Journal of Automation and Control
ISSN (Print): 2372-3033 ISSN (Online): 2372-3041 Website: Editor-in-chief: Santosh Nanda
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Journal of Automation and Control. 2013, 1(1), 6-13
DOI: 10.12691/automation-1-1-2
Open AccessArticle

An ANN Based NARX GPS/DR System for Mobile Robot Positioning and Obstacle Avoidance

Ramazan Akkaya1, , Omer Aydogdu1 and Suleyman Canan2

1Department of Electrical and Electronics Engineering, Selcuk University, Konya, Turkey

2Research Dept. of Elfatek Electronics Corporation, Konya, Turkey

Pub. Date: May 16, 2013

Cite this paper:
Ramazan Akkaya, Omer Aydogdu and Suleyman Canan. An ANN Based NARX GPS/DR System for Mobile Robot Positioning and Obstacle Avoidance. Journal of Automation and Control. 2013; 1(1):6-13. doi: 10.12691/automation-1-1-2


Conventional sensor integration and navigation methods are based on the Kalman filter algorithm. Kalman filter needs a pre-defined model of the dynamic system. In most of the case non-linear system modeling might be a changing computational load. Artificial Neural Network (ANN) computing is a very powerful tool for solving non-linear problems involving mapping input and output relation without any prior knowledge of the system and the environment involved. This study has investigated Global Positioning System (GPS) and Dead Reckoning (DR) sensor fusion approach using ANN Nonlinear Autoregressive with external input (NARX) model. The ANN accepts navigation sensor data and is trained throughout a pre-design training track for gathering training data set which is used to predict mobile robot position where GPS signal is lost. In addition, a simple obstacle avoidance algorithm has been added to the system because the mobile robot can find its own trajectory again by circulates around the obstacle. The experimental results for different test data examples demonstrate that the proposed ANN NARX sensor fusion model can be used for reliable position and heading estimation of the mobile robot.

wheeled mobile robot kalman filter ANN NARX model dead reckoning obstacle avoidance

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