A novel robust AE/MS source location method using optimized M-estimate consensus sample

Due to the complexity of the real engineering environment, the arrival measurement inevitably contains outliers and leads to serious location errors. In order to eliminate the influence of the outliers effectively,this paper proposes a novel robust AE/MS source localization method using optimized M-estimate consensus sample. First, a sample subset is selected from the entire arrival set to obtain fitting model and its parameters. Second, consensus set is determined by checking the arrivals with the fitting model instantiated by the estimated model parameters. Third, optimization process is performed to further optimize the consensus set. The above steps are iterated, and the final source coordinates are obtained by using all the elements in the optimal consensus set. The novel method is validated by a pencil-lead breaks experiment. The results indicate that the novel method has better location accuracy of less than 5 mm compared to existing methods, regardless of the presence or absence of outliers. With the increase of outlier scale and outlier ratio, the location result of the proposed method is always more stable and accurate than that of the existing methods. Mine blasting experiments further demonstrate that the new method holds good prospects for engineering applications.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with general sampling delay

In this paper we provide a unified framework for consensus tracking of leader-follower multi-agent systems with measurement noises based on sampled data with a general sampling delay. First, a stochastic bounded consensus tracking protocol based on sampled data with a general sampling delay is presented by employing the delay decomposition technique. Then, necessary and sufficient conditions are derived for guaranteeing leader-follower multi-agent systems with measurement noises and a time-varying reference state to achieve mean square bounded consensus tracking. The obtained results cover no sampling delay, a small sampling delay and a large sampling delay as three special cases. Last, simulations are provided to demonstrate the effectiveness of the theoretical results.

Consensus for second-order multi-agent systems with position sampled data

In this paper, the consensus problem with position sampled data for second-order multi-agent systems is investigated.The interaction topology among the agents is depicted by a directed graph. The full-order and reduced-order observers with position sampled data are proposed, by which two kinds of sampled data-based consensus protocols are constructed. With the provided sampled protocols, the consensus convergence analysis of a continuous-time multi-agent system is equivalently transformed into that of a discrete-time system. Then, by using matrix theory and a sampled control analysis method, some sufficient and necessary consensus conditions based on the coupling parameters, spectrum of the Laplacian matrix and sampling period are obtained. While the sampling period tends to zero, our established necessary and sufficient conditions are degenerated to the continuous-time protocol case, which are consistent with the existing result for the continuous-time case. Finally, the effectiveness of our established results is illustrated by a simple simulation example.

Stochastic bounded consensus tracking of leader-follower multi-agent systems with measurement noises and sampled-data

This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore, the effects of the sampling period on the tracking performance are analysed. It turns out that from the view point of the sampling period, there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.

Consensus of heterogeneous multi-agent systems based on sampled data with a small sampling delay

In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay for heterogeneous multi-agent systems is proposed. Then, the algebra graph theory, the matrix method, the stability theory of linear systems, and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing heterogeneous multi-agent systems to asymptotically achieve the stationary consensus. Finally, simulations are performed to demonstrate the correctness of the theoretical results.

Sampled-Data Consensus Control of MUSV Systems with Channel Fading and Transmission Delay

This paper studies sampled-data consensus control of a collection of unmanned surface vehicles(USV)operating in network environments with fading channels and time-varying transmission delay.The channel fading is modeled as each independent stochastic process whose probability distribution is known.By considering the effects of channel fading and transmission delay from sampler to the controller,a new MUSV system model is formulated in the framework of network.With the novel established model,stability analysis is given at first,then the sampled-data consensus controller is designed,which also extends to the robust control with wave-induced disturbance.The effectiveness of the presented method is demonstrated by numerical simulation.

Asymptotic bounded consensus tracking of double-integrator multi-agent systems with bounded-jerk target based on sampled-data without velocity measurements

This paper investigates asymptotic bounded consensus tracking（ABCT） of double-integrator multi-agent systems（MASs） with an asymptotically-unbounded-acceleration and bounded-jerk target（AUABJT） available to partial agents based on sampled-data without velocity measurements. A sampled-data consensus tracking protocol（CTP） without velocity measurements is proposed to guarantee that double-integrator MASs track an AUABJT available to only partial agents.The eigenvalue analysis method together with the augmented matrix method is used to obtain the necessary and sufficient conditions for ABCT. A numerical example is provided to illustrate the effectiveness of theoretical results.

Bounded consensus tracking of second-order multi-agent systems with sampling delay under directed networks

The bounded consensus tracking problems of second-order multi-agent systems under directed networks with sam- pling delay are addressed in this paper. When the sampling delay is more than a sampling period, new protocols based on sampled-data control are proposed so that each agent can track the time-varying reference state of the virtual leader. By using the delay decomposition approach, the augmented matrix method, and the frequency domain analysis, necessary and sufficient conditions are obtained, which guarantee that the bounded consensus tracking is realized. Furthermore, some numerical simulations are presented to demonstrate the effectiveness of the theoretical results.

