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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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2012
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| 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 |
| Summary: | 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. |
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