水下机器人运动控制与仿真的数学模型

本文首先确定了水下机器人六自由度空间运动模型，为了便于在控制与仿真中应用，本文借助matlab平台，将水下机器人的六个自由度的方程转换为矩阵乘形式，然后结合水下机器人的环境仿真，推力器仿真，舵翼仿真，建立了水下机器人六自由度运动记真器。最后用该仿真器进行了运动仿真，仿真结果与海上试验吻合得很好，表明该仿真系统是符合实际的，该仿真系统采用一种普遍实用的数学模，精度较高，适用于任何类型的水下机器人，对研究水下机器人操纵与控制有很大现实意义。     

一种基于航位推算的水下机器人导航算法

本文针对智能水下机器人(AUV)导航信息较少的特点,进行了基于航位推算的AUV导航系统研究.首先利用传感器信息进行了航位推算,然后建立了基于一阶时间相关函数的辛格模型,利用强跟踪卡尔曼滤波算法对推算结果进行了次最优估计,提高了系统的跟踪能力,改善了导航精度,同时对GPS数据进行了野值剔除.松花湖上的试验结果验证了算法的有效性.     

船舶水下辐射噪声计算评估方法及应用

本文采用有限元(FEM)和边界元(BEM)相结合的方法对船舶水下辐射噪声进行研究。首先根据船舶的实际情况建立三维结构有限元模型,包括船体结构、压载、主要动力设备及其隔振方式等,然后结合实船测试的柴油发电机组、推进电机机脚振动和轴系中间支撑基座振动数值,及船模试验得到的螺旋桨脉动压力,计算获得流固耦合下结构的响应,最后将船体外壳水线以下结构响应作为约束条件,通过边界元的方法对水下辐射噪声进行计算和分析。从而对船舶设计阶段水下辐射噪声初步评估方法进行修正,同时对比水下辐射噪声实船测试结果,计算准确性较高,修正后的计算评估方法能进一步提高了设计阶段水下噪声的评估精度,为船舶水下辐射噪声控制提供了依据和参考。     

支持向量机的水声信号分类识别

研究水声信号识别特征的提取,在此基础上采用支持向量机理论,提出了一种水声信号的分类识别算法.该算法选取两类水声信号,提取它们的混沌特征值关联维数和h2熵作为目标信息,每类信号各提取32组数据,取两类水声信号各8组数据作为训练样本,训练支持向量机,其它样本用于验证.结果表明,支持向量机的分类算法能实现对目标的有效分类,分类效果较好,比较适合小样本、非线性分类.     

基于改进BOMP算法的水声信道估计

近年来,水声信道估计主要是基于稀疏模型展开.水声介质的非均匀性等使声线以簇的形式传播,导致水声信道展现出块结构稀疏特性.本文针对信道的块结构稀疏特性,在OFDM通信系统中,提出使用改进的BOMP算法进行水声信道估计.BOMP算法一次筛选1个最大相关块,改进的算法一次挑选t个非零块,算法重构时间将降低t倍.仿真结果表明:改进的BOMP算法误码率和重构时间要优于传统的LS、基于压缩感知的OMP算法;在不降低BOMP算法重构精度的前提下,将重构时间降低t倍.     

Segmentation of complex objects'' sonar images using parameter-fixed MRF model

The effective method of the recognition of underwater complex objects in sonar image is to segment sonar image into target, shadow and sea-bottom reverberation regions and then extract the edge of the object. Because of the time-varying and space-varying characters of underwater acoustics environment, the sonar images have poor quality and serious speckle noise, so traditional image segmentation is unable to achieve precise segmentation. In the paper, the image segmentation process based on MRF (Markov random field) model is studied, and a practical method of estimating model parameters is proposed. Through analyzing the impact of chosen model parameters, a sonar imagery segmentation algorithm based on fixed parameters' MRF model is proposed. Both of the segmentation effect and the low computing load are gained. By applying the algorithm to the synthesized texture image and actual side-scan sonar image, the algorithm can be achieved with precise segmentation result.     

基于双阵纯方位的水下运动目标运动分析研究

单基阵纯方位水下目标运动分析需要基阵所在平台至少作一次机动,复杂多变的海战环境中,往往并不允许平台执行机动,故通常需利用多维信息对水下目标进行运动分析.本文基于双阵方位进行水下目标运动分析,目前相关公开文献中多是从仿真层面对该问题进行研究,本文在数值仿真的基础上,通过对海试数据的处理,从试验层面研究这一问题.文中采用最大似然估计法和扩展卡尔曼滤波方法进行双阵纯方位水下目标运动分析,通过数值仿真及海试数据的处理,验证算法的有效性.     

