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.     

AUV辅助的水下传感器网络时钟同步算法

针对当前水下传感器网络中的时钟同步难题,设计了一种三元阵被动定位自主水下航行器（ AUV）模型,并基于此模型提出了AUV辅助的时钟同步（ AUV-Sync）算法。该算法通过AUV与节点之间相对运动过程中进行的信息交换来对节点相对距离进行估计,进而基于相对距离计算单向传播时延来降低由于节点移动性所导致的误差。最后,通过两轮加权最小二乘法进行线性回归来估计时钟同步的参数。仿真结果表明：在存在节点漂移的动态水下传感器网络环境中,该算法较其他相关算法有更高的精度。     

AUV自动跟踪水下管道的试验研究

本文介绍了AUV跟踪水下管道的一种方法,讨论了几 种跟踪方式的优、缺点,给出了控制系统原理图和试验结果．     

Safe breakwater-following control of an autonomous underwater vehicle with non-zero forward velocity

Periodic inspection is essential for the maintenance and security of port facilities. This paper discusses safe path-following control of an autonomous underwater vehicle (AUV) for port area inspection. An AUV usually has a non-holonomic constraint and cannot be stabilized by continuous linear feedback control. In this paper, we derive the error dynamics with respect to a straight line path and show that its linear form is controllable. We then propose a tuning method of linear feedback control gains, which considers the turn point, safety during navigation, and the physical limitations of the vehicle. A critical damped response to a given path is commonly regarded as an ideal one. However, in some cases underdamped and overdamped responses are better for safe navigation. The tuning method can select the suitable damping degree of the control response. Our method is first described for horizontal motion control and then is generalized to vertical motion and 3-D motion controls. Simulations were done to verify the effectiveness of the proposed tuning method and the results are discussed.     

便携式自主水下机器人动力学建模方法研究

针对50公斤级便携式自主水下机器人（AUV）的精确控制和研究,本文介绍了一种基于计算流体力学（CFD）和非线性最优化辨识的动力学建模方法．着重推导了便携式AUV动力学模型,并使用计算流体力学方法代替水池实验获取辨识所需数据和通过非线性最优化方法获得合适的模型结构和模型参数．最终建立了便携式AUV数学模型,并通过对仿真数据与试验数据的对比,证明了该模型及方法的有效性．     

一种基于电子海图的欠驱动AUV区域搜索方案

针对环境(主要指边界信息)已知的区域搜索任务,设计了适用于欠驱动AUV(autonomous underwater vehicle)的区域搜索方案,能在极少量人工参与的情况下在任务区域的电子海图基础上实现环境模型建立进而规划出全局路径,由AUV自主地完成对路径和地形的自主跟踪,从而实施诸如地理信息采集或物体探测等作业任务.首先,路径规划方法能在给定任务参数的基础上自主地对电子海图进行处理形成环境模型并据此给出2维梳状全局路径,即使区域的几何形状为复杂的凹多边形也能给出合理结果;其次,自主跟踪采用解耦的控制方法,即分别对纵向速度、艏向和深度进行控制,使得AUV能同步地完成对水平面路径和对海底地形的跟踪.提出了一种基于Sigmoid函数的路径跟踪制导控制方法,控制器以AUV的当前艏向及其与路径的横向距离偏差作为输入,经过计算输出参考艏向角;将具有良好鲁棒性的非线性自适应S面控制算法融入执行层的控制,在加速了系统响应的同时减小了超调从而提升了控制效果;最后,跟踪仿真试验的结果显示AUV在水平面对梳状路径的跟踪误差在给定路径规划重叠率参数的前提下能保证AUV对区域扫描的全覆盖,在垂直面对地形高度的跟踪误差符合传感器对距离的要求.全局路径规划方法的可行性在介绍算法的同时结合算例得以验证,跟踪仿真试验也说明自主跟踪控制器能较好地同时跟踪梳状路径和变化的地形.     

Robust input design for nonlinear dynamic modeling of AUV

Input design has a dominant role in developing the dynamic model of autonomous underwater vehicles (AUVs) through system identification. Optimal input design is the process of generating informative inputs that can be used to generate the good quality dynamic model of AUVs. In a problem with optimal input design, the desired input signal depends on the unknown system which is intended to be identified. In this paper, the input design approach which is robust to uncertainties in model parameters is used. The Bayesian robust design strategy is applied to design input signals for dynamic modeling of AUVs. The employed approach can design multiple inputs and apply constraints on an AUV system’s inputs and outputs. Particle swarm optimization (PSO) is employed to solve the constraint robust optimization problem. The presented algorithm is used for designing the input signals for an AUV, and the estimate obtained by robust input design is compared with that of the optimal input design. According to the results, proposed input design can satisfy both robustness of constraints and optimality.     

基于反馈增益的AUV稳定神经网络反步变深控制

为解决自主水下航行器的变深控制问题,提出一种基于反馈增益的反步控制方法.首先,通过设计控制器参数消除部分非线性项,在保证系统稳定性的同时设计神经网络控制器来补偿纵倾运动中的模型不确定性;然后,通过自适应鲁棒控制器对神经网络的逼近误差予以消除,以加快神经网络的收敛学习速度,神经网络权值和逼近误差估计的学习律可由李雅普诺夫稳定性理论推导得出,保证了闭环系统的一致最终有界性;最后,通过仿真实验验证了所提出方法的有效性.     

欠驱动AUV模糊神经网络L2增益鲁棒跟踪控制

提出基于模糊神经网络欠驱动水下自主机器人(AUV)的L2增益鲁棒跟踪控制方法,该方法通过在线学习逼近动力学模型的不确定项.控制器克服了由于缺少横向推力对跟踪误差的影响,在考虑未知海流干扰情况下,实现了系统对模糊神经网络逼近误差的L2增益小于γ.利用Lyapunov稳定性理论证明了闭环控制系统误差信号一致最终有界.最后,通过精确模型参数和参数扰动仿真实验验证了该控制方法具有很好的跟踪效果和较强的鲁棒性.     

速度矢量坐标系下水下机器人动态避障方法

针对水下机器人能力有限问题设计了一种适应水下机器人能力的动态避障方法.以水下机器人对运动障碍物的相对速度信息建立速度矢量坐标系,在坐标系内,根据水下机器人速度矢量和障碍物速度矢量的相互关系以及水下机器人的能力,确定理论上能够避开障碍物的期望速度解集,然后按照满足碰撞时间条件的最小转艏避障原则求解水下机器人最优期望速度的大小和方向,使水下机器人能够以相对小的代价安全避开动态障碍物.仿真研究验证了此规划方法对躲避动态障碍物的有效性,该研究大幅度提高了水下机器人的智能水平.