Advertisement

A Bottom-up Robot Architecture Based on Learnt Behaviors Driven Design

  • Ignacio HerreroEmail author
  • Cristina Urdiales
  • José Manuel Peula
  • Francisco Sandoval
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)

Abstract

In reactive layers of robotic architectures, behaviors should learn their operation from experience, following the trends of modern intelligence theories. A Case Based Reasoning (CBR) reactive layer could achieve this goal but, as complexity of behaviors increases, the curse of dimensionality arises:too many cases in the behaviors casebases degrade response times so robot’s reactiveness is finally too slow for a good performance. In this work we analyze this problem and propose some improvements in the traditional CBR structure and retrieval phase, at reactive level, to reduce the impact of scalability problems when facing complex behaviors design.

Keywords

Case based reasoning Reactive layer Learning architecture Robotics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–52 (1994)Google Scholar
  2. 2.
    Aguirre, E., González, A.: Fuzzy behaviors for mobile robot navigation: design, coordination and fusion. Int. J. Approx. Reason. 25(3), 255–289 (2000)zbMATHCrossRefGoogle Scholar
  3. 3.
    Brooks, R.: A robust layered control system for a mobile robot. IEEE J. Robot Automat. 2(1), 14–23 (1986)CrossRefGoogle Scholar
  4. 4.
    Garner, W.R.: The processing of information and structure. Halsted Press, The Experimental Psychology Series. L. Erlbaum Ass. (1974)Google Scholar
  5. 5.
    Hawkins, J., Blakeslee, S.: On Intelligence. Owl Books (2004)Google Scholar
  6. 6.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: IEEE 1985 Int. Conf. Robot, vol. 2, pp. 500–505 (1985)Google Scholar
  7. 7.
    Kruusmaa, M.: Global navigation in dynamic environments using case-based reasoning. Autonomous Robots 14(1), 71–91 (2003)zbMATHCrossRefGoogle Scholar
  8. 8.
    Low, K.H., Leow, W.K., Ang Jr., M.H.: A hybrid mobile robot architecture with integrated planning and control. In: Int. J. Conf. Auton. Agent Multi. Ag. (AAMAS 2002), pp. 219–226, NY, USA (2002)Google Scholar
  9. 9.
    Mataric, M.J.: Interaction and Intelligent Behavior. PhD thesis, Department of Electronic Engineering and Computer Sciencie (1994)Google Scholar
  10. 10.
    Murphy, R.: Introduction to AI Robotics. MIT Press, Cambridge (2000)Google Scholar
  11. 11.
    Murray, J.C., Erwin, H.R.: Wermter., S.: Robotic sound-source localization architecture usingcross-correlation and recurrent neural networks. Neural Networks 22(2), 173–189 (2009)CrossRefGoogle Scholar
  12. 12.
    Peula, J.M., Urdiales, C., Herrero, I., Sánchez-Tato, I., Sandoval, F.: Pure reactive behavior learning using case based reasoning for a vision based 4-legged robot. Robot. Auton. Syst. 57(6–7), 688–699 (2009)CrossRefGoogle Scholar
  13. 13.
    Ros, Raquel, López de Màntaras, Ramon, Arcos, Josep-Lluís, Veloso, Manuela M.: Team Playing Behavior in Robot Soccer: A Case-Based Reasoning Approach. In: Weber, Rosina O., Richter, Michael M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 46–60. Springer, Heidelberg (2007)Google Scholar
  14. 14.
    Wang, M., Liu, J.N.K.: Fuzzy logic-based real-time robot navigation in unknown environment with dead ends. Robot. Auton. Syst. 56(7), 625–643 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ignacio Herrero
    • 1
    Email author
  • Cristina Urdiales
    • 1
  • José Manuel Peula
    • 1
  • Francisco Sandoval
    • 1
  1. 1.Dpt. Tecnología ElectrónicaUniversity of MálagaMálagaSpain

Personalised recommendations