Distributed event-triggered consensus tracking of second-order multi-agent systems with a virtual leader

This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy.

基于点云特征的改进RANSAC地面分割算法

针对室外复杂场景下,轻量级和地面优化的激光雷达里程计与测图(LeGO-LOAM)算法由于地面分割不精确而导致算法定位精度降低的问题,提出一种基于改进随机一致性采样(RANSAC)的多线程地面分割算法:相较于传统RANSAC算法,该算法舍弃从全部原始数据中随机选取种子点拟合地面模型的迭代方式,首先利用点云高程、曲率等点特征信息挑选出所有小于高程、曲率等阈值的种子点以构建种子点集合,并根据种子点集合中的种子点数量判断是否需要多线程处理;然后根据判断结果从种子点集合中选择种子点子集进行地面拟合;最后比较各地面模型所包含的点云数量以获得最优地面模型参数以及地面点云集;地面分割精度的提高有效地降低了LeGO-LOAM算法的定位误差。实验结果表明,在室外复杂场景下所提出的地面分割算法分割效果更好,杂点更少;相较于原LeGO-LOAM算法,改进算法的定位误差降低至3.73 m,平面均方根误差降低了20.8%。

基于机器视觉的电芯绝缘介质定位算法

为保证电芯绝缘介质定位的准确性和时效性,文章提出一种基于双线性插值亚像素坐标结合改进随机抽样一致性(random sample consensus,RANSAC)算法的绝缘介质定位算法。对工业采集的图像进行空间滤波、阈值分割等预处理操作,分割出目标并增强特征;运用Canny边缘检测算子检测边缘,选取定位轮廓并根据最小外接矩形分离4条边缘直线,用双线性插值公式精确定位边缘直线亚像素坐标;采用部分点先验模型约束RANSAC算法提取4条边缘直线的高质量内点,再用最小二乘法分别拟合4条边缘直线并计算出相应偏移量。实验结果表明,该算法能有效保证绝缘介质的定位精度且具有一定的时效性,可以较好地满足实际生产应用的要求。

基于改进SURF的增强现实图像匹配方法

针对传统增强现实图像匹配算法鲁棒性不强且效率不高的问题,提出一种改进的SURF匹配算法。首先,使用SURF算法进行特征点检测,并通过Haar小波模板确定特征主方向,在得到特征主方向后构建特征描述符;由于传统SURF算法采用高达64维的矩形描述符,导致算法的计算量非常大,并且鲁棒性不强。因此,该文使用DAISY圆形描述符替代原始算法中的矩形描述符,DAISY是三层同心圆结构,每层包含8个采样点,可以得到25个维度的描述符,这种结构使得算法的鲁棒性大大增强并且降低了计算复杂度;接着,使用特征描述符计算欧氏距离进行特征点匹配;最后,对得到的匹配点集使用随机抽样一致(RANSAC)与三角不规则网络(TIN)算法进行优化,剔除误匹配点。实验结果表明,该算法虽然略微增加了时间复杂度,但鲁棒性变得更强,并且算法的效率和匹配精度也大大提高,平均精度达到了95%以上。

合成孔径声呐时延估计误差校正改进算法

由相位解缠错误造成的时延估计误差会严重降低合成孔径声呐时延估计的精度。现有的时延估计误差校正算法采用二次函数作为时延的拟合模型,该模型在距离向近端和远端处不符合时延空变规律,拟合误差较大且无法估计载体的横荡和升沉运动。针对该问题,文章提出一种改进的时延估计误差校正算法,利用时延的距离空变函数代替二次函数作为拟合模型。数值仿真和实验结果表明,相较于参考算法,改进算法校正时延估计误差的效果更好、速度更快,同时还能准确地估计载体的横荡和升沉运动。运动补偿结果显示了改进算法能较好地提升成像质量。

基于FPDE-SIFT的声呐干涉图像配准方法

图像配准是声呐进行高精度干涉测量的保障,该文针对水下目标的声呐图像配准,提出了一种基于4阶偏微分方程尺度不变特征变换的声呐干涉图像配准方法。该方法聚焦声呐图像配准的难点,首先基于4阶偏微分方程构建尺度空间,在保持图像细节的前提下滤除噪声,提高特征提取的准确度;对于残余噪声造成的特征点误检,借助特征点的相位一致性信息加以筛选,精简特征点样本集;最后对特征点匹配策略进行优化,提出改进的快速样本一致性匹配策略剔除特征点的误匹配。算法增加了匹配点对的数量,提高了匹配点对的准确度,实现了声呐干涉图像的精确配准。水池实验和外场试验表明,该文所提出的算法相较现有算法对声呐图像有着更好的适用性,配准后的均方根误差与留一法均方根均小于1像素,达到了亚像素配准精度。

