Please use this identifier to cite or link to this item: https://utcc-dspacecris.eval.plus/handle/6626976254/533
Title: A Simulation of 6R Industrial Articulated Robot Arm Using Backpropagation Neural Network
Authors: Manigpan, Supachoke 
Kiattisin, Supaporn 
Leelasantitham, Adisorn 
Issue Date: 2010
Publisher: University of the Thai Chamber of Commerce
University of the Thai Chamber of Commerce
Source: Supachoke Manigpan, Supaporn Kiattisin, Adisorn Leelasantitham (2010) A Simulation of 6R Industrial Articulated Robot Arm Using Backpropagation Neural Network. UTCC Engineering Research Papers.
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.
URI: https://scholar.utcc.ac.th/handle/6626976254/533
ISSN: 1906-1625
Rights: This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
Appears in Collections:EN: Journal Articles

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