Persistent monitoring of a spatiotemporal fluid process requires data sampling and predictive modeling of the process being monitored. In this paper we present PASST algorithm: Predictive-model based Adaptive Sampling of a Spatio-Temporal process. PASST is an adaptive robotic sampling algorithm that leverages predictive models to efficiently and persistently monitor a fluid process in a given region of interest. Our algorithm makes use of the predictions from a learned prediction model to plan a path for an autonomous vehicle to adaptively and efficiently survey the region of interest. In turn, the sampled data is used to obtain better predictions by giving an updated initial state to the predictive model. For predictive model, we use Knowledged-based Neural Ordinary Differential Equations to train models of fluid processes. These models are orders of magnitude smaller in size and run much faster than fluid data obtained from direct numerical simulations of the partial differential equations that describe the fluid processes or other comparable computational fluids models. For path planning, we use reinforcement learning based planning algorithms that use the field predictions as reward functions. We evaluate our adaptive sampling path planning algorithm on both numerically simulated fluid data and real-world nowcast ocean flow data to show that we can sample the spatiotemporal field in the given region of interest for long time horizons. We also evaluate PASST algorithm's generalization ability to sample from fluid processes that are not in the training repertoire of the learned models.

翻译：持续监测时空流体过程需要对被监测过程进行数据采样与预测建模。本文提出PASST算法：基于预测模型的时空过程自适应采样。PASST是一种自适应机器人采样算法，通过利用预测模型在给定感兴趣区域内高效且持续地监测流体过程。该算法借助已学习预测模型生成的预测结果，规划自主载具的路径，以自适应且高效地巡查感兴趣区域。与此同时，采样数据通过为预测模型提供更新后的初始状态，进而获得更优的预测结果。在预测模型方面，我们采用基于知识的神经常微分方程训练流体过程模型。这些模型的规模较之通过直接数值求解描述流体过程的偏微分方程或其它可比拟的计算流体模型所获得的流体数据小数个数量级，且运行速度显著更快。在路径规划方面，我们采用基于强化学习的规划算法，将场预测结果作为奖励函数。基于数值模拟的流体数据与真实世界的海洋流场临近预报数据，我们对所提自适应采样路径规划算法进行了评估，结果表明该算法能够在长时间范围内对给定感兴趣区域的时空场进行有效采样。此外，我们还评估了PASST算法对训练模型知识库中未涵盖的流体过程进行采样的泛化能力。