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Fuzzy genetic Network Programming with Reinforcement Learning for mobile robot navigation

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3 Author(s)
Sendari, S. ; Grad. Sch. of Inf., Production & Syst., Waseda Univ., Kitakyushu, Japan ; Mabu, S. ; Hirasawa, K.

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.

Published in:

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

Date of Conference:

9-12 Oct. 2011

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