Development of vision autonomous guided vehicle behaviour using neural network

This project is motivated by an interest in promoting the use of artificial neural network in manufacturing. Automated guided vehicle (AGV) is used in advanced manufacturing system that can help to reduce cost and increase efficiency. The application of neural network in the AGV is to help in increa...

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Main Author:	Husnul ‘Asyiyyah, Mohamad @ Awang
Format:	Undergraduates Project Papers
Language:	English
Published:	2012
Subjects:	TL Motor vehicles. Aeronautics. Astronautics
Online Access:	http://umpir.ump.edu.my/id/eprint/3447/ http://umpir.ump.edu.my/id/eprint/3447/ http://umpir.ump.edu.my/id/eprint/3447/1/cd6248_100.pdf

id	ump-3447
recordtype	eprints
spelling	ump-34472015-03-03T08:01:15Z http://umpir.ump.edu.my/id/eprint/3447/ Development of vision autonomous guided vehicle behaviour using neural network Husnul ‘Asyiyyah, Mohamad @ Awang TL Motor vehicles. Aeronautics. Astronautics This project is motivated by an interest in promoting the use of artificial neural network in manufacturing. Automated guided vehicle (AGV) is used in advanced manufacturing system that can help to reduce cost and increase efficiency. The application of neural network in the AGV is to help in increasing the AGVs performance and efficiency. The objectives of this project are to develop a line recognition algorithm for automated guided vehicle and to understand two types of neural networks that can be use in manufacturing. The types of guidelines used in this project are straight guideline, turn right guideline, turn left guideline and stop guideline. The line recognition algorithm involved the pre-processing images of the guideline captured by a camera and extracts the feature of the images by using first order statistics to calculate the values of mean, variance, skewness and kurtosis and train the image recognition by using neural networks. Neural network process involved setup the two types of neural network, trained and tested the network and compared the result. There are two types of neural network that used in this project namely, Feedforward Backpropagation and Radial Basis. In Feedforward Backpropagation Network the parameter involves are transfer function and number of neurons. Mean Squared Error (MSE) is used as performance function. Radial Basis Network with spread constant one give significantly better performance compared to Feedforward Backpropagation Network. It produced much lower error compared to Feedforward Backpropagation Network. This project used MATLAB software which able to perform image processing tasks, train and simulate neural networks. 2012-06 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/3447/1/cd6248_100.pdf Husnul ‘Asyiyyah, Mohamad @ Awang (2012) Development of vision autonomous guided vehicle behaviour using neural network. Faculty of Manufacturing Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my:8080/lib/item?id=chamo:66796&theme=UMP
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TL Motor vehicles. Aeronautics. Astronautics
spellingShingle	TL Motor vehicles. Aeronautics. Astronautics Husnul ‘Asyiyyah, Mohamad @ Awang Development of vision autonomous guided vehicle behaviour using neural network
description	This project is motivated by an interest in promoting the use of artificial neural network in manufacturing. Automated guided vehicle (AGV) is used in advanced manufacturing system that can help to reduce cost and increase efficiency. The application of neural network in the AGV is to help in increasing the AGVs performance and efficiency. The objectives of this project are to develop a line recognition algorithm for automated guided vehicle and to understand two types of neural networks that can be use in manufacturing. The types of guidelines used in this project are straight guideline, turn right guideline, turn left guideline and stop guideline. The line recognition algorithm involved the pre-processing images of the guideline captured by a camera and extracts the feature of the images by using first order statistics to calculate the values of mean, variance, skewness and kurtosis and train the image recognition by using neural networks. Neural network process involved setup the two types of neural network, trained and tested the network and compared the result. There are two types of neural network that used in this project namely, Feedforward Backpropagation and Radial Basis. In Feedforward Backpropagation Network the parameter involves are transfer function and number of neurons. Mean Squared Error (MSE) is used as performance function. Radial Basis Network with spread constant one give significantly better performance compared to Feedforward Backpropagation Network. It produced much lower error compared to Feedforward Backpropagation Network. This project used MATLAB software which able to perform image processing tasks, train and simulate neural networks.
format	Undergraduates Project Papers
author	Husnul ‘Asyiyyah, Mohamad @ Awang
author_facet	Husnul ‘Asyiyyah, Mohamad @ Awang
author_sort	Husnul ‘Asyiyyah, Mohamad @ Awang
title	Development of vision autonomous guided vehicle behaviour using neural network
title_short	Development of vision autonomous guided vehicle behaviour using neural network
title_full	Development of vision autonomous guided vehicle behaviour using neural network
title_fullStr	Development of vision autonomous guided vehicle behaviour using neural network
title_full_unstemmed	Development of vision autonomous guided vehicle behaviour using neural network
title_sort	development of vision autonomous guided vehicle behaviour using neural network
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/3447/ http://umpir.ump.edu.my/id/eprint/3447/ http://umpir.ump.edu.my/id/eprint/3447/1/cd6248_100.pdf
first_indexed	2023-09-18T21:57:45Z
last_indexed	2023-09-18T21:57:45Z
_version_	1777414193018830848

Development of vision autonomous guided vehicle behaviour using neural network

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