基于多AUV间任务协作的水下多目标探测路径规划

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.主要研究在水下三维区间内的多AUV任务分配与协作探测路径规划机制,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MTSP(Multiple Traveling Salesman Problem)问题模型,利用遗传算法GA(Genetic Algorithm)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路径规划机制进行了仿真,仿真结果验证了本文方法能均衡多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.     

洋流影响下基于运动矢径的AUV协同定位方法

针对水下自主航行器（AUV）协同定位受水下未知定常洋流影响的问题,给出一种洋流影响下基于运动矢径的AUV协同定位方法.利用AUV的运动学方程和基于运动矢径的量测方程,建立AUV的导航模型;通过扩展的卡尔曼滤波,设计了协同定位滤波算法;利用该算法对洋流速度进行估计,以补偿AUV定位误差.仿真结果表明,该算法能有效估计未知定常洋流速度的大小,并对AUV定位误差进行实时补偿,显著提高了AUV的定位精度.     

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.     

基于鲁棒Restless Bandits模型的多水下自主航行器任务分配策略

针对水下监测网络中多自主航行器（AUV）协同信息采集任务分配问题进行了研究。首先，为了同时考虑系统中目标传感器的节点状态与声学信道状态对AUV任务分配问题的影响，构建了水声监测网络系统的综合模型；其次，针对水下存在的多未知干扰因素并考虑了模型产生不精确的情况，基于强化学习理论将多AUV任务分配系统建模为鲁棒无休止赌博机问题（RBP）。最后，提出鲁棒Whittle算法求解所建立的RBP，从而求解得出多AUV的任务分配策略。仿真结果表明，在干扰环境下与未考虑干扰因素的分配策略相比，在系统分别选择1、2、3个目标时，鲁棒AUV分配策略对应的系统累计回报值参数的性能分别提升了5.5%、12.3%和9.6%，验证了所提方法的有效性。     

基于距离量测的双领航多AUV协同定位队形优化分析

利用领航与跟随多自主水下航行器(AUV)间相对距离量测信息进行协同定位,可有效提高多AUV编队整体定位精度.针对多AUV编队队形对协同定位性能所造成的影响,利用Fisher信息矩阵(FIM)行列式的对数构建协同定位性能评价函数,并通过评价函数的最大化实现编队队形的优化.分析并验证多AUV编队含有两个跟随AUV及两个以上跟随AUV共圆情况下的最优队形,并利用梯度下降算法进行迭代搜索,从而获得两个以上跟随AUV不共圆情况下的优化队形.最后,通过仿真实验结果验证结论的正确性及队形优化算法的有效性.     

多AUV协同导航问题的研究现状与进展

自主水下航行器(Autonomous underwater vehicle, AUV)协同导航是未来50年解决水下中间层区域AUV导航定位的重要方法. 本文针对多AUV协同导航, 对该领域相关问题的研究进展进行了综述, 包括: 1)论述多AUV协同导航领域的研究现状, 包括协同导航问题界定、特点综述与讨论; 2)分析多AUV协同导航系统模型及相关算法的研究进展, 包括基于优化的、 基于参数估计的和基于贝叶斯估计的滤波算法; 3)对协同导航网络下的误差建模与补偿方法的研究进展进行了综述, 包括未知洋流的影响、水声通信延迟补偿等; 4)从影响协同导航定位精度的角度出发, 对AUV协同导航的可观测性与编队最优构型设计的研究进展进行了一系列的分析; 5)陈述目前多AUV协同导航中存在的关键问题, 并讨论其发展趋势.     

利用FastSLAM框架的多自治水下航行器同时定位与跟踪算法

协同定位是共融机器人研究领域的重要问题.协同定位方案的制定受限于机器人间信息交互的能力.针对长时间通讯中断时多自治水下航行器(AUV)协同定位精度明显下降的问题,借鉴同时定位与制图(SLAM)方法,提出了基于FastSLAM框架的同时定位与跟踪(SLAT)算法.将主AUV视为非合作目标,在从AUV上建立起一个关于主AUV的运动估计器,利用从AUV上声呐传感器实时获取的相对量测信息,在对主AUV运动状态估计的同时,完成对从AUV自定位精度的提升.仿真实验结果表明,在长时间通讯中断发生的条件约束下,相比于传统的航位推算方法,所提出的SLATF1.0和2.0算法能够有效减小定位误差,2.0算法对于探测精度变化等因素的影响具有更好适应性.     

异时量测序贯处理的多AUV协同导航

利用多个体之间的相对信息提高个体的定位精度的协同导航是一个很值得探讨的问题。自治水下航行器（AUV）在移动长基线（MLBL）网络中获取各应答器的位置信息并解算出相对各应答器的距离量测后,再通过贝叶斯滤波算法集中式提高其定位精度。但是,集中式利用量测的方法没有考虑由于水下环境的宽广性和水下传感器的限制导致的声信号在AUV和应答器间的往返时间内AUV的移动和应答器间位置的差异所带来的影响。首先以并行滤波的形式总结了集中式扩展卡尔曼滤波（EKF）协同导航方法,并针对集中式利用量测的不利因素,提出了按照异时量测的产生顺序即时更新AUV状态的更有效的序贯EKF协同导航算法,最后在仿真中将两种处理方法进行了比较。     

