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Paper Abstract and Keywords
Presentation 2021-01-22 12:30
Deep Learning based Link Quality Prediction for Autonomous Mobility Robots
Riichi Kudo, Kahoko Takahashi, Tomoki Murakami, Tomoaki Ogawa (NTT) IT2020-102 SIP2020-80 RCS2020-193
Abstract (in Japanese) (See Japanese page) 
(in English) Highly advanced mobility robots are expected to be managed, monitored, or efficiently controlled by using wireless communication links. The wireless links will need to satisfy higher level requirements if they are to realize more advanced applications in future robot systems. In the mobity robot systems, the devices accurately understands self-status such as position, direction, and velocity so as to safely operate without colliding with other objects. Accurate self-status is useful not only for robot operations but also enhancing wireless link performance. This paper proposes deep-learning-based wireless link quality prediction that uses robot status and evaluates the prediction performance of the future link quality by using an implemented autonomous mobility robot in an indoor environment.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep learning / Link quality prediction / Autonomous mobility robot / wireless LAN / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 321, SIP2020-80, pp. 218-223, Jan. 2021.
Paper # SIP2020-80 
Date of Issue 2021-01-14 (IT, SIP, RCS) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IT2020-102 SIP2020-80 RCS2020-193

Conference Information
Committee SIP IT RCS  
Conference Date 2021-01-21 - 2021-01-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2021-01-SIP-IT-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Learning based Link Quality Prediction for Autonomous Mobility Robots 
Sub Title (in English)  
Keyword(1) Deep learning  
Keyword(2) Link quality prediction  
Keyword(3) Autonomous mobility robot  
Keyword(4) wireless LAN  
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1st Author's Name Riichi Kudo  
1st Author's Affiliation NTT (NTT)
2nd Author's Name Kahoko Takahashi  
2nd Author's Affiliation NTT (NTT)
3rd Author's Name Tomoki Murakami  
3rd Author's Affiliation NTT (NTT)
4th Author's Name Tomoaki Ogawa  
4th Author's Affiliation NTT (NTT)
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Speaker Author-1 
Date Time 2021-01-22 12:30:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # IT2020-102, SIP2020-80, RCS2020-193 
Volume (vol) vol.120 
Number (no) no.320(IT), no.321(SIP), no.322(RCS) 
Page pp.218-223 
#Pages
Date of Issue 2021-01-14 (IT, SIP, RCS) 


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