Fuzzy behavior-based control trained by module learning to acquire the adaptive behaviors of mobile robots
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.
Year of publication: |
2000
|
---|---|
Authors: | Izumi, Kiyotaka ; Watanabe, Keigo |
Published in: |
Mathematics and Computers in Simulation (MATCOM). - Elsevier, ISSN 0378-4754. - Vol. 51.2000, 3, p. 233-243
|
Publisher: |
Elsevier |
Subject: | Behavior-based control | Module learning | Subsumption architecture | Genetic algorithm | Fuzzy set theory | Mobile robot |
Saved in:
Online Resource
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