Learning Uncertainty in Ocean Current Predictions for Safe and Reliable Navigation of Underwater Vehicles
Learning Uncertainty in Ocean Current Predictions for Safe and Reliable Navigation of Underwater Vehicles
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学习海流预测的不确定性以实现水下航行器的安全可靠导航
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
G. Sukhatme
中科院分区：
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作者：
Geoffrey A. Hollinger;A. Pereira;J. Binney;Thane Somers;G. Sukhatme
在近海操作自主水下航行器（AUV）是具有挑战性的——繁重的航运交通和其他危险威胁着AUV在水面上的安全，而强大的洋流阻碍了水下航行。洋流预测模型已被证明可以提高导航精度，但这些预测通常是嘈杂的，因此很难有效地使用它们。先前的工作已经探索了概率规划器的使用，如马尔可夫决策过程（mdp），用于这些情景的规划，但先前的方法缺乏对海洋模式预测中的不确定性建模的原则方法，这限制了对高保真模型可用情况的适用性。为了克服这一限制，我们建议使用高斯过程（GPs）增强插值方差来提供预测的置信度度量。本文描述了两种包含这些置信度度量的新型规划器：(1)平稳的风险感知GPMDP（用于低变率电流），(2)非平稳的风险感知NS - GPMDP（用于更快和高变率电流）。大量的仿真表明，所学习的置信度措施可以在不确定的海流模型下安全可靠地运行。在海洋中对Slocum滑翔机进行了几周的实地测试，证明了我们的方法的实际有效性。
Operating autonomous underwater vehicles (AUVs) near shore is challenging—heavy shipping traffic and other hazards threaten AUV safety at the surface, and strong ocean currents impede navigation when underwater. Predictive models of ocean currents have been shown to improve navigation accuracy, but these forecasts are typically noisy, making it challenging to use them effectively. Prior work has explored the use of probabilistic planners, such as Markov decision processes (MDPs), for planning in these scenarios, but prior methods have lacked a principled way of modeling the uncertainty in ocean model predictions, which limits applicability to cases in which high fidelity models are available. To overcome this limitation, we propose using Gaussian processes (GPs) augmented with interpolation variance to provide confidence measures on predictions. This paper describes two novel planners that incorporate these confidence measures: (1) a stationary risk‐aware GPMDP (for low‐variability currents), and (2) a nonstationary risk‐aware NS‐GPMDP (for faster and high‐variability currents). Extensive simulations indicate that the learned confidence measures allow for safe and reliable operation with uncertain ocean current models. Field tests of the planners on Slocum gliders over several weeks in the ocean demonstrate the practical efficacy of our approach.