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Alternative TitleAn AUV Adaptive Sampling Method Based on Gaussian Process Regression
阎述学1,2; 李一平1; 封锡盛1
Source Publication机器人
Indexed ByEI ; CSCD
EI Accession number20192206992147
Contribution Rank1
Funding Organization国家重点研发计划(2017YFC0305901) ; 国家自然科学基金(91648204)
Keyword高斯过程回归 自主水下机器人 自适应采样 在线路径规划 热点区域观测
Other AbstractFor the rapid observation problem in coastal marine environment, an adaptive sampling method based on Gaussian process regression (GPR) for small autonomous underwater vehicle (AUV) is proposed. Firstly, the estimation accuracies and the computational efficiencies are compared among different regression inference methods in GPR, and the sampling interval time is determined. On this basis, GPR analysis is used to predict the environmental data of unobserved areas based on the real-time observation data from AUV, and the AUV is guided to implement online path planning by calculating the regional gradient extremum and the forecasting uncertainty. Finally, this method is used to simulate the regional environmental observation with different feature distributions. Results show that this method can estimate the low-error feature distribution of the observed area more efficiently than the conventional method, can obtain features of the hot spot area more quickly in the observed area, and is more adaptable to the observed area with different feature distributions.
Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Corresponding Author阎述学
Recommended Citation
GB/T 7714
阎述学,李一平,封锡盛. 一种基于高斯过程回归的AUV自适应采样方法[J]. 机器人,2019,41(2):232-241.
APA 阎述学,李一平,&封锡盛.(2019).一种基于高斯过程回归的AUV自适应采样方法.机器人,41(2),232-241.
MLA 阎述学,et al."一种基于高斯过程回归的AUV自适应采样方法".机器人 41.2(2019):232-241.
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