Front delineation and tracking with multiple underwater vehicles

This study describes a method for detecting and tracking ocean fronts using multiple autonomous underwater vehicles (AUVs). Multiple vehicles, equally spaced along the expected frontal boundary, complete near parallel transects orthogonal to the front. Two different techniques are used to determine the location of the front crossing from each individual vehicle transect. The first technique uses lateral gradients to detect when a change in the observed water property occurs. The second technique uses a measure of the vertical temperature structure over a single dive to detect when the vehicle is in upwelling water. Adaptive control of the vehicles ensure they remain perpendicular to the estimated front boundary as it evolves over time. This method was demonstrated in several experiment periods totaling weeks, in and around Monterey Bay, CA, in May and June of 2017. We compare the two front detection methods, a lateral gradient front detector and an upwelling front detector using the Vertical Temperature Homogeneity Index. We introduce two metrics to evaluate the adaptive control techniques presented. We show the capability of this method for repeated sampling across a dynamic ocean front using a fleet of three types of platforms: short‐range Iver AUVs, Tethys‐class long‐range AUVs, and Seagliders. This method extends to tracking gradients of different properties using a variety of vehicles.     

能耗最优的水下滑翔机采样路径规划

研究了以能耗为优化准则的水下滑翔机海洋环境参数采样路径规划方法.首先,根据水下滑翔机的运动特点,建立了水下滑翔机采样作业过程的能耗模型;其次,基于能耗模型提出了一种能耗最小的滑翔运动参数优化方法,该方法避免了求解复杂的混合整数非线性规划问题;之后,在能耗最优的滑翔运动参数优化基础上,提出了一种基于两步链式Lin-Ker...     

A Survey of Ocean Simulation and Rendering Techniques in Computer Graphics

This paper presents a survey of ocean simulation and rendering methods in computer graphics. To model and animate the ocean’s surface, these methods mainly rely on two main approaches: on the one hand, those which approximate ocean dynamics with parametric, spectral or hybrid models and use empirical laws from oceanographic research. We will see that this type of methods essentially allows the simulation of ocean scenes in the deep water domain, without breaking waves. On the other hand, physically‐based methods use Navier–Stokes equations to represent breaking waves and more generally ocean surface near the shore. We also describe ocean rendering methods in computer graphics, with a special interest in the simulation of phenomena such as foam and spray, and light’s interaction with the ocean surface.     

A Fully Implicit Parallel Ocean Model Using MUMPS

The formulation, implementation and performance of a new fully implicit parallel model of the ocean circulation is presented. Within this model, steady states can be traced in one of the control parameters. In addition, transient flows can be computed using relatively (compared to traditional ocean models) large time steps such that long integration times can be reached. The discretized equations of the ocean model are solved by the Newton-Raphson technique and the emerging linear systems are solved by a (MPI) version of the MUltifrontal Massively Parallel Solver. The performance of the code on an SGI Origin 2000 platform is presented here using typical results for a sector ocean flow.     

EM,MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence

Learning spatial models from sensor data raises the challenging data association problem of relating model parameters to individual measurements. This paper proposes an EM-based algorithm, which solves the model learning and the data association problem in parallel. The algorithm is developed in the context of the the structure from motion problem, which is the problem of estimating a 3D scene model from a collection of image data. To accommodate the spatial constraints in this domain, we compute virtual measurements as sufficient statistics to be used in the M-step. We develop an efficient Markov chain Monte Carlo sampling method called chain flipping, to calculate these statistics in the E-step. Experimental results show that we can solve hard data association problems when learning models of 3D scenes, and that we can do so efficiently. We conjecture that this approach can be applied to a broad range of model learning problems from sensordata, such as the robot mapping problem.     

Data‐driven learning and planning for environmental sampling

Robots such as autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) have been used for sensing and monitoring aquatic environments such as oceans and lakes. Environmental sampling is a challenging task because the environmental attributes to be observed can vary both spatially and temporally, and the target environment is usually a large and continuous domain whereas the sampling data are typically sparse and limited. The challenges require that the sampling method must be informative and efficient enough to catch up with the environmental dynamics. In this paper, we present a planning and learning method that enables a sampling robot to perform persistent monitoring tasks by learning and refining a dynamic “data map” that models a spatiotemporal environment attribute such as ocean salinity content. Our environmental sampling framework consists of two components: To maximize the information collected, we propose an informative planning component that efficiently generates sampling waypoints that contain the maximal information; to alleviate the computational bottleneck caused by large‐scale data accumulated, we develop a component based on a sparse Gaussian process whose hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. We validate our method with both simulations running on real ocean data and field trials with an ASV in a lake environment. Our experiments show that the proposed framework is both accurate in learning the environmental data map and efficient in catching up with the dynamic environmental changes.     

