Please use this identifier to cite or link to this item:
DC FieldValueLanguage
dc.contributor.authorManigpan, Supachoke
dc.contributor.otherUniversity of the Thai Chamber of Commerce. Graduate School
dc.identifier.citationSupachoke Manigpan (2010) A Simulation of 6R Industrial Articulated Robot Arm Using Neural Network.
dc.description.abstractThis paper presents a simulation of a 6 degrees-of-freedom (6R) articulatedrobot arm using back propagation neural network to solve the problem regardinginverse kinematics for the industrial articulated robot. The Denavit – Hartenberg modelis used to analyze the robot arm movement. Next, the forward kinematics is used toidentify the relationships for each joint of the robot arm and to determine variousparameters for learning system of random neural network for 5,000 data points. Thesimulation 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.
dc.publisherUniversity of the Thai Chamber of Commerce
dc.rightsThis work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
dc.subject.otherComputer and Multimedia Engineering
dc.titleA Simulation of 6R Industrial Articulated Robot Arm Using Neural Network
dc.rights.holderUniversity of the Thai Chamber of Commerce of Engineering of the Thai Chamber of Commerce
Appears in Collections:GS: Theses / Independent Studies
Files in This Item:
File Description SizeFormat 
2759abstract_eng.pdf51.44 kBAdobe PDFThumbnail
2759abstract_thai.pdf85.66 kBAdobe PDFThumbnail
2759fulltext.pdf1.92 MBAdobe PDFThumbnail
Show simple item record Recommend this item

Page view(s) 50

checked on Aug 28, 2019

Download(s) 50

checked on Aug 28, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.