Implementation of a basic reactive behavior in mobile robotics through artificial neural networks

  • R. Iglesias
  • C. V. Regueiro
  • J. Correa
  • S. Barro
Neural Networks for Communications, Control and Robotics
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1240)


In this work we describe the design and implementation in a Nomad200 mobile robot of a reactive behavior aimed at wall following. A detailed analysis of the application domain has allowed us to modularize the design, conjugating in its. synthesis the potential of artificial neural networks for sensorial abstraction with other decision modules. We have carried out several experiments both in simulated and in real environments, obtaining very good results in different and unfavorable situations, which proves the robustness and flexibility of the system.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    T.L. Anderson and M. Donath, “Animal Behavior as a Paradigm for Developing Robot Autonomy”, in Designing Autonomous Agents, P. Maes (Ed.), MIT Press, pp. 145–168, 1990.Google Scholar
  2. [2]
    R.A. Brooks, “A Robusted Layered Control System for a Mobile Robot”, IEEE Journal Robotics and Automation, RA-2, April, pp. 14–24, 1986.Google Scholar
  3. [3]
    P. Maes, “Behavior-Based Artificial Intelligence”, Proc. of the 2nd. Conf. on Adaptive Behavior, MIT Press, 1993.Google Scholar
  4. [4]
    J.H. Conneil, “Minimalist Mobile Robotics”, Academic Press, 1990.Google Scholar
  5. [5]
    A. Saffioti, K. Konolige and E.H. Ruspini, “A multivalued logic approach to integrating planning and control”, Artificial Inteligence, Vol. 76, pp. 481–526, 1995.Google Scholar
  6. [6]
    M.C. Garcia-Alegre, P. Bustos and D. Guinea, “Complex behaviour generation on autonomous robots: A case study”, Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, Vancouver, pp. 1729–1734, 1995.Google Scholar
  7. [7]
    J. Yen and N. Pfluger, “A Fuzzy Logic Based Extension to Payton and Rosenblatt's Comand Fusion Method for Mobile Robot Navigation”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 25, No. 6, pp. 971–978, 1995.Google Scholar
  8. [8]
    R.C. Arkin, “Motor Schema-Based Mobile Robot Navigation”, The International Journal of Robotics Research, Vol. 8, No. 4, pp. 92–112, 1989.Google Scholar
  9. [9]
    C. Scheier and R. Pfeifer, “Classification as Sensory-Motor Coordination. A Case Study on Autonomous Agents”, Proc. of the Third European Conf. on Artificial Inteligence, Berlin, Springer, pp. 657–667, 11995Google Scholar
  10. [10]
    S. Mahadevan and J. Connell, “Automatic Programming of Behavior-Based Robots Using Reinforcement Learning”, Artificial Intelligence, Vol. 55, pp. 311–365. 1992.Google Scholar
  11. [11]
    J. del R. Millan, “Reinforcement learning of goal-directed obstacle-avoiding reaction strategies in an autonomous mobile robot”. Robotics and Autonomous Systems, Vol. 15, pp. 275–299, 1995.Google Scholar
  12. [12]
    M. Meng and A.C. Kak, “Mobile Robot Navigation Using Neural Networks and Nonmetrical Environment Models”, IEEE Control Systems, October, pp. 30–39, 1993.Google Scholar
  13. [13]
    J.R. Pimentel, D. Gachet, L. Moreno and M.A. Salichs, “On-line Performance Enhancement of a Behavioral Neural Network Controller”, in New Trends in Neural Computation, IWANK93, J. Mira, J. Cabestany and A. Prieto (Eds.), Springer-Verlag, pp. 694–701, 1993.Google Scholar
  14. [14]
    S. Nagata, M. Sekiguchi and K. Asakawa, “Mobile Robot Control by a Structured Hierarchical Neural Network”, IEEE Contr. Syst. Mag., April, pp. 69–76, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • R. Iglesias
    • 1
  • C. V. Regueiro
    • 1
  • J. Correa
    • 1
  • S. Barro
    • 1
  1. 1.Grupo de Sistemas Inteligentes. Dpto. Electrónica y Computatión. Facultad de FísicaUniv. Santiago de CompostelaSantiago de CompostelaSpain

Personalised recommendations