多途效应对信息隐藏技术水下应用的影响

水声信道复杂多变的特点给信息隐藏技术在水下的应用带来了很大的困难,最大的挑战就在于多途效应。通过建立简单的多途效应模型,分析了多途效应引起的误差噪声。讨论了多途效应对信息隐藏算法的影响,并提出了相应的对策。     

基于多传感器递推总体最小二乘融合的水下机器人动力学模型参数辨识

对于水下机器人动力学模型辨识问题,如果其观测方程的系数矩阵包含随机扰动,则其最小二乘估计一般是有偏的。为此,该文提出一种基于多传感器递推总体最小二乘融合的水下机器人动力学模型辨识算法(RTLS_F)。首先,给出了集中式总体最小二乘融合的算法;然后,在总体最小二乘框架下,推导出多传感器递推融合估计算法。通过仿真实验对RTLS_F与其它水下机器人动力学参数辨识算法进行了比较。实验结果表明,在系数矩阵和观测向量都含有误差的情况下,最小二乘融合是有偏估计且难以提高估计精度,而RTLS_F算法可以有效改善参数辨识性能。     

基于粒子群优化卡尔曼滤波的水下机器人信号处理

在卡尔曼滤波的基础上,引入粒子群优化算法,对卡尔曼滤波方法进行改进,提出基于粒子群优化的卡尔曼滤波器模型,从而提高水下机器人测量数据的精度,降低系统噪声和量测噪声所带来的误差。水池仿真试验结果表明改进的滤波方法有效、实用。     

基于强化学习的自主式水下潜器障碍规避技术（英文）

Obstacle avoidance becomes a very challenging task for an autonomous underwater vehicle(AUV) in an unknown underwater environment during exploration process. Successful control in such case may be achieved using the model-based classical control techniques like PID and MPC but it required an accurate mathematical model of AUV and may fail due to parametric uncertainties, disturbance, or plant model mismatch. On the other hand, model-free reinforcement learning(RL) algorithm can be designed using actual behavior of AUV plant in an unknown environment and the learned control may not get affected by model uncertainties like a classical control approach. Unlike model-based control model-free RL based controller does not require to manually tune controller with the changing environment. A standard RL based one-step Q-learning based control can be utilized for obstacle avoidance but it has tendency to explore all possible actions at given state which may increase number of collision.Hence a modified Q-learning based control approach is proposed to deal with these problems in unknown environment.Furthermore, function approximation is utilized using neural network(NN) to overcome the continuous states and large statespace problems which arise in RL-based controller design. The proposed modified Q-learning algorithm is validated using MATLAB simulations by comparing it with standard Q-learning algorithm for single obstacle avoidance. Also, the same algorithm is utilized to deal with multiple obstacle avoidance problems.     

一种隧洞探测型AUV及其图像拼接方法研究

裂缝是输水隧洞最普遍的缺陷和潜在威胁,AUV巡检与拍摄输水隧洞中的裂缝,通过拍摄的水下图像进行处理,以确定裂缝位置。但由于水下环境极大限制了光视觉图像可视范围与分辨率,单幅水下图像所获得的视角范围有限,所以本文提出了基于SURF算法的AUV水下图像拼接方法研究。该方法首先在图像预处理阶段对水下图像进行去畸变与限制对比度自适应直方图均衡化处理,用于解决水下图像存在的畸变、对比度低、噪声严重等问题。接着将SURF特征点检测算法应用于水下图像配准当中,并与RANSAC算法相结合对特征点进行精确匹配,然后应用线性渐变融合算法实现水下图像融合,有效去除拼接缝隙,完成水下图像拼接,最终通过水池与外场实验拼接图像来验证该方法的正确性。     

智能水下机器人路径规划方法综述

路径规划方法是智能水下机器人技术研究的核心内容之一,是实现自主航行和作业的关键环节。本文将水下机器人的路径规划方法按智能程度分为传统和智能两大类。传统方法包括基于路线图构建的路径规划方法、基于单元分解的路径规划方法以及基于人工势场的路径规划方法,智能方法包括基于群智能的路径规划方法和基于机器学习的路径规划方法。针对水下机器人规划环境的特点,分别对这几类典型方法进行总结与评价,重点分析了智能方法的优缺点和关键问题,最后展望智能水下机器人路径规划的未来研究方向。     

A new hybrid terrain coverage method for underwater robotic exploration

It is well known that it is difficult to explore underwater terrains using an autonomous underwater vehicle due to the varieties and complexities of underwater terrain elements. Since conventional underwater terrain coverage techniques are usually based on the assumption that the underwater surface is planar, they generate an unnecessary exploration path especially on steep sloped surfaces of ocean basins. This paper proposes a new type of coverage technique, the hybrid terrain coverage framework (HTCF), which considers various surface conditions in three-dimensional environments and generates an efficient exploration path for all environments. The HTCF incorporates a planar terrain coverage algorithm, a spiral path terrain coverage algorithm, and a hybrid decision module to recognize and select the most suitable technique depending on the sloped surface variations. Simulation results show that the proposed HTCF is more efficient than the conventional terrain coverage algorithm in terms of the energy consumption of the underwater vehicle.     

