引用本文: 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.
YAN Shuxue, LI Yiping, FENG Xisheng. An AUV Adaptive Sampling Method Based on Gaussian Process Regression[J]. ROBOT, 2019, 41(2): 232-241.
 Citation: YAN Shuxue, LI Yiping, FENG Xisheng. An AUV Adaptive Sampling Method Based on Gaussian Process Regression[J]. ROBOT, 2019, 41(2): 232-241.

## An AUV Adaptive Sampling Method Based on Gaussian Process Regression

• 摘要: 针对区域海洋特征环境快速观测的需求，提出了一种基于高斯过程回归的小型自主水下机器人（AUV）自适应采样方法.首先，通过比较高斯过程回归（GPR）中使用不同的回归推理方法的估计准确度和计算效率，确定AUV的合适采样间隔时间；在此基础上，根据AUV实时观测的数据进行GPR分析，预测未观测区域环境数据，并通过计算预测区域梯度极值和预测不确定度引导AUV进行在线路径规划；最后使用该方法，对具有不同特征分布的区域环境观测过程进行仿真.结果显示，本方法与常规方法相比，能够更高效地获得观测区域的低误差特征分布估计，更快地获得观测区域热点区特征，更好地适应观测区域特征分布不同的情况.

Abstract: For 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.

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