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 and 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) |
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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) |
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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) |
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Keyword(1) |
Deep learning |
Keyword(2) |
Link quality prediction |
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Autonomous mobility robot |
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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 |
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NTT (NTT) |
4th Author's Name |
Tomoaki Ogawa |
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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 |
6 |
Date of Issue |
2021-01-14 (IT, SIP, RCS) |
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