An accurate sampling-based method for approximating geometry

We present a sampling-based method for approximating the boundary of a geometry defined by various geometric operations. Based on a novel adaptive sampling condition, we first construct volumetric grids such that an error-minimizing point can be found in each cell to capture all the geometric objects inside the cell. We then construct a polygonal model from the grid. We guarantee the boundary approximation has the same topology as the exact surfaces, and the maximum approximation error from the exact surfaces is bounded by a user specified tolerance. Our method is robust and easy to implement. We have applied it in various applications such as remeshing of polygonal models, Boolean operations, and offsetting operations. We report experimental results on a variety of CAD models.     

改进Mask R-CNN模型的海洋锋检测

目的 海洋锋的高效检测对海洋生态环境变化、渔业资源评估、渔情预报及台风路径预测等具有重要意义。海洋锋具有边界信息不明显且多变的弱边缘性,传统基于梯度阈值法及边缘检测的海洋锋检测方法,存在阈值选择不固定、判定指标不一致导致检测精度较低的问题。针对上述问题,基于Mask R-CNN（region convolutional neural network）提出一种改进的海洋锋自动检测方法。方法 兼顾考虑海洋锋的小数据量及弱边缘性,首先对数据扩增,并基于不同算法对海表温度（sea surface temperatures,SST）遥感影像进行增强;其次,基于迁移学习的思想采用COCO（common objects in context）数据集对网络模型进行初始化;同时,对Mask R-CNN中残差神经网络（residual neural network,ResNet）和特征金字塔模型（feature pyramid network,FPN）分别进行改进,在充分利用低层特征高分辨率和高层特征的高语义信息的基础上,对多个尺度的融合特征图分别进行目标预测,提升海洋锋的检测精度。结果 为验证本文方法的有效性,从训练数据和实验模型上分别设计多组对比实验。实验结果表明,相比常用的Mask R-CNN和YOLOv3（you only look once）神经网络,本文方法对SST梯度影像数据集上的海洋锋检测效果最好,海洋锋的定位准确率（intersection over union,IoU）及检测平均精度均值（mean average precision,mAP）达0.85以上。此外,通过对比分析实验结果发现,本文方法对强海洋锋的检测效果明显优于弱海洋锋。结论 本文根据专家经验设立合理的海洋锋检测标准,更好地考虑了海洋锋的弱边缘性。通过设计多组对比实验,验证了本文方法对海洋锋的高精度检测效果。     

基于FFT的海浪实时仿真方法

针对虚拟海战场仿真需求,提出一种真实感海浪的实时仿真方法。在改进Gerstner波形的基础上,构建海浪的基本模型,推导出海浪的离散FFT模型,并通过采样波数谱提取FFT模型所需参数。仿真结果表明,该方法用于大面积海浪的实时仿真时,能获得高度逼真的动画效果。     

Persistent ocean monitoring with underwater gliders: Adapting sampling resolution

Ocean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high‐value data. More specifically, this paper proposes a path planning algorithm and a speed control algorithm for underwater gliders, which together give informative trajectories for the glider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long‐term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California, as well as in Monterey Bay, California. The experimental results show improvements in both data resolution and path reliability compared to previously executed sampling paths used in the respective regions. © 2011 Wiley Periodicals, Inc.     

基于海浪谱的Gerstner波浪模拟

本文从海洋学现有的观测和研究成果出发,结合海浪的Gerstner模型,提出一种风力作用下的基于海浪谱的三维Gerstner海浪模型。首先,通过分析Gerstner模型各个参数的随机性,利用海浪谱和方向谱相关公式,获得固定风速下模型各随机参数值,建立固定风速下的海浪模型;然后,在此基础上进一步构造了基于海浪谱的风力模型,模拟了风力作用下的海浪;最后,采用基于视点的网格拓扑结构,实现了海浪的实时绘制。实验结果表明,该模型不仅能更真实地模拟海浪,还具有较好的交互性。     

Insensitive output feedback H ∞ control of delta operator systems with insensitivity to sampling time jitter

The insensitive multi‐objective H _∞ control synthesis problem via dynamic output feedback for linear delta operator systems with insensitivity to sampling time jitter is investigated in the case of small sampling times. The delta‐domain model instead of the standard shift‐domain model is used to avoid the inherent numerical ill‐condition resulting from using the latter model at high sampling rates. Parameter sensitivity function of the transfer function with respect to sampling time is defined to mitigate the effect of sampling time jitter because it may cause significant degradation of the overall system performance. It is worth pointing out that a novel bounded real lemma for delta operator allowing extra degree of freedom for multi‐objective control design is presented by using the well‐known projection lemma. Then, from this new lemma, a two‐step design procedure based on LMI is presented to design insensitive dynamic output feedback controllers such that the resulting closed‐loop system is asymptotically stable and meets the requirement of sensitivity specification. A numerical example is also presented to show the effectiveness of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.     

