EMBEDDED LEARNING ROBOT USING FUZZY Q-LEARNING FOR OBSTACLE AVOIDANCE BEHAVIOR

Abstract

Fuzzy Q-learning is extending of Q-learning algorithm that uses fuzzy inference system to enable Qlearning holding continuous action and state. This learning has been implemented in various robot learning

application like obstacle avoidance and target searching. However, most of them have not been realized in

embedded robot. This paper presents implementation of fuzzy Q-learning for obstacle avoidance navigation in

embedded mobile robot. The experimental result demonstrates that fuzzy Q-learning enables robot to be able to

learn the right policy i.e. to avoid obstacle.