Fuzzy Genetic Network Programming with Reinforcement Learning for Mobile Robot Navigation

被引：0
|
作者：
Sendari, Siti [1 ]
Mabu, Shingo [1 ]
Hirasawa, Kotaro [1 ]
机构：
[1] Waseda Univ, Grad Sch Informat Prod & Syst, Wakamatsu Ku, Kitakyushu, Fukuoka 8080135, Japan
来源：
2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2011年
关键词：
Fuzzy logic; Genetic Network Programming; Reinforcement Learning; Robustness; Wall following behavior; ALGORITHM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes Fuzzy Genetic Network Programming with Reinforcement Learning (Fuzzy GNP-RL). This method integrates fuzzy logic to the conventional GNP-RL. The new part of the proposed method is fuzzy judgment nodes. Fuzzy GNP-RL provides flexibility to determine the appropriate next node by the probabilistic transition instead of that by the threshold values on GNP-RL. The simulation of the wall following behavior of a Khepera robot is used to evaluate the performance of Fuzzy GNP-RL compared with that of GNP-RL. The result shows that Fuzzy GNP-RL is more robust than GNP-RL.
引用
收藏
页码：2243 / 2248
页数：6
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