Abstract
This paper describes an evolutionary way to lean behaviors of a mobile robot for recognizing environments. We have proposed AEM (Action-based Environment Modeling) which is an appropriate approach for a simple mobile robot to recognize environments, and made experiments using a real robot. The suitable behaviors for AEM have been described by a human designer. However the design is very difficult for them because of the huge search space. Thus we propose the evolutionary design method of such behaviors using genetic algorithm and make experiments in which a robot recognizes the environments with different structures. As results, we found out that the evolutionary approach is promising to automatically acquire behaviors for AEM.
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© 1998 Springer-Verlag Berlin Heidelberg
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Yamada, S. (1998). Learning behaviors for environmental modeling by genetic algorithm. In: Husbands, P., Meyer, JA. (eds) Evolutionary Robotics. EvoRobots 1998. Lecture Notes in Computer Science, vol 1468. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64957-3_72
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DOI: https://doi.org/10.1007/3-540-64957-3_72
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