AUV同时定位与跟踪研究

由于低精度航位推算系统带来的累积误差,自治水下航行器（AUV）在未知环境中的定位准确性会随着时间的推移变得越来越差。当环境中有非合作体运动时,AUV不但可利用自身携带的声纳传感器对其探测并利用基于模型的目标跟踪方法估计非合作体轨迹,而且可以同时利用探测到的与非合作体的相对信息来提高自身定位精度。在同时定位与制图（SLAM）方法中的FastSLAM算法框架的基础上提出了同时定位与跟踪（SLAT）算法,即设置多个估计粒子,利用每个粒子中的粒子滤波器（PF）和扩展卡尔曼滤波器（EKF）分别估计AUV和非合作体的轨迹,并能根据实际量测与粒子估计量测之间的差别赋予粒子权值,继而得到多粒子加权的最终估计。最后仿真验证了算法的有效性。     

多自治水下机器人多任务分配的自组织算法

针对自治水下机器人（AUV）研究中的多机器人多任务分配问题,提出一种基于自组织映射（SOM）神经网络的多AUV多目标分配策略.将目标点的位置坐标作为SOM神经网络的输入向量进行自组织竞争计算,输出为对应的AUV机器人,从而控制一组AUV在不同的地点完成不同的任务,使机器人按照优化的路径规则到达指定的目标位置.为了表明所提出算法的有效性,给出了二维、三维作业环境中的仿真实验结果.     

Multi-AUV SOM task allocation algorithm considering initial orientation and ocean current environment

There is an ocean current in the actual underwater working environment. An improved self-organizing neural network task allocation model of multiple autonomous underwater vehicles (AUVs) is proposed for a three-dimensional underwater workspace in the ocean current. Each AUV in the model will be competed, and the shortest path under an ocean current and different azimuths will be selected for task assignment and path planning while guaranteeing the least total consumption. First, the initial position and orientation of each AUV are determined. The velocity and azimuths of the constant ocean current are determined. Then the AUV task assignment problem in the constant ocean current environment is considered. The AUV that has the shortest path is selected for task assignment and path planning. Finally, to prove the effectiveness of the proposed method, simulation results are given.

     

AUV 水下着陆策略研究

水下自航行器（AUV）的续航能力主要取决于其所携带能源总量．为了有效地减少能耗,提出了 一种具有变浮力系统、能够着陆坐底的小型AUV,它可以利用有限的能源实现长时间的海洋环境监测．首先 介绍了该AUV 的总体结构,然后对AUV 的着陆策略进行了详细研究．在对三种着陆策略进行对比的基础上, 选择下潜航行到位控制注水着陆策略作为最佳的水下着陆方案．该策略能够使着陆时的冲击力保持在安全范 围内,并且着陆时间最短．最后对着陆轨迹进行规划．仿真和水域实验的结果都证明采用该着陆策略能够安 全、平稳地实现水下着陆．     

Underwater guidance of distributed autonomous underwater vehicles using one leader

Consider the case where autonomous underwater vehicles (AUVs) are deployed to monitor a 3D underwater environment. This paper tackles the problem of guiding all AUVs to the destination while not colliding with a priori unknown 3D obstacles. Suppose that among all AUVs, only the leader AUV has an ability of locating itself, while accessing a destination location. A follower, an AUV that is not a leader, has no sensors for locating itself. Every follower can only measure the relative position of its neighbor AUVs utilizing its sonar sensors. Our paper addresses distributed controls, so that multiple followers track the leader while preserving communication connectivity. We design controls, so that all AUVs reach the destination safely, while maintaining connectivity in cluttered 3D environments. To the best of our knowledge, our article is novel in developing 3D underwater guidance controls, so that all AUVs equipped with sonar sensors are guided to reach a destination in a priori unknown cluttered environments. MATLAB simulations are used to validate the proposed guidance scheme in underwater environments with many obstacles.     

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.     

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.     

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

We present a cooperative bathymetry-based localization approach for a team of low-cost autonomous underwater vehicles (AUVs), each equipped only with a single-beam altimeter, a depth sensor and an acoustic modem. The localization of the individual AUV is achieved via fully decentralized particle filtering, with the local filter’s measurement model driven by the AUV’s altimeter measurements and ranging information obtained through inter-vehicle communication. We perform empirical analysis on the factors that affect the filter performance. Simulation studies using randomly generated trajectories as well as trajectories executed by the AUVs during field experiments successfully demonstrate the feasibility of the technique. The proposed cooperative localization technique has the potential to prolong AUV mission time, and thus open the door for long-term autonomy underwater.     

基于多个自主水下航行器的分布式协同流场估计

本文考虑利用多个自主式水下航行器(AUV)实现流场估计, 提出了一种基于树型网络的分布式方法来估计 水下流场. 在本文中, 借助绝对运动积分误差和相对运动积分误差, 流场估计问题被描述为求解一个以未知流场为 变元的非线性方程组. 继而本文在多AUV系统内建立一个低通讯成本的树型网络, 并在该网络上运行一种分布式 算法以求解与流场估计相关的非线性方程组. 在该算法中, 每个AUV将当前的流场估计值连续地投影到自身拥有 的约束方程的解集中, 并通过扩散和池化两个步骤在树型网络间传递流场估计值. 本文证明了上述算法的收敛性, 并通过仿真实验验证了所述分布式协同流场估计方法的有效性.