深海长期驻留自主水下机器人系统研究现状与发展趋势

深海环境的长周期监测已成为人类分析和认识海洋生态系统和海洋环境变换过程的必要手段,而深海长期驻留自主水下机器人（LRAUV）系统则是实现深海环境长周期监测的有效装备,成为近年来深海领域的研究热点。本文通过梳理、总结前人的研究,首先对LRAUV系统的含义及其工作模式进行了介绍;对LRAUV系统的典型应用领域进行了分析与展望;然后,对国内外LRAUV系统进行了综述,包括其配置、功能与技术参数等内容,并总结了其发展趋势;最后对LRAUV系统长期生存、基站支撑、AUV自主对接、能源补充及数据传输、探测作业等关键技术的研究现状、难点问题及未来发展方向进行了综述与分析。     

柔性机器人水下运动的建模与仿真

本文以柔性机器人水下环境中的柔顺运动为研究对象,建立了柔性机器人水下运动的动力学模型。并运用VC 6.0和Matlab7.0进行仿真,结果显示了柔性机器人在水下环境中良好的运动学特性。     

水下船体清刷机器人运动分析及仿真

本文分析了水下船体清刷机器人的运动,建立了机器人运动学方程,根据所设计的伺服控制系统对机器人系统进行了运动仿真.结果表明,所设计的机器人系统鲁棒性好,满足设计要求.     

基于决策树的羽流追踪机器人自主决策方法研究

针对无法获得可靠羽流流向信息不利于实现羽流追踪的问题，提出了一种基于决策树的羽流追踪移动机器人自主决策方法。该方法通过移动机器人两侧的浓度传感器采集到的浓度信息，利用追踪的行为规则建立决策树模型，获得行为决策信息，使机器人高效地追踪到羽流并精确地定位。由于浓度变化关系蕴含了羽流的流向及流速信息，从而取代了传统方法中流向及流速传感器。在扩散环境下，通过移动机器人羽流追踪实验，实现了良好的源定位效果。     

多水下机器人视景仿真系统技术研究

该文介绍了多水下机器人群体分佰式智能控制软件的视景仿真系统。以MultiGen Creator建模,利用实时视景开发软件包OpenGL Performer,采用POSIX Thread多线程技术,在Linux环境下开发了分布式多水下机器人三维视景仿真平台。实现了多水下机器人分布式智能控制软件系统的实时视景仿真,保证了整个系统的信息共享与时间同步。同时该文讨论了该视景仿真软件的开发过程以及整个系统的体系结构和信息流程。试验结果表明,该视景系统可逼真演示多水下机器人编队水下航行、作业等过程,并满足系统仿真的实时性要求。     

自治水下机器人全自由度仿真

由于自治水下机器人的复杂性，系统仿真技术变得越来越重要，系统地分析了自治水下机器人（AUV，Autonomous UnderwaterVehicle)的运动模型和空间运动方程，运用MATAB下的SIMULINK，设计了自治水下机器人的全自由度仿真工具箱，包括机器人本体运动，位姿求解和坐标系统转换等多个部分，可以方便地进行控制方法的全自由度的仿真。     

Linux环境下基于Agent的自主机器人仿真系统

针对复杂、不可预测环境下自主机器人系统的特点,研究开发了自主机器人仿真系统（AR－SIM）。本文介绍了AR－SIM的设计目的、系统特点、系统结构,以及在Linux环境下现,并提出了一种基于Agent的自主机器人控制结构。     

微小型自主水下机器人研究现状

综述了国内外微小型自主水下机器人（AUV）的研究现状。首先,对微小型AUV的定义进行了梳理。其次,基于微小型AUV的平台结构特征,对微小型AUV的应用背景和特征进行了归纳和总结。最后,对微小型AUV的发展趋势进行了分析和探讨。为微小型AUV的设计提供了参考,对微小型AUV的应用发展具有借鉴意义。     