多率采样机制下多智能体动态事件触发二分一致性研究

针对满足细节平衡结构的对抗交互多智能体系统,研究多率采样机制下其动态事件触发二分一致性问题。在多率采样机制下,通过构造一个多率缓存器解决多率采样时序不匹配的问题,设计一类多率观测器便于获得系统的估计状态以实现目标控制。通过引入动态事件触发机制,多率观测器在事件触发时刻广播其状态数据至通信网络中的邻居智能体。每个智能体基于触发时刻的观测器状态信息,利用一组开环估计器以获得连续的智能体状态估计值。在此基础上,设计一个分布式控制协议,利用代数黎卡提方程和李雅普诺夫稳定性理论证明系统可以在细节平衡的多智能体系统通信网络中实现二分一致性,并且排除动态事件触发可能导致的芝诺行为。通过一个包含3种不同采样机制和控制方案的对比仿真,证明了所提控制方案的有效性,表明多率采样机制相较于传统的单率采样机制具有更快和更稳定的收敛性能,并且动态事件触发相较于静态事件触发可以进一步降低触发次数。

基于投影约束和随机采样Hough变换的虹膜分割方法

非合作式虹膜图像的采集导致图像中具有大量干扰和噪声,因此,提出基于投影约束和随机采样Hough变换的虹膜分割方法提取虹膜有效区域。对虹膜二值图像进行投影粗定位虹膜的外轮廓圆心和半径,通过圆心和半径确定虹膜的大致区域,在该区域上,使用随机采样的Hough变换确定虹膜的内外轮廓,从而提高了虹膜分割的准确性。实验证明,与现有方法相比虹膜分割方法错误指标E1和E2最多提高8%和20%,具有更低的错误率和较高的时间效率。

采样数据交互的异构非完整轮式机器人集群连通保持切换编队控制

轮式移动机器人作为一种体积小巧、操作灵活的机器人,广泛应用于工业、农业等领域。考虑到实际情况中,机器人间能够互相交流信息的通讯范围通常是有限制的,且存在非线性、非完整约束等特点,因此针对通讯范围受限的轮式移动机器人编队控制问题的研究很有价值。面对复杂的任务和环境,需要多种机器人的配合,因此该文研究了异构的非完整轮式移动机器人集群,构建了一种采样数据交互模式。采样数据虚拟框架为实际机器人系统提供参考轨迹,降低了机器人之间的通讯要求,不再需要时刻保持通讯。采用含预设性能的控制方法设计了实际机器人的控制器,保证机器人的跟踪误差满足工程应用中期望的稳态性能与瞬态性能要求。最后,通过数值仿真和机器人操作系统(ROS)物理仿真实验验证了所设计控制协议的有效性。

基于三角形相似性的点云配准算法

针对点云配准过程易产生错误匹配对、点云配准精度低等问题,提出了一种基于三角形相似性的点云配准方法。三对正确的匹配点对在几何空间上形成的三角形一定满足相似三角形的一些性质,例如三角形三边成固定比例、三角形顶点的法线和平面法线的夹角固定。因此,可以利用这些性质有效地减少点云配准过程中错误匹配点对其影响。该方法首先计算点云中每个点的快速点特征直方图(FPFH)描述符,使用三角形相似性的方法进行特征匹配,通过奇异值(SVD)分解得到一个变换矩阵。实验结果表明,该方法与基于正态分布变换(NDT)算法与最近邻迭代(ICP)算法结合的点云配准算法相比,配准效率提升了15.3%,配准精度提升了18.2%。

基于选星预处理的改进随机抽样一致性RAIM方法

多星座导航能够增加可视卫星数量,改善卫星几何构型,已成为卫星导航定位领域发展的重要方向之一。多星座导航接收机自主完好性监测(RAIM)技术对提高导航系统的完好性具有重要作用。面向多星座导航的完好性监测需求,分析了传统随机抽样一致性(RANSAC)故障检测方法的不足,提出了一种基于最小样本集选星预处理的改进RANSACRAIM算法。该算法基于最大四面体积法和GDOP值贡献度的选星方法选取具有较好构型的卫星构成卫星子集,取代了传统RANSACRAIM方法通过遍历构成卫星子集,可有效避免卫星子集中存在较差卫星几何构型的情况,减少子集数量,提升故障检测的准确率。静态和动态仿真实验表明,改进的RANSACRAIM算法在检测效率和检测准确率等方面明显优于传统方法。