Integrated Localization and Tracking for AUV With Model Uncertainties via Scalable Sampling-Based Reinforcement Learning Approach

This article studies the joint localization and tracking issue for autonomous underwater vehicle (AUV), with constraints of asynchronous time clock in cyberchannels model uncertainty physical channels. More specifically, we develop a reinforcement learning (RL)-based algorithm to localize position AUV, where AUV is not required be well synchronized real time. Based on estimated position, scalable sampling strategy called multivariate probabilistic collocation method orthogonal fractional factorial design (M-PCM-OFFD) employed evaluate time-varying uncertain parameters AUV. After that, an RL-based controller designed drive desired target point. Besides performance analyses integration solution are also presented. Of note, advantages our highlighted as: 1) can avoid local optimal traditional least-square methods; 2) M-PCM-OFFD-based address reduce computational cost; 3) communication energy consumption. Finally, simulation experiment demonstrate that proposed effectively eliminate impact clock, more importantly, M-PCM-OFFD find accurate optimization solutions limited costs.