Fuzzy behavior-based control trained by module learning to acquire the adaptive behaviors of mobile robots

Kiyotaka Izumi and Keigo Watanabe

Mathematics and Computers in Simulation (MATCOM), 2000, vol. 51, issue 3, 233-243

Abstract: Intelligent control techniques for robotic systems have been used with some success in a wide variety of applications. In this paper, we construct a method for the intelligent control system of a robot using the fuzzy behavior-based control, which decomposes the control system into several elemental behaviors, and each one is realized by fuzzy reasoning. In particular, a module learning method is investigated for obtaining each representative group behavior, so that the robot can, consequently, acquire more general knowledge or fuzzy reasoning, than a central learning method. The proposed method is applied for an obstacle-avoidance problem of a mobile robot; the effectiveness of the method is illustrated through some simulations.

Keywords: Behavior-based control; Module learning; Subsumption architecture; Genetic algorithm; Fuzzy set theory; Mobile robot (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:51:y:2000:i:3:p:233-243

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