ABSTRACT To solve the mapping problem for the mobile robots in the unknown environment, a dynamic growing self-organizing map with growing-threshold tuning automatically algorithm (DGSOMGT) based on Self-organizing Map is proposed. It introduces a value of spread factor to describe the changing process of the growing threshold dynamically. The method realizes the network structure growing by training through mobile robot movement constantly in the unknown environment. The proposed algorithm is based on self-organizing map and can adjust the growing-threshold value by the number of network neurons increasing. It avoids tuning the parameters repeatedly by human. The experimental results show that the proposed method detects the complex environment quickly, effectively and correctly. The robot can realize environment mapping automatically. Compared with the other methods the proposed mapping strategy has better topological properties and time property.
Cite this paper
nullX. Ruan, Y. Gao, H. Song and J. Chen, "A New Dynamic Self-Organizing Method for Mobile Robot Environment Mapping," Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 4, 2011, pp. 249-256. doi: 10.4236/jilsa.2011.34028.
 H. P. Moravec and A. Elfes, “High Resolution Maps from Wide Angle Sonar,” IEEE International Conference on Robotics and Automation, St. Louis, 25-28 March 1985, pp. 116-121.
 B. Kuipers and Y. T. Byun, “A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations,” Journal of Robotics and Autonomous Systems, Vol. 8, No. 1-2, 1999, pp. 47-63.
 R. Chatila and J. P. Laumond, “Position Referencing and Consistent World Modeling for Mobile Robots,” IEEE International Conference on Robotics and Automation, St. Louis, 25-28 March 1985, pp. 138-145.
 D. Avots, E. Lin, R. Thibaux, et al., “A Probabilistic Technique for Simultaneous Localization and Door State Estimation with Mobile Robots in Dynamic Environments,” Proceedings of IEEE/RSJ International conference on Intelligent Robots and System, Lausanne, 30 September-5 October 2002, pp. 521-526.
 B. Kuipers, J. Modayil, P. Beeson, M. Macmahon and F. Savelli, “Local Metrical and Global Topological Maps in the Hybrid Spatial Semantic Hierarchy,” Proceedings of International conference on Robotics and Automation, New Orleans, 26 April-1 May 2004, pp. 4845-4851.
 T. Kohonen, “Self-Organized Formation of Topologically Correct Feature Maps,” Biological Cybernetics, Vol. 43, No. 1, 1982, pp. 59-69. doi:10.1007/BF00337288
 U. Nehmzow and T. Smithers, “Mapbuilding Using Self- Organizing Networks in Really Useful Robots,” From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior, Paris, 24-28 September 1991, pp. 152-159.
 S. Najand, Z. Lo and B. Bavarian, “Application of Self-Organizing Neural Networks for Mobile Robot Environment Learning,” Neural Network in Robotics, Kluwer Academic Publishers, Vol. 202, No. 1, 1993, pp. 85-96.
 V. Morellas, J. Minners and M. Donath, “Implementation of Real Time Spatial Mapping in Robotic Systems through Self-Organizing Neural Networks,” Proceedings of 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Co-operative Robots, Vol. 1, Pittsburgh, 5-9 August 1995, pp. 277-284.
 F. Shen, O. Hasegawa and H. Osamu, “An Enhanced Self-Organizing Incremental Neural Network for Online Unsupervised Learning,” Neural Networks, Vol. 20, No. 8, 2007, pp. 893-903. doi:10.1016/j.neunet.2007.07.008
 T. Oishi, K. Furuta and S. Kondo, “Workspace Recognition and Navigation of Autonomous Mobile Robot Using Self-Creating and Organizing Neural Network,” Transaction of the Society of Instrument and Dontrol Engineers, Vol. 33, No. 3, 1997, pp. 203-208.
 G. J. Choi and D. S. Ahn, “Map Building and Localization on Autonomous Mobile Robot Using Graph and Fuzzy Inference System,” IEEE International Joint Conference on Neural Networks, Budapest, 25-29 July 2004, pp. 2419-2424.
 Y. Zhuang, X. D. Xu and W. Wang, “Mobile Robot Geometric-Topological Map Building and Self-Localization,” Control and Decision, Vol. 20, No. 7, 2005, pp. 815-818.
 A. Kawewong, Y. Honda, M. Tsuboyama and O. Hasegawa, “Reasoning on the Self-Organizing Incremental Associative Memory for Online Robot Path Planning,” IEICE Transactions on Information and Systems, Vol. E93-D, No. 3, 2010, pp. 569-582.
 X. G. Ruan and X. T. Xing, “Application of Autonomous Mapping Algorithm on a Desktop Robot System,” Proceedings of the 5th International Conference on Natural Computation, Tianjin, 14-16 August 2009, pp. 448-453. doi:10.1109/ICNC.2009.156
 T. M. Martinetz, S. G.Berkovich and K. J. Schulten, “Neural-Gas Network for Vector Quantization and Its Application to Ime-Series Prediction,” IEEE Transactions on Neural Networks, Vol. 4, No. 4, 1996, pp. 558-569.
 B. Fritzke, “Growing Cell Structures—A Self-Organizing Network for Unsupervised and Supervised Learning,” Neural Network, Vol. 7, No. 9, 1994, pp. 1411-1460.
 D. Choi and S. Park, “Self-Creating and Organizing Neural Networks,” IEEE Transactions on Neural Networks, Vol. 5, No. 4, 1994, pp. 561-575. doi:10.1109/72.298226
 D. Alahakoon and S. K. Halgamuge, “Dynamic Self-Organizing Maps with Controlled Growth for Knowledge Discovery,” IEEE Transactions on Neural Networks, Vol. 11, No.3, 2000, pp. 601-614. doi:10.1109/72.846732
 F. Shen and O. Hasegawa, “An Incremental Network for On-Line Unsupervised Classification and Topology Learning,” Neural Networks, Vol. 19, 2006, pp. 90-106.