Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control / Juang, Chia-Feng in IEEE transactions on industrial electronics, Vol. 56 N° 10 (Octobre 2009)  

Public ISBD [article]  inIEEE transactions on industrial electronics > Vol. 56 N° 10 (Octobre 2009) . - pp. 3931 - 3940 Titre : Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control Type de document : texte imprimé Auteurs : Juang, Chia-Feng, Auteur ; Hsu, Chia-Hung, Auteur Article en page(s) : pp. 3931 - 3940 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Ant colony optimization (ACO) Fuzzy Q-learning Reinforced learning Robot motion control Type-2 fuzzy systems Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : This paper proposes a reinforcement ant optimized fuzzy controller (FC) design method, called RAOFC, and applies it to wheeled-mobile-robot wall-following control under reinforcement learning environments. The inputs to the designed FC are range-finding sonar sensors, and the controller output is a robot steering angle. The antecedent part in each fuzzy rule uses interval type-2 fuzzy sets in order to increase FC robustness. No a priori assignment of fuzzy rules is necessary in RAOFC. An online aligned interval type-2 fuzzy clustering (AIT2FC) method is proposed to generate rules automatically. The AIT2FC not only flexibly partitions the input space but also reduces the number of fuzzy sets in each input dimension, which improves controller interpretability. The consequent part of each fuzzy rule is designed using Q-value aided ant colony optimization (QACO). The QACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of whose values are updated using reinforcement signals. Simulations and experiments on mobile-robot wall-following control show the effectiveness and efficiency of the proposed RAOFC. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4804797 [article] Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control [texte imprimé] / Juang, Chia-Feng, Auteur ; Hsu, Chia-Hung, Auteur . - pp. 3931 - 3940. Génie électrique Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 56 N° 10 (Octobre 2009) . - pp. 3931 - 3940 Mots-clés : Ant colony optimization (ACO) Fuzzy Q-learning Reinforced learning Robot motion control Type-2 fuzzy systems Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : This paper proposes a reinforcement ant optimized fuzzy controller (FC) design method, called RAOFC, and applies it to wheeled-mobile-robot wall-following control under reinforcement learning environments. The inputs to the designed FC are range-finding sonar sensors, and the controller output is a robot steering angle. The antecedent part in each fuzzy rule uses interval type-2 fuzzy sets in order to increase FC robustness. No a priori assignment of fuzzy rules is necessary in RAOFC. An online aligned interval type-2 fuzzy clustering (AIT2FC) method is proposed to generate rules automatically. The AIT2FC not only flexibly partitions the input space but also reduces the number of fuzzy sets in each input dimension, which improves controller interpretability. The consequent part of each fuzzy rule is designed using Q-value aided ant colony optimization (QACO). The QACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of whose values are updated using reinforcement signals. Simulations and experiments on mobile-robot wall-following control show the effectiveness and efficiency of the proposed RAOFC. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4804797

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