There has been significant interest in recent years in the utility and implementation of autonomous underwater and surface vehicles (AUVs and ASVs) for persistent surveillance of the ocean. Example studies include the dynamics of physical phenomena, e.g., ocean fronts, temperature and salinity profiles, and the onset of harmful algae blooms. For these studies, AUVs are presented with a complex planning and navigation problem to achieve autonomy lasting days and weeks under uncertainties while dealing with resource constraints. We address these issues by adopting motion, sensing, and environment uncertainties via a Partially Observable Markov Decision Process (POMDP) framework. We propose a methodology with a novel extension of POMDPs to incorporate spatiotemporally-varying ocean currents as energy and dynamic obstacles as environment uncertainty. Existing POMDP solutions such as the Cost-Constrained Partially Observable Monte-Carlo Planner (POMCP) do not account for energy efficiency. Therefore, we present a scalable Energy Cost-Constrained POMCP algorithm utilizing the predicted ocean dynamics that optimizes energy and environment costs along with goal-driven rewards. A theoretical analysis, along with simulation and real-world experiment results is presented to validate the proposed methodology.

中文翻译：

---

在动态海洋环境的不确定性下运行的 AUV 的长期自主性


近年来，人们对用于持续监视海洋的自主水下和水面航行器（AUV 和 ASV）的实用和实施产生了浓厚的兴趣。示例研究包括物理现象的动态，例如海洋前沿、温度和盐度分布以及有害藻类大量繁殖的发生。在这些研究中，AUV 面临着复杂的规划和导航问题，以在不确定的情况下实现持续数天或数周的自主性，同时应对资源限制。我们通过部分可观察马尔可夫决策过程 (POMDP) 框架采用运动、传感和环境不确定性来解决这些问题。我们提出了一种对 POMDP 进行新颖扩展的方法，将时空变化的洋流作为能量，将动态障碍作为环境不确定性。现有的 POMDP 解决方案（例如成本受限的部分可观测蒙特卡罗规划器 (POMCP)）并未考虑能源效率。因此，我们提出了一种可扩展的能源成本受限 POMCP 算法，利用预测的海洋动力学来优化能源和环境成本以及目标驱动的奖励。提出了理论分析以及模拟和现实实验结果来验证所提出的方法。