An AUV Adaptive Sampling Method Based on Gaussian Process Regression

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.