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
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.
-  J. Xiao, Z. Michalewicz, L. Zhang, and K. Trojanowski, “Adaptive Evolutionary planner/Navigator for Mobile Robots,” IEEE Transactions on Evolutionary Computation, 1-1, pp. 18-28, 1997.
-  K. Murase, Md. Monirul Islam, T. Hirata, A Matsumoto, H. Akita, T. Asai, K. Sakai, T. Sasajima, M. Naruse, S. Terao, and M. Okura, “Chapter 3: Analysis and production of organized behavior in real environment with a mini-robot Khepera,” Evolutionary Robotics III (Applied Artificial Intelligence Book), Ontario, Canada, pp. 81-108, 2000.
-  S. Nolfi and D. Floreano, “Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-organizing Machines,“ The MIT Press, 2000.
-  G. G. Yen and T. W. Hickey, “Reinforcement learning algorithms for robotic navigation in dynamic environments,” ISA Transactions, 43-2, pp. 217-230, 2004.
-  L. J. Lin, “Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching,” Machine Learning, 8, pp. 293-321, 1992.
-  M. Rodríguez, R. Iglesias, C. V. Regueiro, J. Correa, and S. Barro, “Autonomous and fast robot learning through motivation,” Robotics and Autonomous Systems, 55-9, pp. 737-740, 2007.
-  S. Bhatnagar and K. M. Babu, “New algorithms of the Q-learning type,” Automatica, 44-4, pp. 1111-1119, 2008.
-  M. Nowostawski, L. Epiney, and M. Purvis, “Self-Adaptation and Dynamic Environment Experiments with Evolvable Virtual Machines,” Engineering Self-Organizing Systems, pp. 46-60, Springer-Verlag, 2005.
-  M. Likhachev, M. Kaess, and R. C. Arkin, “Learning Behavioral Parameterization Using Spatio-Temporal Case-Based Reasoning,” Proc. of the IEEE Int. Conf. on Robotics and Automation, pp. 1282-1289, 2002.
-  M. Tan, “Cost-sensitive reinforcement learning for adaptive classification and control,” Proc. of the Ninth National Conf. on Artificial Intelligence, pp. 774-780, 1991.
-  N. Jakobi, “Minimal Simulations for Evolutionary Robotics,” Ph.D. thesis, School of Cognitive and Computing Sciences, University of Sussex, 1998.
-  F. Alnajjar and K. Murase, “Self organization of spiking neural network that generates autonomous behavior in a real mobile robot,” Int. Journal of Neural Systems, 16-4, pp. 229-239, 2006.
-  F. Alnajjar, I. B. M. Zin, and K. Murase, “A Spiking Neural Network with Dynamic Memory for a Real Autonomous Mobile Robot in Dynamic Environment,” Proc. of Int. Joint Conf. on Neural Networks, pp. 2207-2213, 2008.
-  W. Hamming, “Error Detecting and Error Correcting Codes,” Bell System Technical Journal, 29, pp. 147-150, 1950.
-  Developed by Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland 2005. Available: http://www.e-puck.org.
-  D. Floreano and C. Mattiussi, “Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies,” The MIT Press, 2008.
-  S. Kim, M. Spenko, S. Trujillo, B. Heyneman, V. Mattoli, and M. R. Cutkosky, “Whole body adhesion: Hierarchical, directional and distributed control of adhesive forces for a climbing robot,” In Proc. of the IEEE Int. Conf. on Robotics and Automation, Rome, pp. 1268-1273, 2007.
-  X. Wang, Z. Hou, A. Zou, M. Tan, and L. Cheng, “A behavior controller based on spiking neural networks for mobile robots,” Neurocomputing, 71, 4-6, pp. 655-666, 2008.
-  Y. Adachi, H. Saito, Y. Matsumoto, and T. Ogasawara, “Memory-Based Navigation using Data Sequence of Laser Range Finder,” Proc. of IEEE Int. Symposium on Computational Intelligence in Robotics and Automation, 1, pp. 479-484, 2003.