Cooperative fuzzy hint acquisition for industrial redundant robots to avoid the joint limits

Samy F.M. Assal; Keigo Watanabe; Kiyotaka Izumi

Cooperative fuzzy hint acquisition for industrial redundant robots to avoid the joint limits

Samy F.M. Assal, Keigo Watanabe, Kiyotaka Izumi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A novel neural network (NN) based inverse kinematics solution of redundant manipulators is proposed to solve the joint limits problem. An adaptive learning algorithm for that NN is derived based on Lyapunov approach. Since the inverse kinematics has infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self-motion. This FNN is designed based on cooperatively controlling each joint angle of the manipulator. Experiments are implemented for the PA-IO redundant manipulator and a comparative study is made with the gradient projection method.

Original language	English
Title of host publication	Soft Computing as Transdisciplinary Science and Technology - Proceedings of the 4th IEEE International Workshop, WSTST 2005
Pages	41-50
Number of pages	10
Edition	AISC
Publication status	Published - Dec 1 2005
Externally published	Yes
Event	4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology, WSTST 2005 - Muroran, Japan Duration: May 25 2005 → May 27 2005

Publication series

Name	Advances in Soft Computing
Number	AISC
ISSN (Print)	1615-3871
ISSN (Electronic)	1860-0794

Other

Other	4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology, WSTST 2005
Country/Territory	Japan
City	Muroran
Period	5/25/05 → 5/27/05

ASJC Scopus subject areas

Computer Science (miscellaneous)
Computational Mechanics
Computer Science Applications

Cite this

Cooperative fuzzy hint acquisition for industrial redundant robots to avoid the joint limits. / Assal, Samy F.M.; Watanabe, Keigo; Izumi, Kiyotaka.
Soft Computing as Transdisciplinary Science and Technology - Proceedings of the 4th IEEE International Workshop, WSTST 2005. AISC. ed. 2005. p. 41-50 (Advances in Soft Computing; No. AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Assal, SFM, Watanabe, K & Izumi, K 2005, Cooperative fuzzy hint acquisition for industrial redundant robots to avoid the joint limits. in Soft Computing as Transdisciplinary Science and Technology - Proceedings of the 4th IEEE International Workshop, WSTST 2005. AISC edn, Advances in Soft Computing, no. AISC, pp. 41-50, 4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology, WSTST 2005, Muroran, Japan, 5/25/05.

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abstract = "A novel neural network (NN) based inverse kinematics solution of redundant manipulators is proposed to solve the joint limits problem. An adaptive learning algorithm for that NN is derived based on Lyapunov approach. Since the inverse kinematics has infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self-motion. This FNN is designed based on cooperatively controlling each joint angle of the manipulator. Experiments are implemented for the PA-IO redundant manipulator and a comparative study is made with the gradient projection method.",

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N2 - A novel neural network (NN) based inverse kinematics solution of redundant manipulators is proposed to solve the joint limits problem. An adaptive learning algorithm for that NN is derived based on Lyapunov approach. Since the inverse kinematics has infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self-motion. This FNN is designed based on cooperatively controlling each joint angle of the manipulator. Experiments are implemented for the PA-IO redundant manipulator and a comparative study is made with the gradient projection method.

AB - A novel neural network (NN) based inverse kinematics solution of redundant manipulators is proposed to solve the joint limits problem. An adaptive learning algorithm for that NN is derived based on Lyapunov approach. Since the inverse kinematics has infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self-motion. This FNN is designed based on cooperatively controlling each joint angle of the manipulator. Experiments are implemented for the PA-IO redundant manipulator and a comparative study is made with the gradient projection method.

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Cooperative fuzzy hint acquisition for industrial redundant robots to avoid the joint limits

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