海洋溢油观测网络性能评价方法研究

海上溢油事故对海洋生态环境污染严重,溢油监测对于溢油事故的有效处理具有重要作用。本文从空、天、海、地的空间维度对现有的海上溢油事故监测平台进行分类,对常见的溢油信息监测传感器进行对比分析,对多平台、多传感器的海上溢油观测组网技术进行分析。建立了海上观测网络评价指标体系,并以机动性指标为例,给出了具体观测方案特定指标的定性和定量评价方法。     

国内外海底观测网络的建设进展

海底观测网络是海底技术发展的新领域,它将各种观测仪器放到海底,可对海水层、海底和海底以下的岩石进行长期动态的观测,并实时将数据传回到陆地。简介了国内外主要的海底观测网络,重点跟踪了它们的最新建设进展。     

水下机器人-机械手系统构建与研究

描述了一水下机器人——机械手系统研究平台的搭建,详细介绍了三功能水下电动机械手的设计与实验,给出了载体分系统的设计结果,利用Matlab工具箱和M函数构建了系统仿真模型,可以有效地对系统规划和控制算法进行验证(包括分别对载体分系统和机械手分系统的控制),可为进一步的现场试验提供指导和方法验证。     

自治水下机器人机械手系统协调运动研究

简单描述了自治水下机器人搭载的三功能水下电动机械手的设计,鉴于自治水下机器人-机械手系统运动学冗余、内部可能干涉以及载体圆筒式外形等特点,将惩罚调节因子引入系统运动学伪逆矩阵,保证了关节在允许范围内运动,避免载体大幅度姿态变化及载体与机械手之间的干涉,同时采用梯度投影法优化海流作用下的系统推力。仿真表明,该算法在解决系统冗余度的同时,有效地协调多任务下的系统动作。     

海底监测技术之海底观测网络

海底观测网络的出现改变了人们观测海洋的方式,成为了海洋科学界新的研究热点。通过介绍现有观测技术的局限性,探讨了海底观测网具有的特征及功能,总结了各国的研究进展,指出了我国大陆架科学钻探中海底观测网建设应考虑的观测内容及部署原则。     

水下机器人-机械臂系统的滑模自抗扰控制

针对水下机器人机械臂系统的强耦合、强非线性、复杂海洋多源干扰等因素影响,提出了滑模自抗扰控制器,将复杂系统模型转变为简单的积分串联系统,将内部参数不确定性、测量误差、建模误差和海洋多源干扰等扰动归结为总扰动,并采用线性扩张观测器对其进行估计并抵消。利用滑模控制器提高系统对参数摄动的不敏感性,增强控制系统的抗干扰性能,通过李雅普诺夫理论分析了控制系统的有界稳定性。仿真结果表明滑模自抗扰与传统滑模控制和自抗扰控制相比,能使水下机器人机械臂实现更好的轨迹跟踪,且系统具有更好的抗干扰能力。     

AUV水下对接装置控制系统设计

自主式水下机器人(Autonomous Underwater Vehicle,简称AUV)在航行使命结束后需要回收至甲板或陆地进行补给和维护。为避免重复布放回收所带来的不便,根据锥形导向式回收原理,针对水下对接装置及其控制系统进行了设计。水下对接装置控制系统由水面控制终端,水下控制系统和水下外部设备等部分组成,使用超短基线引导AUV进入指定区域,在对接过程中依靠行程开关和无线电反馈的信息判断AUV的相对位置及状态,并通过驱动相应的液压机构对AUV姿态进行校正和固定,进而完成对接过程。水下对接装置在千岛湖进行了试验,在吊装水下7 m的情况下实现了AUV的水下对接,并利用湿插拔电连接器完成了对AUV的有线充电和数据上传。试验验证了对接方案的可行性以及控制系统的稳定性,为将来AUV能够进行长时间、不间断航行提供了可能。     

基于RBF-MPC的水下机器人避碰控制

水下机器人避碰控制是自主作业的重要基础,但复杂的约束条件和模型的不精确性增加了避障路径跟踪的技术难度。在传统模型预测控制的基础上,结合作业场景多种约束条件,引入径向基函数神经网络,提出了一种水平面避碰控制方法。首先,采用径向基神经网络建立误差补偿函数,提高传统动力学预测模型精度;然后,结合避碰路径跟踪控制,在滚动优化环节选取性能指标函数,并显式引入障碍物、执行机构与控制稳定性等约束条件;最后,通过仿真试验证明该方法能够控制水下机器人跟踪避碰路径实现水平面内障碍物规避。     

