Abstract
In this paper we describe a fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. Takagi-Sugeno-Kang (TSK) fuzzy logic Is used to motivate a small mobile robot to acquire complex behaviors and to perform environment recognition. This method is implemented and tested in behavior based navigation and action sequence based environment recognition tasks in a Khepera mobile robot simulator. Our fuzzy logic based motivation technique Is shown as a simple and powerful method for a robot to acquire a diverse set of fit behaviors as well as providing an intuitive user Interface framework. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Arredondo, V. T., Freund, W., Muñoz, C., Navarro, N., & Quirós, F. (2006). Fuzzy motivations for evolutionary behavior learning by a mobile robot. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4031 LNAI, pp. 462–471). Springer Verlag. https://doi.org/10.1007/11779568_50
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