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Real-Time Adaptive Fuzzy Motivations for Evolutionary Behavior Learning by a Mobile Robot

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MICAI 2006: Advances in Artificial Intelligence (MICAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4293))

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Abstract

In this paper we investigate real-time adaptive extensions of our fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. The main idea is to introduce active battery level sensors and recharge zones to improve robot behavior for reaching survivability in environment exploration. In order to achieve this goal, we propose an improvement of our previously defined model, as well as a hybrid controller for a mobile robot, combining behavior-based and mission-oriented control mechanism. This method is implemented and tested in action sequence based environment exploration tasks in a Khepera mobile robot simulator. We investigate our technique with several sets of configuration parameters and scenarios. The experiments show a significant improvement in robot responsiveness regarding survivability and environment exploration.

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Authors and Affiliations

  1. Departamento de Electrónica, Universidad Técnica Federico Santa María, Casilla 110 V, Valparaíso, Chile

    Wolfgang Freund, Tomas Arredondo Vidal, César Muñoz, Nicolás Navarro & Fernando Quirós

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  1. Wolfgang Freund
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  2. Tomas Arredondo Vidal
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  3. César Muñoz
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  4. Nicolás Navarro
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  5. Fernando Quirós
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Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, 07738, Mexico City, México

    Alexander Gelbukh

  2. Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro No. 1, Sta. Ma. Tonanzintla, 72840, Puebla, México

    Carlos Alberto Reyes-Garcia

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Freund, W., Vidal, T.A., Muñoz, C., Navarro, N., Quirós, F. (2006). Real-Time Adaptive Fuzzy Motivations for Evolutionary Behavior Learning by a Mobile Robot. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_10

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  • DOI: https://doi.org/10.1007/11925231_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49026-5

  • Online ISBN: 978-3-540-49058-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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