Underwater Localization for Multiple AUVs Based on Bearing and Range Measurements

A novel underwater localization algorithm for autonomous underwater vehicle （AUVs） is proposed. Taking aim at the high cost of the traditional ＂leader-follower＂ positioning, a ＂parallel＂ model is adopted to describe the localization problem. Under an unknown-but-bounded assumption for sensor noise, bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs. Merged these constraints, a convex polyhedron representing the set of all configurations consistent with the sensor measurements can be induced. Estimates for the,uncertainty in the position of a single AUV or the relative positions of two or more AUVs can then be obtained by projecting this polyhedron into appropriate subspaces of the configuration space. The localization uncertain region for each AUV can be recovered by an approximation algorithm to realize underwater localization for multiple AUVs. The deduced theoretically and the simulated results show that it is an economical and practical localization method for the AUV swarm.     

基于AUVs的水下移动无线传感器网络愈合算法

水下移动无线传感器网络（MUWSNs）动态演化特性制约着网络的拓扑的可靠性,很大程度上限制了MUWSNs的推广应用。针对水下实体移动导致的MUWSNs拓扑失效,提出了一种基于自主式水下航行器（AUV）的MUWSNs拓扑愈合算法,并构建了水下实体移动模型,利用AUV自主移动特性对失效拓扑进行快速愈合。仿真结果验证了该算法的正确性和有效性,极大地提高了MUWSNs可靠性,对MUWSNs拓扑领域研究具有一定贡献。     

多水下机器人的协调规划策略研究

以对策论为基础,从理论上分析了多水下机器人的运动协调机制,建立了多水下机器人运动协调问题的数学模型;以此为基础;提出了多机器人协调规划算法。     

Backstepping-based cooperative and adaptive tracking control design for a group of underactuated AUVs in horizontal plan

In this paper, we investigate new implementable cooperative adaptive backstepping controllers for a group of underactuated autonomous vehicles that are communicating with their local neighbours to track a time-varying virtual leader of which the relative position may only be available to a portion of the team members. At the kinematic cooperative control level of the autonomous underwater vehicle, the virtual cooperative controller is basically designed on a proportional and derivative consensus algorithm presented in Ren (2010) Ren, W. (2010). Consensus tracking under directed interaction topologies: Algorithms and experiments. IEEE Transactions on Control Systems Technology, 18, 230–237.[Crossref], [Web of Science ®] , [Google Scholar], which involves velocity information from local neighbours. In this paper, we propose a new design algorithm based on singular perturbation theory that precludes the use of the neighbours’ velocity information in the cooperative design. At the dynamic cooperative control level, calculation of the partial derivatives of some stabilising functions which in turn will contain velocity information from the local neighbours is required. To facilitate the implementation of the cooperative controllers, we propose a command filter approach technique to avoid analytic differentiation of the virtual cooperative control laws. We show how Lyapunov-based techniques and graph theory can be combined together to yield a robust cooperative controller where the uncertain dynamics of the cooperating vehicles and the constraints on the communication topology which contains a directed spanning tree are explicitly taken into account. Simulation results with a dynamic model of underactuated autonomous underwater vehicles moving on the horizontal plane are presented and discussed.     

基于自适应神经网络的AUVs路径跟踪控制

为解决由于随时间变化水动力阻尼引起的参数变化和不确定性的问题,提出了基于径向基函数神经网络的未知评估算法,引入自适应算法以保证神经网络权值的最优评估.基于Lyapunov稳定性理论,设计一种自适应神经网络控制器以保证路径跟踪系统中所有误差状态都趋于稳定.为了验证该控制器的可行性,对系统施加如位置误差、方向误差等虚拟干扰,证明该控制器可将误差消减为零.另一方面,机器人在以恒定的速度行驶时,每个航点被指定一个适合半径的圆弧可以保证其有较高的精度.为了评估路径跟踪控制器的性能,提出直线型和直线加圆弧型路径方案.仿真结果表明,该控制器可以有效地消除机器人非线性和模型不确定性造成的干扰.     

