Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
A fuzzy temporal rule-based velocity controller for mobile robotics
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
A genetic-fuzzy approach for mobile robot navigation among moving obstacles
International Journal of Approximate Reasoning
Fuzzy temporal rules for mobile robot guidance in dynamicenvironments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy logic techniques for navigation of several mobile robots
Applied Soft Computing
A case study for learning behaviors in mobile robotics by evolutionary fuzzy systems
Expert Systems with Applications: An International Journal
Genetic based fuzzy logic controller for a wall-following mobile robot
ACC'09 Proceedings of the 2009 conference on American Control Conference
Hybrid intelligent systems applied to the pursuit-evasion game
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Computational Optimization and Applications
Implementation and integration of algorithms into the KEEL data-mining software tool
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Automatic lateral control for unmanned vehicles via genetic algorithms
Applied Soft Computing
Self-learning fuzzy logic controllers for pursuit-evasion differential games
Robotics and Autonomous Systems
Automatic rule tuning of a fuzzy logic controller using particle swarm optimisation
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Information Sciences: an International Journal
Double chains quantum genetic algorithm with application to neuro-fuzzy controller design
Advances in Engineering Software
Artificial bee colony algorithm and pattern search hybridized for global optimization
Applied Soft Computing
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The design of fuzzy controllers for the implementation of behaviors in mobile robotics is a complex and highly time-consuming task. The use of machine learning techniques, such as evolutionary algorithms or artificial neural networks for the learning of these controllers allows to automate the design process. In this paper, the automated design of a fuzzy controller using genetic algorithms for the implementation of the wall-following behavior in a mobile robot is described. The algorithm is based on the Iterative Rule Learning (IRL) approach, and a parameter (@d) is defined with the aim of selecting the relation between the number of rules and the quality and accuracy of the controller. The designer has to define the universe of discourse and the precision of each variable, and also the scoring function. No restrictions are placed neither in the number of linguistic labels nor in the values that define the membership functions.