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

Full metadata record

DC Field	Value	Language
dc.contributor.author	Anam, Khairul
dc.contributor.author	Prihastono, Prihastono
dc.contributor.author	Wicaksono, Handy
dc.contributor.author	Effendi, Rusdhianto
dc.contributor.author	Adji S, Indra
dc.contributor.author	Kuswadi, Son
dc.contributor.author	Jazidie, Achmad
dc.contributor.author	Sampei, Mitsuji
dc.date.accessioned	2017-10-11T07:55:25Z
dc.date.available	2017-10-11T07:55:25Z
dc.date.issued	2017-10-11
dc.identifier.isbn	978-979-8689-12-3
dc.identifier.uri	http://repository.unej.ac.id/handle/123456789/82066
dc.description	Proceeding of the 11th Industrial Electronics 2009	en_US
dc.description.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.	en_US
dc.language.iso	en	en_US
dc.subject	fuzzy q-learning	en_US
dc.subject	obstacle avoidance	en_US
dc.title	EMBEDDED LEARNING ROBOT USING FUZZY Q-LEARNING FOR OBSTACLE AVOIDANCE BEHAVIOR	en_US
dc.type	Prosiding	en_US
Appears in Collections:	LSP-Conference Proceeding

Files in This Item:

File	Description	Size	Format
F. T_Prosiding_Khairul Anam_Embedded_Learning_Robot_with_Fuzzy_Q-Lea.pdf		704.69 kB	Adobe PDF	View/Open

DSpace JSPUI