基于水声传感器网络的目标纯方位运动分析

研究了基于水声传感器网络的目标纯方位运动分析原理及方法,建立了基于水声传感器网络的目标运动分析模型。在此基础上,讨论了模型中多维非线性估计问题,提出了一种基于传感器网络新的水下目标运动分析方法。该方法采用改进的粒子滤波EKF-PF(扩展卡尔曼-粒子滤波)算法实现,并与传统的扩展卡尔曼滤波(EKF)和粒子滤波(PF)算法进行了比较。通过Monte Carlo仿真分析,表明基于水声传感器网络的目标运动分析方法充分利用了网络的优势和当前测量信息。这种方法对水下目标运动状态估计时,不仅降低了计算量而且表现出较高的估计精度。所得结论为水下传感器网络进行目标被动定位提供了参考。     

大范围环境下自主式水下潜器两种全局路径规划方法的研究

应用遗传算法(GA)和A·算法对自主式水下潜器(简称AUV)在大范围海洋环境中的全局路径规划问题进行了研究.介绍了基于栅格的环境模型及其数据结构,讨论了GA的染色体编码方式、基于知识的初始种群生成方法与适应度函数,基于领域知识设计了五种遗传算子,给出了A·算法的具体实现方法.通过仿真结果可以看出:GA采用可变长编码方式使路径描述简单、清晰,具有收敛速度快、求解实际问题效率高的特点;A*算法可在较短时间内求得相对栅格优化的路径.两种算法均可满足系统实时性要求.     

某型水下航行器控制方案的半实物仿真

在以ＶＡＸｌａｂ计算机为主机的小型通用动力学半实物仿真系统上对某型水下航行器的运动进行了半实物仿真。给出了半实物仿真系统的基本组成和工作原理，讨论了水下航行器运动的仿真模型和实时积分算法。结果表明，该仿真系统结构合理，精度高，可靠性好，所采用的控制方案可使其满足使用要求，达到了预期的目的。     

Numerical analysis of the high skew propeller of an underwater vehicle

A numerical analysis based on the boundary element method (BEM) was presented for the hydrodynamic performance of a high skew propeller (HSP) which is employed by an underwater vehicle (UV). Since UVs operate at two different working conditions (surface and submerged conditions), the design of such a propeller is a cumbersome task. This is primarily due to the fact that the resistance forces as well as the vessel efficiency under these conditions are significantly different. Therefore, some factors are necessary for the design of the optimum propeller to utilize the power at the mentioned conditions. The design objectives of the optimum propeller are to obtain the highest possible thrust, minimum torque, and efficiency. In the current study, a 5-bladed HSP was chosen for running the UV. This propeller operated at the stern of the UV hull where the inflow velocity to the propeller was non-uniform. Some parameters of the propeller were predicted based on the UV geometrical hull and operating conditions. The computed results include the pressure distribution and the hydrodynamic characteristics of the HSP in open water conditions, and comparison of these results with those of the experimental data indicates good agreement. The propeller efficiency for both submerged and surface conditions was found to be 67% and 64%, respectively, which compared to conventional propellers is a significantly higher efficiency.     

一种面向水下无人航行器的实效图像增强算法

针对水下图像存在严重模糊、对比度低、噪声多等问题,根据小波的时频特性及多分辨率特点,提出一种基于小波变换的水下模糊图像增强算法。利用小波变换对RGB图像进行二层小波分解,把原图分解为高、低频子带,利用导向滤波算法估计低频子带上的照射分量并进行去除;利用软阈值算法对高频子带上的图像边轮廓信息进行去噪和增强处理;对水下图像进行小波逆构,并进行伽玛校正;最后利用改进的灰度世界算法对水下图像进行颜色校正。试验结果表明,使用文章中算法处理所得到图像的对比度及信噪比都较高,且清晰度较高,满足水下无人航行器的要求。     

Application of A* algorithm for real-time path re-planning of an unmanned surface vehicle avoiding underwater obstacles

This paper describes path re-planning techniques andunderwater obstacle avoidance for unmanned surface vehicle （USV）based on multi-beam forward looking sonar （FLS）. Near-optimalpaths in static and dynamic environments with underwaterobstacles are computed using a numerical solution procedure basedon an A algorithm. The USV is modeled with a circular shape in 2degrees of freedom （surge and yaw）. In this paper, two-dimensional（2-D） underwater obstacle avoidance and the robust real-time pathre-planning technique for actual USV using multi-beam FLS aredeveloped. Our real-time path re-planning algorithm has beentested to regenerate the optimal path for several updated frames inthe field of view of the sonar with a proper update frequency of theFLS. The performance of the proposed method was verifiedthrough simulations, and sea experiments. For simulations, theUSV model can avoid both a single stationary obstacle, multiplestationary obstacles and moving obstacles with the near-optimaltrajectory that are performed both in the vehicle and the worldreference frame. For sea experiments, the proposed method for anunderwater obstacle avoidance system is implemented with a USVtest platform. The actual USV is automatically controlled andsucceeded in its real-time avoidance against the stationary underseaobstacle in the field of view of the FLS together with the GlobalPositioning System （GPS） of the USV.