An AUV Adaptive Front-Tracking Algorithm Based on Data-Driven

Abstract

For the dynamic observation of oceanic fronts, a data-driven adaptive front-tracking algorithm for autonomous underwater vehicle (AUV) is proposed based on the model prediction of the ambient temperature data obtained by online sampling. Firstly, a dynamic model of front temperature is established by analyzing the temperature characteristics of fronts and water masses on both sides. Secondly, Gauss process regression (GPR) is used to process the real-time AUV observation data and predicts the current location environment model. Finally, an improved gradient search algorithm is used to plan the sampling path. The simulation results show that the proposed method can achieve continuous tracking down the front. By comparing with other front tracking algorithms, the proposed method can effectively track complex fronts, and acquisition of front area data is more efficient.