Evolving Robot’s Behavior by Using CNNs

  • Eleonora Bilotta
  • Giuseppe Cutrí
  • Pietro Panano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


This paper deals with a new kind of robotic control, based on Chua’s nonlinear circuit called Cellular Neural Network (CNN). A CNN is a net of coupled circuits, connected in a grid structure, which inherits its features and properties from the well known Artificial Neural Network and Cellular Automata. It has been demonstrated that CNNs are able of universal computation, many cognitive processes such as pattern recognition, features extraction, image processing, and mathematical simulations of nonlinear equations such as Navier-Stokes equations, reaction-diffusion equations, and so on. Using an approach like Evolutionary Robotics, we evolved, instead of Neural Networks, CNNs by using Genetic Algorithms (GAs), for controlling the behavior of an hexapod robot in a simulated environment. We developed a Java3D software in which physical simulations are carried on by using different kind of robots. In this program, a module for evolving the robot’s behavior by GAs has been implemented. Furthermore, many advanced sensors and actuators complete the evolution of the robot’s behavior. The evolved behavior of our robots is very similar to that of real insects, and we analyzed the pathways these agents perform in the simulated environment.


Genetic Algorithm Internal Joint Cellular Neural Network Spherical Joint Evolutionary Robotic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nolfi, S., Floreano, D.: Evolutionary Robotics - The Biology, Intelligence, and Technology of Self-Organizing Machines. The MIT Press, Cambridge (1997)Google Scholar
  2. 2.
    Arena, P., Fortuna, L., Branciforte, M.: Realization of a Reaction-Diffusion CNN Algorithm for Locomotion Control in an Hexapode Robot. Journal of VLSI Signal Processing (1999)Google Scholar
  3. 3.
    Arena, P., Basile, A., Fortuna, L., Frasca, M., Patané, L.: A CNN Approach for Controlling a Roving Robot. In: CLAWAR (2003)Google Scholar
  4. 4.
    Manganaro, G., Arena, P., Fortuna, L.: Cellular Neural Networks – Chaos, Complexity and VLSI Processing. Springer, Heidelberg (1998)Google Scholar
  5. 5.
    Kozek, T., Roska, T., Chua, L.O.: Genetic Algorithm for CNN Template Learning. IEEE Transactions on Circuits and Systems (1993)Google Scholar
  6. 6.
    Chua, L.O.: CNN: A Paradigm for Complexity. World Scientific Publishing Co. Pte. Ltd., Singapore (1998)MATHCrossRefGoogle Scholar
  7. 7.
    Holland, J.: Adaptation in natural and artificial systems. Penguin Books (1993)Google Scholar
  8. 8.
    Bianco, R., Nolfi, S.: Evolving the neural controller for a robotic arm able to grasp objects on the basis of tactile sensors. Adaptive Behavior (2004)Google Scholar
  9. 9.
    Nolfi, S., Marocco, D.: Evolving robots able to visually discriminate between objects with different sizes. International Journal of Robotics and Automation (2002)Google Scholar
  10. 10.
    Miglino, O., Lund, H.H., Nolfi, S.: Evolving mobile robots in simulated and real environments. Artificial Life (1995)Google Scholar
  11. 11.
  12. 12.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eleonora Bilotta
    • 1
  • Giuseppe Cutrí
    • 2
  • Pietro Panano
    • 1
  1. 1.University of CalabriaArcavacata di RendeItaly
  2. 2.University of TurinTurinItaly

Personalised recommendations