A Simulation of 6R Industrial Articulated Robot Using Neural Network

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University of the Thai Chamber of Commerce. School of Engineering
Publisher(s)
University of the Thai Chamber of Commerce
Date Issued
2010
ISSN
1906-1625
Resource Type
Text::Journal::Journal article
Language
English
Abstract
This paper presents a simulation of a 6 degrees-of-freedom (6R) articulated robot arm using backpropagation neural network to solve the problem regarding inverse kinematics for the industrial articulated robot. The Denavit – Hartenberg model is used to analyze the robot arm movement. Next, the forward kinematics is used to identify the relationships for each joint of the robot arm and to determine various parameters for learning system of random neural network for 5,000 data points. The simulation results show that the robot arm can move to target positions with precision, and the average error for the entire 6 joints is at approximately 4.03 degrees.
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Journal
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Open access
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This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
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University of the Thai Chamber of Commerce
Bibliographic Citation
Supachoke Manigpan, Supaporn Kiattisin, Adisorn Leelasantitham (2010) A Simulation of 6R Industrial Articulated Robot Using Neural Network. UTCC Engineering Research Papers.
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