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An AUV Adaptive Sampling Path Planning Method Based On Online Model Prediction
Yan SX(阎述学)1,2; Li YP(李一平)1; Feng XS(封锡盛)1; Li S(李硕)1; Tang YG(唐元贵)1; Li ZG(李智刚)1; Yuan MZ(苑明哲)2
Department水下机器人研究室
Conference Name12th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles (CAMS)
Conference DateSeptember 18-20, 2019
Conference PlaceDaejeon, KOREA
Source Publication12th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles (CAMS)
PublisherELSEVIER
Publication PlaceAMSTERDAM
2019
Pages323-328
Indexed ByEI ; CPCI(ISTP)
EI Accession number20200808207044
WOS IDWOS:000504414000055
Contribution Rank1
ISSN2405-8963
Keywordadaptive sampling Gaussian Process Regression AUV online path planning hot spot area observation
AbstractAiming at the problem of rapid observation of coastal marine environment, an adaptive sampling method based on Gaussian Process Regression (GPR) for small Autonomous Underwater Vehicle (AUV) is proposed. GPR analysis is used to predict the environmental data of unobserved areas based on the real-time observation data from the AUV, and the AUV is guided to implement online path planning by calculating the regional gradient extremes and the forecasting uncertainty. Based on this, an AUV observation direction selection method based on the global estimation of the boundary gravity matrix after data exchange is designed. Finally, this method is used to simulate the regional environmental observation with different feature distributions. Results show that this method can obtain the estimation of low-error feature distribution of the observed area more efficiently than the conventional method, and obtain the hot spot monitor of the observed area more quickly and show more adaptable of the different regional characteristics observation. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/26175
Collection水下机器人研究室
Corresponding AuthorYan SX(阎述学)
Affiliation1.State Key Laboratory of Robotics of Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Shenyang Institute of Automation, Guangzhou, Chinese Academy of Sciences, Guangzhou 511458, China
Recommended Citation
GB/T 7714
Yan SX,Li YP,Feng XS,et al. An AUV Adaptive Sampling Path Planning Method Based On Online Model Prediction[C]. AMSTERDAM:ELSEVIER,2019:323-328.
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