Abstract
Structure and function of a small but effective neural network controlling the behavior of an autonomous miniatur robot is analyzed. The controller was developed with the help of an evolutionary algorithm, and it uses recurrent connectivity structure allowing non-trivial dynamical effects. The interplay of three different hysteresis elements leading to a skilled behavior of the robot in challenging environments is explicitly discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Braitenberg, V. (1984), Vehicles: Experiments in Synthetic Psychology, MIT Press, Cambridge, MA.
Husbands, P., Harvey, I. and Cliff, D. (1993), Analysing recurrent dynamical networks evolved for robot control, Proceedings of the Third IEE International Conference on Artificial Neural Networks (ANN93), pp. 158–162. IEE Press.
Mondada, F., Franzi, E., and Ienne, P. (1993), Mobile robots miniturization: a tool for investigation in control algorithms, in: Proceedings of ISER’ 93, Kyoto, October 1993.
Nolfi, S., and Floreano, D. (2000), Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines, MIT Press, Cambridge.
Pasemann, F. (1993), Dynamics of a single model neuron, International Journal of Bifurcation and Chaos, 2, 271–278.
Pasemann, F. (1995), Characteristics of periodic attractors in neural ring networks, Neural Networks, 8, 421–429.
Pasemann, F. (1998), Evolving neurocontrollers for balancing an inverted pendulum, Network: Computation in Neural Systems, 9, 495–511.
Pasemann, F., Steinmetz, U., Hülse, M., and Lara, B. (2001), Robot Control and the Evolution of Modular Neurodynamics, Theory in Biosciences, 120, 311–326.
Pfeifer, R., and Scheier, C. (2000), Understanding Intelligence, MIT Press, Cambridge.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hülse, M., Pasemann, F. (2002). Dynamical Neural Schmitt Trigger for Robot Control. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_127
Download citation
DOI: https://doi.org/10.1007/3-540-46084-5_127
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44074-1
Online ISBN: 978-3-540-46084-8
eBook Packages: Springer Book Archive