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Trends in Applied Sciences Research
Year: 2014  |  Volume: 9  |  Issue: 6  |  Page No.: 312 - 318

Underwater Mobile Robot Global Localization by using Feedforward Backpropagation Neural Network

Mohammed Waadalla, C.K. Yong, David F.W. Yap and R. Abd. Rahim    

Abstract: Underwater global localization is an essential tool for underwater researchers. In this study global gocalization for underwater mobile robot has been developed using Feedforward Backpropagation Neural Network (FBNN). Twelve sonar sensors have been recorded with the x and y location of the robot using MobotSim software. There are a total of 58081 points and 12 sonars that are used to record each point. These recordings have been used for supervised training by using MATLAB software. The results are determined by using four random points to calculate the location of the robot from the sonar sensor readings. The proposed method that is used in calculating x and y points has accuracy equal to 0.01 m. The result shows that in 10 layers network, the 0.000511 absolute error value with percentage error of 0.035% in x point and the 0.0028893 absolute error value with percentage error of 0.13% in y point are achieved. While, in 12 layers network, the 4.43x10-05 absolute error value with percentage error of 0.003% in x point and the 0.0001767 absolute error value with percentage error of 0.008% in y point are achieved. This study illustrates that feedforward backpropagation neural network can be used to determine the location of the robot with marginal percentage error. Moreover, the resulted percentage error is internationally accepted by electronic engineers.

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