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

Kiyotaka Izumi, Keigo Watanabe

Research output: Contribution to journalArticle

16 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)233-243
Number of pages11
JournalMathematics and Computers in Simulation
Volume51
Issue number3-4
DOIs
Publication statusPublished - Jan 2000
Externally publishedYes

Keywords

  • Behavior-based control
  • Fuzzy set theory
  • Genetic algorithm
  • Mobile robot
  • Module learning
  • Subsumption architecture

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)
  • Numerical Analysis
  • Modelling and Simulation
  • Applied Mathematics

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