=52449093155&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:52449093155
VL - 3
SP - 937
EP - 952
JO - International Journal of Innovative Computing, Information and Control
JF - International Journal of Innovative Computing, Information and Control
SN - 1349-4198
IS - 4
ER -