Optimization Model of Fast and Untrapped Neural Based Inverse Kinematic: Implementation on Multiple-Links Planar Robot

Azhar Aulia Saputra; Jinseok Woo; Naoyuki Kubota

doi:10.1109/SMC.2018.00153

Optimization Model of Fast and Untrapped Neural Based Inverse Kinematic: Implementation on Multiple-Links Planar Robot

Azhar Aulia Saputra, Jinseok Woo, Naoyuki Kubota

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

Abstract

In order to solve the overlap link constraint, trapped movement, and computational cost problem in current IK model, this paper proposes a new coupled spiking neural network (CSNN) model which is combined with artificial neural network (ANN). Several references of end of effector's movement will be generated as training model of ANN. Current joint positions and angle values, movement direction and distance will be the input data. Angular velocity of every joint will be the output data. However, ANN structure and number of references will be minimized. As an alternative, CSNN will be implemented, where one joint angle is represented by a coupled neurons interconnected to each others. CSNN has feedback input from the current condition of arm robot, and its output will be combined with ANN's output. CSNN interconnection will be optimized using steady state evolutionary algorithm with several epoch. The proposed model is implemented to simulate multiple link planar robot. The result shows the effectiveness of the proposed model which succeeded in several trajectory tests with minimum computational cost.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	856-861
Number of pages	6
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00153
State	Published - 2 Jul 2018
Externally published	Yes
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: 7 Oct 2018 → 10 Oct 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/Territory	Japan
City	Miyazaki
Period	7/10/18 → 10/10/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Artificial Neural Network
Coupled Spiking Neural Network
Inverse Kinematics

ASJC Scopus subject areas

Information Systems
Information Systems and Management
Health Informatics
Artificial Intelligence
Computer Networks and Communications
Human-Computer Interaction

Access to Document

10.1109/SMC.2018.00153

Cite this

Saputra, A. A., Woo, J., & Kubota, N. (2018). Optimization Model of Fast and Untrapped Neural Based Inverse Kinematic: Implementation on Multiple-Links Planar Robot. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 856-861). Article 8616149 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00153

Saputra, Azhar Aulia ; Woo, Jinseok ; Kubota, Naoyuki. / Optimization Model of Fast and Untrapped Neural Based Inverse Kinematic : Implementation on Multiple-Links Planar Robot. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 856-861 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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title = "Optimization Model of Fast and Untrapped Neural Based Inverse Kinematic: Implementation on Multiple-Links Planar Robot",

abstract = "In order to solve the overlap link constraint, trapped movement, and computational cost problem in current IK model, this paper proposes a new coupled spiking neural network (CSNN) model which is combined with artificial neural network (ANN). Several references of end of effector's movement will be generated as training model of ANN. Current joint positions and angle values, movement direction and distance will be the input data. Angular velocity of every joint will be the output data. However, ANN structure and number of references will be minimized. As an alternative, CSNN will be implemented, where one joint angle is represented by a coupled neurons interconnected to each others. CSNN has feedback input from the current condition of arm robot, and its output will be combined with ANN's output. CSNN interconnection will be optimized using steady state evolutionary algorithm with several epoch. The proposed model is implemented to simulate multiple link planar robot. The result shows the effectiveness of the proposed model which succeeded in several trajectory tests with minimum computational cost.",

keywords = "Artificial Neural Network, Coupled Spiking Neural Network, Inverse Kinematics",

author = "Saputra, \{Azhar Aulia\} and Jinseok Woo and Naoyuki Kubota",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/SMC.2018.00153",

language = "English",

series = "Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018",

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Saputra, AA, Woo, J & Kubota, N 2018, Optimization Model of Fast and Untrapped Neural Based Inverse Kinematic: Implementation on Multiple-Links Planar Robot. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616149, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 856-861, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 7/10/18. https://doi.org/10.1109/SMC.2018.00153

Optimization Model of Fast and Untrapped Neural Based Inverse Kinematic: Implementation on Multiple-Links Planar Robot. / Saputra, Azhar Aulia; Woo, Jinseok; Kubota, Naoyuki.
Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 856-861 8616149 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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

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T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

AU - Saputra, Azhar Aulia

AU - Woo, Jinseok

AU - Kubota, Naoyuki

PY - 2018/7/2

Y1 - 2018/7/2

N2 - In order to solve the overlap link constraint, trapped movement, and computational cost problem in current IK model, this paper proposes a new coupled spiking neural network (CSNN) model which is combined with artificial neural network (ANN). Several references of end of effector's movement will be generated as training model of ANN. Current joint positions and angle values, movement direction and distance will be the input data. Angular velocity of every joint will be the output data. However, ANN structure and number of references will be minimized. As an alternative, CSNN will be implemented, where one joint angle is represented by a coupled neurons interconnected to each others. CSNN has feedback input from the current condition of arm robot, and its output will be combined with ANN's output. CSNN interconnection will be optimized using steady state evolutionary algorithm with several epoch. The proposed model is implemented to simulate multiple link planar robot. The result shows the effectiveness of the proposed model which succeeded in several trajectory tests with minimum computational cost.

AB - In order to solve the overlap link constraint, trapped movement, and computational cost problem in current IK model, this paper proposes a new coupled spiking neural network (CSNN) model which is combined with artificial neural network (ANN). Several references of end of effector's movement will be generated as training model of ANN. Current joint positions and angle values, movement direction and distance will be the input data. Angular velocity of every joint will be the output data. However, ANN structure and number of references will be minimized. As an alternative, CSNN will be implemented, where one joint angle is represented by a coupled neurons interconnected to each others. CSNN has feedback input from the current condition of arm robot, and its output will be combined with ANN's output. CSNN interconnection will be optimized using steady state evolutionary algorithm with several epoch. The proposed model is implemented to simulate multiple link planar robot. The result shows the effectiveness of the proposed model which succeeded in several trajectory tests with minimum computational cost.

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M3 - Conference contribution

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BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Saputra AA, Woo J, Kubota N. Optimization Model of Fast and Untrapped Neural Based Inverse Kinematic: Implementation on Multiple-Links Planar Robot. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 856-861. 8616149. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). doi: 10.1109/SMC.2018.00153

Optimization Model of Fast and Untrapped Neural Based Inverse Kinematic: Implementation on Multiple-Links Planar Robot

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this