Adapting real mobile robots to complex environments using a pattern association network controller (PAN-C)

Indra Bin Mohd Zin, Fady Alnajjar, Kazuyuki Murase

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)312-319
Number of pages8
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Issue number3
Publication statusPublished - 2009
Externally publishedYes


  • Adaptive controller
  • Learning and memory
  • Pattern association network controller
  • Symmetrical neural network

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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