Associative Model for Solving the Wall-Following Problem

  • Rodolfo Navarro
  • Elena Acevedo
  • Antonio Acevedo
  • Fabiola Martínez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7329)

Abstract

A navigation system for a robot is presented in this work. The Wall-Following problem has become a classic problem of Robotics due to robots have to be able to move through a particular stage. This problem is proposed as a classifying task and it is solved using an associative approach. In particular, we used Morphological Associative Memories as classifier. Three testing methods were applied to validate the performance of our proposal: Leave-One-Out, Hold-Out and K-fold Cross-Validation and the average obtained was of 91.57%, overcoming the neural approach.

Keywords

Classification Associative Models Morphological models Wall-Following 

References

  1. 1.
    ASIMO The World’s Most Advanced Humanoid Robot, http://asimo.honda.com/asimo-specs/
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
    Ross, J.S., Daida, J.M., Doan, C.M., Bersano-Begey, T.F., McClain, J.J.: Variations in Evolution of Subsumption Architectures Using Genetic Programming: The Wall Following Robot Revisited. In: Genetic Programming: Proceedings of the First Annual Conference, July 28 (1996)Google Scholar
  7. 7.
    Braunsting, R., Sanz, P., Ezkerra, J.M.: Fuzzy Logic Wall Following of a Mobile Robot Based on the Concept of General Perception. In: 7th International Conference on Advanced Robotics, ICAR 1995, pp. 367–376 (1995)Google Scholar
  8. 8.
    DeSouza, G.N., Kak, A.C.: Vision for Mobile Robot Navigation: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2), 237–267 (2002)CrossRefGoogle Scholar
  9. 9.
    Borenstein, J., Koren, Y.: Real-time Obstacle Avoidance for Fast Mobile Robots in Cluttered Environments. Reprint of Proceedings of the 1990 IEEE International Conference on Robotics and Automation, pp. 572–577 (1990)Google Scholar
  10. 10.
    Carelli, R., Oliveira, F.E.: Corridor navigation and wall-following stable control,for sonar-based mobile robots. Robotics and Autonomous Systems 45, 235–247 (2003)CrossRefGoogle Scholar
  11. 11.
    Meisburger, S., Hubler, A.: Chaos in Wall Following Robots (September 2006)Google Scholar
  12. 12.
    Bullen IV, H.W., Ranjan, P.: Chaotic transitions in Wall-Following Robots (August 2009)Google Scholar
  13. 13.
    Sadati, N., Taheri, J.: Solving Robot Motion Planning Problem Using Hopfield Neural Network In A Fuzzified Environment. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, pp. 1144–1149 (2002)Google Scholar
  14. 14.
    Huang, W.H., Beevers, K.R.: Topological Mapping with Sensing-limited Robots. In: Sixth International Workshop on the Algorithmic Foundations of Robotics (WAFR 2004), pp. 1–16 (2004)Google Scholar
  15. 15.
    Chung, T.L., Bui, T.H., Kim, S.B., Oh, M.S.: Wall-Following Control of a Two-Wheeled Mobile Robot. KSME International Journal 18(8), 1288–1296 (2004)Google Scholar
  16. 16.
    Mehta, S.: An Autonomous Wall Following Robot, Department of Electrical and Computer Engineering Cleveland State University Cleveland, Ohio 44115 (2008)Google Scholar
  17. 17.
    Gavrilut, I., Tiponut, V., Gacsadi, A., Tepelea, L.: Wall-following Method for an Autonomous Mobile Robot using Two IR Sensors. In: 12th WSEAS International Conference on Systems, Heraklion, Greece, July 22-24 (2008)Google Scholar
  18. 18.
    Huang, L.: Wall-following control of an infrared sensors guided wheeled mobile robot. International Journal of Intelligent Systems Technologies and Applications 7(1), 106–117 (2009)CrossRefGoogle Scholar
  19. 19.
    Lamperski, A.G., Loh, O.Y., Kutscher, B.L., Cowan, N.J.: Dynamical Wall-Following for a Wheeled Robot, using a Passive Tactile Sensor. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, pp. 3838–3843 (2005)Google Scholar
  20. 20.
    UC Irvine Machine Learning Repository, http://archive.ics.uci.edu/ml/
  21. 21.
    Freire, A.L., Barreto, G.A., Veloso, M., Varela, A.T.: Short-term memory mechanisms in neural network learning of robot navigation tasks: A case study. In: 6th Latin American Robotics Symposium (LARS), pp. 1–6 (2009)Google Scholar
  22. 22.
    Yáñez-Márquez, C.: Associative Memories Based on Order Relations and Binary Operators (In Spanish). PhD Thesis. Centro de Investigación en Computación, Mexico (2002)Google Scholar
  23. 23.
    Steinbuch, K.: Die Lernmatrix. Kybernetik 1(1), 36–45 (1961)MATHCrossRefGoogle Scholar
  24. 24.
    Willshaw, D., Buneman, O., Longuet-Higgins, H.: Non-holographic associative memory. Nature 222, 960–962 (1969)CrossRefGoogle Scholar
  25. 25.
    Anderson, J.A.: A simple neural network generating an interactive memory. Mathematical Biosciences 14, 197–220 (1972)MATHCrossRefGoogle Scholar
  26. 26.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences 79, 2554–2558 (1982)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Ritter, G.X., Sussner, P., Diaz de León, J.L.: Morphological Associative Memories. IEEE Transactions on Neural Networks 9, 281–293 (1998)CrossRefGoogle Scholar
  28. 28.
    Wall-Following Robot Navigation Data Data Set (2010), http://archive.ics.uci.edu/ml/datasets/Wall-Following+Robot+Navigation+Data (released)
  29. 29.
    Navarro, R., Pineda, G.: Solution to the Wall-Following problem by using Morphological Associative Memories, Thesis, Escuela Superior de Ingeniería Mecánica y Eléctrica, Mexico City (2011)Google Scholar
  30. 30.
    Acevedo, M.E., Yáñez, C., López, I.: Alpha-Beta Bidirectional Associative Memories: Theory and Applications. Neural Processing Letters (26), 1–40 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rodolfo Navarro
    • 1
  • Elena Acevedo
    • 1
  • Antonio Acevedo
    • 1
  • Fabiola Martínez
    • 1
  1. 1.Escuela Superior de Ingeniería Mecánica y Eléctrica, IPNMexico CityMexico

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