4.8 Summary
In the previous sections we have seen how the evolutionary computations algorithms were successfully used to evolve many types of controllers for Khepera robot. It was used to evolve neural network synaptic weights in the obstacle avoidance behavior of experiment 1 and the battery recharging behavior of experiment 3. We have also seen how it can evolve the architecture of the neural network along with the synaptic weights as in the experiment of evolving light seeking behavior. Alternatively, it can evolve the learning rules and learning rate necessary for training the neural network synaptic weights. Other types of controllers were successfully evolved too, such as fuzzy logic controllers and computer programs.
Many other experiments are conducted using evolutionary computations on different robotic platforms recently. In fact, evolutionary computation is a very promising approach for designing controllers for mobile robots.
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.
Similar content being viewed by others
References
K-Team, “Khepera User Manual,” Lasuanne, Switzerland, 1999.
F. Mondada, F. Franz and I. Paolo, “Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithm,” Proceedings of the Third International Symposium on Experimental Robotics, Kyoto, Japan, 1993.
A. Schultz and J. Grefenstette, “Using a Genetic Algorithm to Learn Behaviors for Autonomous Vehicles,” Naval Research Laboratory, Washington, Dc, 1992.
J. Meyer, P. Husbands and I. Harvey, “Evolutionary Robotics: a Survey of Applications and Problems,” In Evolutionary Robotics: First European Workshop, Evorobot’98, P. Husbands and J. Meyer (editors), Springer Verlag 1998.
I. Harvey, P. Husbands, D. Cliff, A. Thompson, N. Jakobi, “Evolutionary Robotics: the Sussex Approach,” In Robotics and Autonomous Systems, Vol. 20, pp. 205–224, 1997.
A. Loffler, J. Klahold and U. Ruckert, “The Mini-Robot Khepera as a Foraging Animate: Synthesis and Analysis of Behavior,” In Proceedings of the Fifth International Heinz Nixdorf Symposium: Autonomous Minirobots for Research and Edutainment (AMiRE), Vol. 97, pp. 93–130, 2001.
D. Floreano and F. Mondada, “Automatic Creation of An Autonomous Agent: Genetic Evolution of a Neural Network Driven Robot,” From Animals to An-imats:3, Proceedings of the Conference on Simulation of Adaptive Behavior, edited by D. Cliff, P. Husbands and S. Wilson, MIT Press, 1994.
M. Botros “Evovlving Neural Network Based Controllers for Autonomous Robots Using Genetic Algorithms,” Master Thesis, Cairo University, Egypt, 2003.
M. Hulse, B. Lara, F. Pasemann and U. Steinmetz, “Evolving Neural Behaviour Control for Autonomous Robots,” Max-Planck Institute for Mathematics in the Sciences, Leipzig, Germany, 2001.
D. Floreano and F. Mondada, “Evolution of Homing Navigation in a Real Mobile Robot,” IEEE Transactions on Systems, Man, and Cybernetics (B), Vol. 2, pp. 396–407, 1996.
S. Nolfi, “Using Emergent Modularity to Develop Control Systems for Mobile Robots,” Journal of Adaptive Behavior, Vol. 5, pp. 343–363, 1997.
C. Scheier and R. Pfeifer, “Classification as Sensory-Motor Coordination,” Advances in Artificial Life: Proceedings of the Third European Conference on Artificial Life, edited by F. Moran, A. Moreno, J. Merelo and P. Chacon, Springer Verlag, 1995.
D. Floreano and S. Nolfi, “God Save the Red Queen! Competetion in Co-evolutionary Robotics,” Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, edited by J. Koza, K. Deb, M. Dorigo, D. Fogel, M. Garzon, H. Iba, and R. Riolo, pp. 398–406, 1997.
D. Floreano and S. Nolfi, “Adaptive Behavior in Competing Co-Evolving Species,” Fourth European Conference on Artificial Life, MIT press, Cambridge MA, editted by P. Husbands and I. Harvey, pp. 378–387, 1997.
Zadeh, L., “Outline of a New Approach to the Analysis of Complex Systems and Decision Process,” IEEE Transaction Systems, Man and Cybernetics, Vol. 3, pp 28–40, 1973.
E. Mamdani and S. Assilian, “An Experiment in Linguistic Synthesis with Fuzzy Logic Controller,” Journal of Man-Machine Studies, Vol. 7, pp. 1–7, 1975.
S. Lee and S. Cho, “Emergent Behaviors of a Fuzzy Sensory-Motor Controller Evolved by Genetic Algorithm,” IEEE Transaction Systems Man and Cybernetics (B), Vol. 31, No. 6, pp. 919–929, 2001.
J. Koza, “Genetic Programming,” MIT Press, Cambridge MA, 1992.
P. Nordin and W. Banzhaf, “Genetic Programming Controlling a Miniature Robot,” Working Notes for the AAAI Symposium on Genetic Programming, MIT, Cambridge MA, 1995.
W. Gerstner and W. Kistler, “Spiking Neuron Models,” Cambridge University Press, 2002.
W. Gerstner, J. van Hemmen, and J. Cowan, “What Matters in Neuronal Locking?,” Neural Computation, Vol. 8, pp. 1653–1676, 1996.
D. Floreano and C. Mattiussi, “Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots,” Evolutionary Robotics. From Intelligent Robotics to Artificial Life, Springer Verlag, Tokyo, 2001.
E. Tunstel, H. Danny, and M. Jamshidi, “Behavior Hierarchy for Autonomous Mobile Robots: Fuzzy-behavior modulation and evolution,” International Journal of Intelligent Automation and Soft Computing, Special Issue: Autonomous Control Engineering at NASA ACE Center, Vol. 3, pp. 37–49, 1997.
J. Godjevac, “Comparative Study of Fuzzy Control, Neural Network Control and Neuro-Fuzzy Control”, In Fuzzy Set Theory and Advanced Mathematical Applications, D. Ruan Ed., Kluwer Academic, Chapter 12, pp. 291–322, 1995.
S. Marapane, M. Trivedi, N. Lassiter and M. Holder, “Motion Control of Cooperative Robotic Teams through Visual Observation and Fuzzy Logic Control,” Proceedings of IEEE International Conference on Robotics and Automation, Vol. 2, pp. 1738–1743, 1996.
F. Hoffmann and G. Pfister, “Evolutionary Design of a Fuzzy Knowledge Base for a Mobile Robot,” International Journal of Approximate Reasoning, Vol. 17, pp. 447–469, 1997.
D. Goldberg, B. Krob and K. Deb, “Messy Gentic Algorithms Motivations, Analysis and First Results,” Complex Systems, Vol. 3, pp. 493–530, 1989.
M.A. Lewis, M. Hartmann, R. Etienne-Cummings, and A. Cohen, “Biomorphic Control of a Running Robot Leg using a Custom aVLSI CPG Chip,” Neuro-computing, Vol. 38–40, pp. 1409–1421, June 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Botros, M. (2005). Evolving Controllers for Miniature Robots. In: Nedjah, N., Mourelle, L.d.M. (eds) Evolvable Machines. Studies in Fuzziness and Soft Computing, vol 161. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32364-3_4
Download citation
DOI: https://doi.org/10.1007/3-540-32364-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22905-6
Online ISBN: 978-3-540-32364-8
eBook Packages: EngineeringEngineering (R0)