基于局域网的多水下机器人仿真系统设计与实现

多水下机器人仿真系统是一个能够对多水下机器人系统的体系结构、协调控制、路径规划、学习算法等进行演示验证的分布式实时数字仿真系统,是开展多水下机器人技术研究的基础和有效手段．讨论了应用基于局域网的分布式仿真技术来解决多水下机器人系统仿真的问题,并详细说明了仿真系统的硬件组成和软件总体设计．     

自治水下机器人实时仿真系统开发研究

针对自治水下机器人(AUBs)开发和研究中的瓶颈问题,该文开展了AUV实时仿真系统的研究工作。该文提出了采用半实物实时仿真模式,建立实时仿真系统平台的方案,并对实时仿真系统平台的硬件结构和软件结构进行了详细设计。在方案设计的基础上,正在进行实时仿真系统平台开发和研制工作。     

基于神经网络的自治水下机器人广义预测控制

针对自治式水下机器人高度非线性和时变性的特点,提出了一种基于神经网络的水下机器人广义预测控制策略．利用改进型Elman网络作为多步预测模型,在对网络学习算法进行改进的基础上,实现了Elman网络的在线学习,并提出了用于求解神经广义预测控制律的灵敏度公式．进行了具有神经网络在线学习功能和不具有在线学习功能的水下机器人的速度控制实验,并就预测控制效果进行了对比分析．实验结果表明,具有自适应学习功能的水下机器人速度控制法的精度要优于不具有在线学习功能的速度控制法,且当水下机器人动态特性发生变化时具有较强的自适应能力．     

小型自治水下机器人控制系统研究开发

开发了一种性价比高的CAN总线分布式自治水下机器人控制系统结构,并采用了独特的故障处理方法．根据弱耦合简化模型,详细设计了航行控制器,并对航行控制系统进行了仿真研究．仿真与试验结果表明: 控制系统性能可靠、传输率高,并具有很好的鲁棒性与稳定性,能够满足水下机器人工作环境复杂多变的要求．     

基于凸优化算法的无人水下航行器协同定位

In this paper, a cooperative localization algorithm for autonomous underwater vehicles (AUVs) is proposed. A ``parallel" model is adopted to describe the cooperative localization problem instead of the traditional ``leader-follower" model, and a linear programming associated with convex optimization method is used to deal with the problem. After an unknown-but-bounded model for sensor noise is assumed, bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs. Merging these constraints induces a convex polyhedron representing the set of all configurations consistent with the sensor measurements. Estimates for the uncertainty in the position of a single AUV or the relative positions of two or more nodes can then be obtained by projecting this polyhedron onto appropriate subspaces of the configuration space. Two different optimization algorithms are given to recover the uncertainty region according to the number of the AUVs. Simulation results are presented for a typical localization example of the AUV formation. The results show that our positioning method offers a good localization accuracy, although a small number of low-cost sensors are needed for each vehicle, and this validates that it is an economical and practical positioning approach compared with the traditional approach.     

自主水下航行器的变质心跟踪控制

The trajectory-tracking control problem is investigated for an autonomous underwater vehicle (AUV) moving in the vertical plane using an internal point mass and a rear thruster as actuators. Combined with the dynamics of the point mass, the AUV is modeled as an underactuated system. A Lyapunov-based tracking controller is proposed by using backstepping approach to stabilize the error dynamics and force the position errors to a small neighborhood of the origin. Simulation results validate the proposed tracking approach.     

基于CAN总线的自治水下机器人控制系统

将CAN总线应用在自治水下机器人中,构成多主站的分布式控制系统,取代了以往水下机器人采用的集中式控制结构及主从式网络结构．从硬件和软件两方面详细介绍了CAN总线在自治水下机器人中的应用方案,设计了基于P87C591单片机的CAN总线控制器,并针对水下机器人分布式控制系统的需求特点,设计了CAN应用层协议和相应的通信软件．最后通过湖试验证了控制系统的可行性和可靠性．     

Global Adaptive Neural Network Control of Underactuated Autonomous Underwater Vehicles with Parametric Modeling Uncertainty

