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Applying evolution strategies to neural networks robot controller

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Engineering Applications of Bio-Inspired Artificial Neural Networks (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1607))

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Abstract

In this paper an evolution strategy (ES) is introduced, to learn weights of a neural network controller in autonomous robots. An ES is used to learn high-performance reactive behavior for navigation and collisions avoidance. The learned behavior is able to solve the problem in different environments; so, the learning process has proven the ability to obtain a specialized behavior. All the behaviors obtained have been tested in a set of environment and the capability of generalization is showed for each learned behavior. No subjective information about “how to accomplish the task” has been included in the fitness function. A simulator based on mini-robot Khepera has been used to learn each behavior.

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References

  1. Brooks R. A. “Intelligence without Representation”. Artificial Intelligence, 47, 139–159, (1991).

    Article  Google Scholar 

  2. Ishikawa S. “A Method of Autonomous Mobile Robot Navigation by using Fuzzy Control”, Advanced Robotics, vol. 9, No. 1, 29–52, (1995)

    Article  Google Scholar 

  3. Matellán, V., Molina J.M., Sanz J., Fernández C. “Learning Fuzzy Reactive Behaviors in Autonomous Robots”. Proceedings of the Fourth European Workshop on Learning Robots, Germany, (1995).

    Google Scholar 

  4. Miglino O, Hautop H., Nolfi S. “Evolving Mobile Robots in Simulated and Real Environment”, Artificial Life 2:417–434 (1995).

    Article  Google Scholar 

  5. Rechenberg, I. Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann-Holzboog, Stuttgart (1973).

    Google Scholar 

  6. Schwefel, H. P. Numerical Optimization of Computer Models. New York: John Wiley & Sons (1981).

    MATH  Google Scholar 

  7. Goldberg D., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York, (1989).

    MATH  Google Scholar 

  8. Rechenberg I., Evolution strategy: Nature's Way of Optimization. In H. W. Bergmann, editor, “Optimization: Methods and Applications, Possibilities and Limitations”, Lecture Notes in Engineering, pag 106–26, Springer, Bonn (1989).

    Google Scholar 

  9. Mondada F. and Franzi P.I. “Mobile Robot Miniaturization: A Tool for Investigation in Control Algorithms”. Proceedings of the Second International Conference on Fuzzy Systems. San Francisco, USA, (1993).

    Google Scholar 

  10. Sommaruga L., Merino I., Matellán V and Molina J. “A Distributed Simulator for Intelligent Autonomous Robots”, Fourth International Sympsium on Intelligent Robotic Systems-SIRS96, Lisboa (Portugal), (1996).

    Google Scholar 

  11. Braitenberg V. Vehicles: experiments on synthetic psychology. MIT Press Cambridge, Massachusets (1984).

    Google Scholar 

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José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Berlanga, A., Molina, J.M., Sanchis, A., Isasi, P. (1999). Applying evolution strategies to neural networks robot controller. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100519

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  • DOI: https://doi.org/10.1007/BFb0100519

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66068-2

  • Online ISBN: 978-3-540-48772-2

  • eBook Packages: Springer Book Archive

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