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Evolving Neural Behaviour Control for Autonomous Robots

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 2130)

Abstract

An evolutionary algorithm for the creation of recurrent network structures is presented. The aim is to develop neural networks controlling the behaviour of miniature robots. Two different tasks are solved with this approach. For the first, the agents are required to move within an environment without colliding with obstacles. In the second task, the agents are required to move towards a light source. The evolution process is carried out in a simulated environment and individuals with high performance are also tested on a physical environment with the use of Khepera robots.

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  • DOI: 10.1007/3-540-44668-0_132
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References

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© 2001 Springer-Verlag Berlin Heidelberg

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Hülse, M., Lara, B., Pasemann, F., Steinmetz, U. (2001). Evolving Neural Behaviour Control for Autonomous Robots. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_132

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  • DOI: https://doi.org/10.1007/3-540-44668-0_132

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

  • eBook Packages: Springer Book Archive

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