Risk‐aware Path Planning for Autonomous Underwater Vehicles using Predictive Ocean Models

Recent advances in Autonomous Underwater Vehicle (AUV) technology have facilitated the collection of oceanographic data at a fraction of the cost of ship‐based sampling methods. Unlike oceanographic data collection in the deep ocean, operation of AUVs in coastal regions exposes them to the risk of collision with ships and land. Such concerns are particularly prominent for slow‐moving AUVs since ocean current magnitudes are often strong enough to alter the planned path significantly. Prior work using predictive ocean currents relies upon deterministic outcomes, which do not account for the uncertainty in the ocean current predictions themselves. To improve the safety and reliability of AUV operation in coastal regions, we introduce two stochastic planners: (a) a Minimum Expected Risk planner and (b) a risk‐aware Markov Decision Process, both of which have the ability to utilize ocean current predictions probabilistically. We report results from extensive simulation studies in realistic ocean current fields obtained from widely used regional ocean models. Our simulations show that the proposed planners have lower collision risk than state‐of‐the‐art methods. We present additional results from field experiments where ocean current predictions were used to plan the paths of two Slocum gliders. Field trials indicate the practical usefulness of our techniques over long‐term deployments, showing them to be ideal for AUV operations.     

Kriging‐based robotic exploration for soil moisture mapping using a cosmic‐ray sensor

Soil moisture monitoring is a fundamental process to enhance agricultural outcomes and to protect the environment. The traditional methods for measuring moisture content in the soil are laborious and expensive, and therefore there is a growing interest in developing sensors and technologies which can reduce the effort and costs. In this work, we propose to use an autonomous mobile robot equipped with a state‐of‐the‐art noncontact soil moisture sensor building moisture maps on the fly and automatically selecting the most optimal sampling locations. We introduce an autonomous exploration strategy driven by the quality of the soil moisture model indicating areas of the field where the information is less precise. The sensor model follows the Poisson distribution and we demonstrate how to integrate such measurements into the kriging framework. We also investigate a range of different exploration strategies and assess their usefulness through a set of evaluation experiments based on real soil moisture data collected from two different fields. We demonstrate the benefits of using the adaptive measurement interval and adaptive sampling strategies for building better quality soil moisture models. The presented method is general and can be applied to other scenarios where the measured phenomena directly affect the acquisition time and need to be spatially mapped.     

一种视点相关的海浪网格模型

在海浪模拟中,通过对两种常用网格化模型(基于随机采样点的网格化和基于固定采样点的网格化)的实验分析,给出了一种视点相关的海浪网格模型.在此基础上,运用纹理映射技术较好地模拟了大范围内的海浪生成,在保证较好的图像质量的同时提高了绘制效率.     

A General BRDF Representation Based on Tensor Decomposition

Generating photo‐realistic images through Monte Carlo rendering requires efficient representation of light–surface interaction and techniques for importance sampling. Various models with good representation abilities have been developed but only a few of them have their importance sampling procedure. In this paper, we propose a method which provides a good bidirectional reflectance distribution function (BRDF) representation and efficient importance sampling procedure. Our method is based on representing BRDF as a function of tensor products. Four‐dimensional measured BRDF tensor data are factorized using Tucker decomposition. A large data set is used for comparing the proposed BRDF model with a number of well‐known BRDF models. It is shown that the underlying model provides good approximation to BRDFs.     

动态天空环境下的实时海洋渲染

提出一种用于对完整海洋环境模型进行实时渲染的方法,包括天空球、大气系统和海浪模型.天空球采用纹理扰动的方法实现了云层飘动;大气系统考虑了大气的散射和吸收效应;海浪模型采用简单的正弦波构造海浪表面网格,并利用bump map法向扰动方法实现了海浪表面的凸凹效果,海浪的光照计算是通过对天空球采样得到入射光,再根据镜面反射得到反射光.最后给出了通过GPU硬件加速的实现实例以及在海战模拟中的实际应用.实例表明:该算法实现简单、绘制效率高,具有较高的真实感.     

Beyond equidistant sampling for performance and cost: A loop‐shaping approach applied to a motion system

Nonequidistant sampling potentially enhances the performance/cost trade‐off that is present in traditional equidistant sampling schemes. The aim of this paper is to develop a systematic feedback control design approach for systems that go beyond equidistant sampling. A loop‐shaping design framework for such nonequidistantly sampled systems is developed that addresses both stability and performance. The framework only requires frequency response function measurements of the LTI system, whereas it appropriately addresses the linear periodically time‐varying behavior introduced by the nonequidistant sampling. Experimental validation on a motion system demonstrates the superiority of the design framework for nonequidistantly sampled systems compared to traditional designs that rely on equidistant sampling.     

Learning Uncertainty in Ocean Current Predictions for Safe and Reliable Navigation of Underwater Vehicles

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.     

Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops

We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known.