机器鱼在线功率检测系统设计与实现

水下仿生机器鱼由于其高效而灵活的水下运动而受到广泛关注.至今,人们对于水下机器人的研究主要集中在运动仿生以及动力学机理解释等方面,对于机器鱼的运动中的实时功率消耗研究还几乎空白.其中一个主要原因是水下机器人的功率消耗不能直接实时获得,因而提出了一个实时在线高速功率测量系统,通过该系统能够实现5000S/s的采样速率,获得精确的机器鱼在水中运动下的实时功率,从而为后期提高机器鱼游动效率的研究做基础铺垫.一系列实验证明了其对水下机器人的实时效率检测的有效性.此外,实现对机器鱼的功耗评估能够更好地为设计节能型水下航行器提供有力的指南.     

Multi-Scale Fusion Algorithm for AUVs Integrated Navigation Systems

To deal with the low location accuracy issue of existing underwater navigation technologies in autonomous underwater vehicles(AUVs),a distributed fusion algorithm which combines the model's analysis method with a multi-scale transformation method is proposed for integrated navigation system based on AUV.First,integrated navigation system theory and system error sources are introduced in details.Secondly,a navigation systems observation equation on the original scale is decomposed into different scales by the discrete wavelet transform method,and noise reduction is performed by setting the wavelet de-noising threshold.At last,the dynamic equation and observation equations are fused on different scales by the wavelet transformation and Kalman filter.The results show that the proposed algorithm has smaller navigation error and higher navigation accuracy.     

基于多AUV协作的稀疏水下传感网定位技术研究

如何在稀疏部署的水下传感器网络中实现传感器节点的高效定位是一个研究热点.提出了一种基于多个移动AUV协作的水下传感器网络内节点定位机制,利用AUV的精确自导航功能实现对网内未知位置节点的定位协助.提出的协作定位算法扩展了水下传感器网络的网内节点位置迭代估计方法,将信标节点和多AUV联合作为定位参考点,然后推导了基于最小二乘法的定位估计方程.仿真结果验证了该方法可以在定位节点比例、归一化定位误差和平均置信度等几个方面提高定位性能.     

基于多AUV间任务协作的水下多目标探测机制

利用自主式水下航行器（Autonomous Underwater Vehicle, AUV）对水下多目标进行协同探测是目前海洋技术领域的研究热点。本文主要研究在水下三维区间内的多AUV任务分配与协作探测机制,建立了以每个AUV能量耗费与能耗均衡为约束条件的水下三维空间中的多旅行商（Multiple Traveling Salesman Problem, MTSP）问题模型,利用遗传算法（Genetic Algorithm, GA）对该NP-Complete问题进行启发式求解,同时设计了考虑巡航总路径及访问目标数的适应度函数以提高多AUV间的能耗均衡性,实现多个AUV对多个水下目标的优化协同探测。最后本文利用Matlab R2014a软件对多AUV任务协作与多目标探测机制进行了仿真,仿真结果验证了该方法能够均衡多AUV多目标探测问题的能量消耗,进而提高巡航速度和生命周期。     

不同时变延迟下的多AUV编队协调控制

针对无领航者AUV编队协调控制问题,提出了一种不同时变通信延迟下的一致性协调控制方法;首先考虑到AUV之间水下通信存在时间延迟和数据丢失的问题,利用状态反馈线性化理论处理AUV数学模型中的非线性耦合项,从而将复杂的AUV模型转换为双积分器动态模型;其次,针对不同延迟通信情况,设计了位置和速度双独立的拓扑结构以减少编队成员之间发送每个数据包中的数据量;最后,提出了无领航者的多AUV稳定条件,进而将多AUV编队控制问题看作是一致性问题,基于Laypunov-Razumikhin定理证明时延多AUV编队系统的稳定性;该控制方法不仅能够克服不同时变延迟和数据丢失对编队的影响,使所有以随机位置和速度出发的AUV的三维轨迹均能达到一致状态,同时能抑制外界干扰;仿真结果与所提控制方法理论结果一致。