Paper:
Language: English:

Adapting Real Mobile Robots to Complex Environments Using a Pattern Association Network Controller (PAN-C)

Indra Bin Mohd Zin^*, Fady Alnajjar^, and Kazuyuki Murase^,***

^Industrial Computing Research Group, Graduate School of Information Science and Technology, University Kebangsaan Malaysia, Malaysia
^Department of Human and Artificial Intelligence System, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan
^Research and Education Program for Life Science, University of Fukui, Bunkyo, Fukui, Japan

Received: November 17, 2008

Accepted: February 25, 2009

Keywords: adaptive controller, learning and memory, symmetrical neural network, pattern association network controller

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.13, No.3 pp. 312-319, 2009

Abstract

Adapting real mobile robots to complex or dynamic environments is just one of the many challenges robotics researchers face. The difficulty in such environments is in developing a simple, quick adaptive controller that adapts robots to patterns in these environments, especially when individual patterns require unique behavior from the robot. Although most standard evolutionary algorithms attempt to obtain optimal networks for such environments, this is difficult to attain due to network confusion in adapting and readapting patterns. We propose a simple adaptive controller able to learn and remember. It simplifies environments into simple groups of patterns, each of which the robot can independently learn and memorize. The memory introduced in the controller enhances the robot's ability to track its own experience and to cope with upcoming events. Experimental results show that the controller handles general complexity and gives the robot more adaptability, stability, and autonomy.

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