Fuzzy genetic Network Programming with Reinforcement Learning for mobile robot navigation

Author

Sendari, Siti ; Mabu, Shingo ; Hirasawa, Kotaro

Author_Institution

Grad. Sch. of Inf., Production & Syst., Waseda Univ., Kitakyushu, Japan

fYear

2011

fDate

9-12 Oct. 2011

Firstpage

2243

Lastpage

2248

Abstract

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.

Keywords

fuzzy logic; genetic algorithms; learning (artificial intelligence); mobile robots; path planning; probability; robust control; Khepera robot; fuzzy GNP-RL; fuzzy genetic network programming; fuzzy judgment node; fuzzy logic; mobile robot navigation; probabilistic transition; reinforcement learning; threshold value; Economic indicators; Learning; Mobile robots; Robot sensing systems; Training; Wheels; Fuzzy logic; Genetic Network Programming; Reinforcement Learning; Robustness; Wall following behavior;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1062-922X

Print_ISBN

978-1-4577-0652-3

Type

conf

DOI

10.1109/ICSMC.2011.6084011

Filename

6084011