A simulation of 6R industrial articulated robot arm using backpropagation neural network

Author

Manigpan, Supachoke ; Kiattisin, Supaporn ; Leelasantitham, Adisorn

Author_Institution

Sch. of Eng., Univ. of the Thai Chamber of Commerce, Bangkok, Thailand

fYear

2010

Firstpage

823

Lastpage

826

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.

Keywords

backpropagation; industrial manipulators; manipulator kinematics; neural nets; Denavit-Hartenberg model; backpropagation neural network; industrial articulated robot; inverse kinematics; learning system; random neural network; robot arm; Artificial neural networks; Joints; Kinematics; Manipulators; Mathematical model; Service robots; articulated robot arm; inverse kinematics; neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Automation and Systems (ICCAS), 2010 International Conference on

Conference_Location

Gyeonggi-do

Print_ISBN

978-1-4244-7453-0

Electronic_ISBN

978-89-93215-02-1

Type

conf

Filename

5670125