Fuzzy genetic Network Programming with Reinforcement Learning for mobile robot navigation

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.