Redundancy resolution for underwater mobile manipulators

A new fault-tolerant redundancy resolution scheme is presented that allows a single six degree of freedom (DOF) command to be distributed over a small remotely operated underwater vehicle–manipulator (ROVM) system. ROVM systems are composed of a remotely operated underwater vehicle (ROV) and serial manipulator. The combined system is often kinematically redundant for the six-DOF end-effector command, and such a ROVM system admits an infinite number of joint-space solutions for a commanded end-effector state. In the current work, the primary objective is to follow the desired end-effector velocities commanded by a human pilot. The primary objective is realized using the right Moore–Penrose pseudoinverse solution that minimizes the two-norm of the collective joint velocities. Secondary objectives considered are: avoiding manipulator joint limits, avoiding singularities and high joint velocities, keeping the end-effector in sight of the on-board camera, minimizing the ROV motion, and minimizing the drag-forces on the ROV. Each criterion is defined within the framework of the gradient projection method (GPM). The hierarchy for the secondary tasks is established by a low-level artificial pilot that determines a weighting factor for each criterion based on if–then-type fuzzy rules that reflect an expert human pilot's knowledge. The resulting weight schedule yields a self-motion (null-space motion) that emulates how a skilled operator would utilize the redundancy of the ROVM to achieve the secondary objectives. In addition, the proposed method has a fault-tolerant property that enforces joint-velocity limits and also redistributes the end-effector velocity command in the case of faulty joints. To demonstrate the efficacy of the proposed scheme, a numerical simulation case study is performed. The results illustrate that complex spatial end-effector manoeuvres that are otherwise not possible with a stationary ROV can be accomplished in real-time via the coordination of the ROV and the manipulator. The on-line nature of the proposed scheme makes it suitable for remote systems where the desired end-effector state is not known a priori.     

Study mobile underwater positioning system with expendable and multi-functional bathythermographs

The purpose of the current study is to introduce a set of mobile underwater positioning systems (MUPS) that will enable non-offshore vessels to execute underwater missions. Besides mobility, the system would also possess the advantage of having to use fewer acoustic instruments than conventional acoustic positioning systems. The method adopted by the system will involve the use of expendable and multi-functional bathythermographs (XBT) to measure the underwater acoustic speed and the depth of water at the same time. Then it must utilize the geometric relations formed by measuring the position of underwater targets at set intervals during navigation. In addition, since sound does not travel in a straight line when underwater, the iteration and convergence method must be used to perform corrections on the transmission speed and positional errors to obtain an accurate coordinate of the underwater target. After simulation testing, the positioning system established by the current study has proven to be fast in converging the error values along with high positioning accuracy of the system. The results of the study indicate that the MUPS built by the research institute can be utilized on a vessel, and will be very helpful in assisting the management of urgent underwater positioning missions.     

Estimating the underwater light field from remote sensing of ocean color

We present a new approach that incorporates two models to estimate the underwater light field from remote sensing of ocean color. The first employs a series of analytical, semi-analytical, and empirical algorithms to retrieve the spectrum of inherent optical properties (IOPs), including the absorption and the backscatter coefficients, from the spectrum of remote sensing reflectance. The second model computes the profile of photosynthetically available radiation E _0,PAR (z) for a vertically homogeneous water column using the information of the retrieved IOPs and the ambient optical environment. This computation is based on an improved look-up table technology that possesses high accuracy, comparable with the full solution of the radiative transfer equation, and meets the computational requirement of remote sensing application. This new approach was validated by in situ measurements and an extensive model-to-model comparison with a wide range of IOPs. We successfully mapped the compensation depth by applying this new approach to process the SeaWiFS imagery. This research suggests that E _0,PAR (z) can be obtained routinely from ocean-color data and may have significant implications for the estimation of global heat and carbon budget.     

Formation rates of subtropical underwater in the Pacific Ocean

Water mass formation rates were calculated for subtropical underwater (STUW) in the North and South Pacific by two partially independent methods. One is based on the World Ocean Circulation Experiment (WOCE)/TOGA drifter array over two periods: 1988–1992, and 1992–1996. Drifter velocities were used to calculate two components of the subduction rate, lateral induction and vertical pumping. The second method used CFC-12 data (1987–1994) from WOCE and Pacific Marine Environmental Laboratory to calculate ages on σ_θ surfaces. Subduction rates were estimated from the inverse age gradient. The two subduction rate methods are independent, but they share a common identification of STUW formation area based on satellite-derived surface temperature maps. Using both methods, one can put bounds on the formation rates: 4–5 Sv in the North and 6–7 Sv in the South Pacific. The drifter calculated STUW subduction rates for 1988–1992 and 1992–1996 are 21 and 13 m/yr in the North Pacific and 25 and 40 m/yr in the South. The CFC-12 calculated STUW subduction rate in the North Pacific is 26 m/yr, and 32 m/yr in the South. The South Pacific rates exceed those in the North Pacific. Consistent differences between the two methods support earlier studies, they conclude that mixing contributes to STUW formation in addition to the larger-scale circulation effects. The drifter and tracer rates agree well quantitatively, within 22%, except for the second period in the North Pacific and there are some differences in spatial patterns. Tracer rates integrate over time, and drifters allow analysis of interannual variability. The decrease in subduction rate between periods in the North Pacific is due to negative lateral induction entraining STUW into the mixed layer. The increase in the South Pacific rate is due to an increase in the vertical pumping. Although Ekman pumping is in phase in the North and South, the subduction rate is out of phase. These results confirm that subduction depends on the large-scale circulation and a combination of the outcrop pattern and air–sea fluxes. Temporal differences in rates and partitioning between the hemispheres are consistent with interannual changes in gyre intensity and current positions.     

