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Applied Computational Intelligence and Soft Computing
Volume 2012, Article ID 382782, 8 pages
Research Article

Neural Behavior Chain Learning of Mobile Robot Actions

1Faculty of Electrical Engineering, University of Tuzla, 75000 Tuzla, Bosnia and Herzegovina
2Infonet, 75000 Tuzla, Bosnia and Herzegovina
3ABB, 75000 Tuzla, Bosnia and Herzegovina
4General Secretariat Council of Ministers of B&H, 71000 Sarajevo, Bosnia and Herzegovina

Received 25 April 2012; Accepted 24 September 2012

Academic Editor: R. Saravanan

Copyright © 2012 Lejla Banjanovic-Mehmedovic et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM) in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.