This paper focuses on global adaptive neural network control for a class of underactuated autonomous underwater vehicles in the presence of possibly large modeling parametric uncertainty. As the control inputs cannot directly act in the sway and heave directions, two virtual velocities defined here, plus three actual control actions provided by the thrusters and rudders, are used to achieve the convergence of the system errors to around zero. Motivated by real‐time characteristics in the trajectory tracking, the proposed controller presents a significant advantage because it contains only one adaptive parameter to be updated online rather than the neural network weights. In addition, we also consider the practical situation that the velocities of the vehicle may experience sharp speed jumps when the position tracking errors initially change suddenly, which always results in thruster saturation. The biologically inspired model is introduced to smooth the virtual velocity commands such that the vehicle satisfies the control input and velocity constraints. Finally, comparison simulations are given to show the effectiveness of the proposed scheme.     

基于滤波反步法的欠驱动AUV三维路径跟踪控制

研究了欠驱动自主水下航行器 (Autonomous underwater vehicle, AUV)的三维空间路径跟踪控制问题.针对基于虚拟向导建立的三维路径跟踪误差模型, 采用滤波反步法设计跟踪控制器,通过二阶滤波过程获得虚拟控制量的导数, 避免了直接对虚拟控制量解析求导的复杂过程, 同时滤除了高频测量噪声, 增加了系统对噪声的鲁棒性.通过设计滤波误差补偿回路, 保证了滤波信号对虚拟控制量的逼近精度.基于李雅普诺夫稳定性理论设计鲁棒项, 保证了闭环跟踪误差系统状态的渐近稳定.仿真结果表明了该控制器对噪声干扰具有一定的鲁棒性, 能够实现对三维路径的精确跟踪.     

基于双目技术的无人机自主三维定位方法研究

无人机具有体积小、造价低、机动性强、隐蔽性好等优点,在军事侦查、地理勘测、目标跟踪等领域应用广泛。由于无法采用造价昂贵、体积庞大的精密惯导设备,当应用场景为弱或无GPS环境时,无人机的准确位置信息将难以获取,并且在实际飞行过程中还面临着GPS信号不稳或易遭欺骗等挑战。针对以上问题,提出了基于双目技术的无人机自主三维定位方法。介绍了双目立体视觉技术的光学物理原理,以及双目摄像头与目标的距离判别算法;设计了无人机通过双目技术进行三维定位的方法;详细说明了通过RSS定位方法计算出无人机的精确三维位置信息的运算步骤及矩阵计算式;通过MATLAB仿真实验检验了该基于双目技术的无人机自主三维定位方法的有效性和可实现性。结果表明,该无人机自主三维定位方法具有定位精度高、普适性强等特点。     

Globally Stable Adaptive Dynamic Surface Control for Cooperative Path Following of Multiple Underactuated Autonomous Underwater Vehicles

The cooperative path following problem of multiple underactuated autonomous underwater vehicles (AUVs) involves two tasks. The first one is to force each AUV to converge to the desired parameterized path. The second one is to satisfy the requirement of a cooperative behavior along the paths. In this paper, both of the tasks have been further studied. For the first one, a simplified path following controller is proposed by incorporating the dynamic surface control (DSC) technique to avoid the calculation of derivatives of virtual control signals. Besides, in order to handle the uncertain dynamics, a new type of neural network (NN) adaptive controller is derived, and then an NN based energy‐efficient path following controller is firstly proposed, which consists of an adaptive neural controller dominating in the neural active region and an extra robust controller working outside the neural active region. For the second one, in order to reduce the amount of communications between multiple AUVs, a distributed estimator for the reference common speed is firstly proposed as determined by the communications topology adopted, which means the global knowledge of the reference speed is relaxed for the problem of cooperative path following. The overall algorithm ensures that all the signals in the closed‐loop system are globally uniformly ultimately bounded (GUUB) and the output of the system converges to a small neighborhood of the reference trajectory by properly choosing the design parameters. Simulation results validate the performance and robustness of the proposed strategy.