On-line learning control of autonomous underwater vehicles usingfeedforward neural networks

A neural-network-based learning control scheme for the motion control of autonomous underwater vehicles (AUV) is described. The scheme has a number of advantages over the classical control schemes and conventional adaptive control techniques. The dynamics of the controlled vehicle need not be fully known. The controller with the aid of a gain layer learns the dynamics and adapts fast to give the correct control action. The dynamic response and tracking performance could be accurately controlled by adjusting the network learning rate. A modified direct control scheme using multilayered neural network architecture is used in the studies with backpropagation as the learning algorithm. Results of simulation studies using nonlinear AUV dynamics are described in detail. The robustness of the control system to sudden and slow varying disturbances in the dynamics is studied and the results are presented     

Formation control of multiple underwater vehicles subject to communication faults and uncertainties

This paper proposes a novel approach to analyze and design the formation keeping control protocols for multiple underwater vehicles in the presence of communication faults and possible uncertainties. First, we formulate the considered vehicle model as the Port-controlled Hamiltonian form, and introduce the spring-damping system based formation control. Next, the dynamics of multiple underwater vehicles under uncertain relative information is reformulated as a network of Lur’e systems. Moreover, the agents under unknown disturbances generated by an external system are considered, where the internal model is applied to tackle the uncertainties, which still can be regulated as the Lur’e systems. In each case, the formation control is derived from solving LMI problems. Finally, a numerical example is introduced to illustrate the effectiveness of the proposed theoretical approach.     

基于SolidWorks移动式平台稳性计算方法

提出一种利用SolidWorks进行移动式平台稳性计算的方法.应用VB.NET对SolidWorks进行二次开发,自动创建、修改平台的三维实体模型,并由此快速准确地求出体积、形心等稳性计算所需的参数,从而使平台稳性计算变得更加方便快捷而且精度更高.最后通过计算实例证明,利用该方法计算平台的稳性是切实可行的.     

Remote sensing of water depths in shallow waters via artificial neural networks

Determination of the water depths in coastal zones is a common requirement for the majority of coastal engineering and coastal science applications. However, production of high quality bathymetric maps requires expensive field survey, high technology equipment and expert personnel. Remotely sensed images can be conveniently used to reduce the cost and labor needed for bathymetric measurements and to overcome the difficulties in spatial and temporal depth provision. An Artificial Neural Network (ANN) methodology is introduced in this study to derive bathymetric maps in shallow waters via remote sensing images and sample depth measurements. This methodology provides fast and practical solution for depth estimation in shallow waters, coupling temporal and spatial capabilities of remote sensing imagery with modeling flexibility of ANN. Its main advantage in practice is that it enables to directly use image reflectance values in depth estimations, without refining depth-caused scatterings from other environmental factors (e.g. bottom material and vegetation). Its function-free structure allows evaluating nonlinear relationships between multi-band images and in-situ depth measurements, therefore leads more reliable depth estimations than classical regressive approaches. The west coast of the Foca, Izmir/Turkey was used as a test bed. Aster first three band images and Quickbird pan-sharpened images were used to derive ANN based bathymetric maps of this study area. In-situ depth measurements were supplied from the General Command of Mapping, Turkey (HGK). Two models were set, one for Aster and one for Quickbird image inputs. Bathymetric maps relying solely on in-situ depth measurements were used to evaluate resultant derived bathymetric maps. The efficiency of the methodology was discussed at the end of the paper. It is concluded that the proposed methodology could decrease spatial and repetitive depth measurement requirements in bathymetric mapping especially for preliminary engineering application.     

卫星遥感SSH数据在水下温度垂直分布反演中的应用研究

结合MODAS基本原理,应用动态气候态的数据同化概念,阐述应用卫星遥感SSH数据反演水下温度垂直分布的理论和方法,并利用西太平洋海区的ARGO数据进行试验,建立了单点的温度剖面,通过引入SSH数据进行水温反演,演示结果显示了卫星遥感SSH资料同化的有效性,并得出分析结论。     

基于BP人工神经网络的水体遥感测深方法研究

利用Landsat7ETM＋遥感图像反射率和实测水深值之间的相关性,建立了动量BP人工神经网络水深反演模型,并对长江口南港河段水深进行了反演.结果表明：具有较强非线性映射能力的动量BP神经网络模型能较好地反演出长江口南港河段的水深分布情况;由于受长江口水体高含沙量的影响,模型对小于5 m的水深值反演精度较高,而对大于10 m的水深值反演精度较低.