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Fuzzy obstacle avoidance optimization of soccer robot based on an improved genetic algorithm

Fuzzy obstacle avoidance optimization of soccer robot based on an improved genetic algorithm Due to its high-tech confrontation and entertainment, soccer robots have attracted a large number of researchers to participate in it. Robot obstacle avoidance is an active research branch in the field of intelligent robot technology, and is a comprehensive subject covering multiple disciplines. The traditional obstacle avoidance method of soccer robots has the disadvantages of poor local optimization ability and slow convergence speed, and it is easy to fall into local extreme points. Based on the active obstacle avoidance strategy of local sensors and processors, this paper studies the method of automatic extraction and optimization of fuzzy rules of fuzzy path planner by genetic algorithm. This fuzzy control method does not depend on accurate environmental information, and has a small amount of computation and learning ability. A fitness function for evaluating the validity of a rule is proposed. The simulation results show that the algorithm has good applicability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Ambient Intelligence and Humanized Computing Springer Journals

Fuzzy obstacle avoidance optimization of soccer robot based on an improved genetic algorithm

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References (30)

Publisher
Springer Journals
Copyright
Copyright © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Subject
Engineering; Computational Intelligence; Artificial Intelligence; Robotics and Automation; User Interfaces and Human Computer Interaction
ISSN
1868-5137
eISSN
1868-5145
DOI
10.1007/s12652-019-01636-0
Publisher site
See Article on Publisher Site

Abstract

Due to its high-tech confrontation and entertainment, soccer robots have attracted a large number of researchers to participate in it. Robot obstacle avoidance is an active research branch in the field of intelligent robot technology, and is a comprehensive subject covering multiple disciplines. The traditional obstacle avoidance method of soccer robots has the disadvantages of poor local optimization ability and slow convergence speed, and it is easy to fall into local extreme points. Based on the active obstacle avoidance strategy of local sensors and processors, this paper studies the method of automatic extraction and optimization of fuzzy rules of fuzzy path planner by genetic algorithm. This fuzzy control method does not depend on accurate environmental information, and has a small amount of computation and learning ability. A fitness function for evaluating the validity of a rule is proposed. The simulation results show that the algorithm has good applicability.

Journal

Journal of Ambient Intelligence and Humanized ComputingSpringer Journals

Published: Dec 12, 2019

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