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Paper Abstract and Keywords
Presentation 2024-03-14 10:30
Informative Path Planning in Dynamic Environments Using Spatio-Temporal Gaussian Process Regression
Gao Tian, Techasarntikul Nattaon, Ohsita Yuichi, Shimonishi Hideyuki (Osaka Univ.) CQ2023-80
Abstract (in Japanese) (See Japanese page) 
(in English) With the rapid advancement of automation technology, the role of autonomous robots in environmental monitoring and exploration in dynamic environments has become crucial. To address this challenge, this paper proposes a Continuous U-prediction method, an uncertainty-driven path planning based on Spatio-Temporal Gaussian Process Regression (ST-GPR). This method improves the certainty of collected environmental information by optimizing path planning, which effectively responds to frequent changes in environmental conditions. Unlike traditional approaches, our U-prediction method uses uncertainty within the ST-GPR model as a guide, demonstrating its efficacy in a 10$times$10 obstacle-free 2D simulated environment. Compared to the local Next-Best-View and random path methods, U-prediction significantly reduces the uncertainty of environmental information and improves the efficiency of environmental monitoring.
Keyword (in Japanese) (See Japanese page) 
(in English) Autonomous Robots / Path Planning / Gaussian Process Regression / Spatio-Temporal Model / Environmental Monitoring / Uncertainty-Driven / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 431, CQ2023-80, pp. 56-61, March 2024.
Paper # CQ2023-80 
Date of Issue 2024-03-06 (CQ) 
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)
Download PDF CQ2023-80

Conference Information
Committee IE MVE CQ IMQ  
Conference Date 2024-03-13 - 2024-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Sangyo Shien Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc（AC） 
Paper Information
Registration To CQ 
Conference Code 2024-03-IE-MVE-CQ-IMQ 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Informative Path Planning in Dynamic Environments Using Spatio-Temporal Gaussian Process Regression 
Sub Title (in English)  
Keyword(1) Autonomous Robots  
Keyword(2) Path Planning  
Keyword(3) Gaussian Process Regression  
Keyword(4) Spatio-Temporal Model  
Keyword(5) Environmental Monitoring  
Keyword(6) Uncertainty-Driven  
Keyword(7)  
Keyword(8)  
1st Author's Name Gao Tian  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Techasarntikul Nattaon  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Ohsita Yuichi  
3rd Author's Affiliation Osaka University (Osaka Univ.)
4th Author's Name Shimonishi Hideyuki  
4th Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2024-03-14 10:30:00 
Presentation Time 20 minutes 
Registration for CQ 
Paper # CQ2023-80 
Volume (vol) vol.123 
Number (no) no.431 
Page pp.56-61 
#Pages 6 
Date of Issue 2024-03-06 (CQ) 

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