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

Please use this identifier to cite or link to this item: https://repository.unej.ac.id/xmlui/handle/123456789/82066

Title:	EMBEDDED LEARNING ROBOT USING FUZZY Q-LEARNING FOR OBSTACLE AVOIDANCE BEHAVIOR
Authors:	Anam, Khairul Prihastono, Prihastono Wicaksono, Handy Effendi, Rusdhianto Adji S, Indra Kuswadi, Son Jazidie, Achmad Sampei, Mitsuji
Keywords:	fuzzy q-learning obstacle avoidance
Issue Date:	11-Oct-2017
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.
Description:	Proceeding of the 11th Industrial Electronics 2009
URI:	http://repository.unej.ac.id/handle/123456789/82066
ISBN:	978-979-8689-12-3
Appears in Collections:	LSP-Conference Proceeding

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