Virtual local target method for avoiding local minimum in potential field based robot navigation

A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.

Source localization with minimum variance distortionless response for spherical microphone arrays

To improve localization accuracy, the spherical microphone arrays are used to capture high-order wavefield information. For the far field sound sources, the array signal model is constructed based on plane wave decomposition. The spatial spectrum function is calculated by minimum variance distortionless response (MVDR) to scan the three-dimensional space. The peak values of the spectrum function correspond to the directions of multiple sound sources. A diagonal loading method is adopted to solve the ill-conditioned cross spectrum matrix of the received signals. The loading level depends on the alleviation of the ill-condition of the matrix and the accuracy of the inverse calculation. Compared with plane wave decomposition method, our proposed localization algorithm can acquire high spatial resolution and better estimation for multiple sound source directions, especially in low signal to noise ratio (SNR).

Locally Minimum Storage Regenerating Codes in Distributed Cloud Storage Systems

In distributed cloud storage systems, inevitably there exist multiple node failures at the same time. The existing methods of regenerating codes, including minimum storage regenerating(MSR) codes and minimum bandwidth regenerating(MBR) codes, are mainly to repair one single or several failed nodes, unable to meet the repair need of distributed cloud storage systems. In this paper, we present locally minimum storage regenerating(LMSR) codes to recover multiple failed nodes at the same time. Specifically, the nodes in distributed cloud storage systems are divided into multiple local groups, and in each local group(4, 2) or(5, 3) MSR codes are constructed. Moreover, the grouping method of storage nodes and the repairing process of failed nodes in local groups are studied. Theoretical analysis shows that LMSR codes can achieve the same storage overhead as MSR codes. Furthermore, we verify by means of simulation that, compared with MSR codes, LMSR codes can reduce the repair bandwidth and disk I/O overhead effectively.

改进的局部最小像素先验遥感图像盲复原算法

为了解决遥感图像盲复原时模糊核估计不准确、复原图像存在振铃效应的问题,提出改进的局部最小像素先验遥感图像盲复原算法。该算法首先引入极端通道先验与局部最小像素先验结合,对图像的强度进行更好的约束,有利于得到更好的潜在清晰图像;然后采用基于梯度的方法估计模糊核,模糊核估计与中间潜在清晰图像估计交替迭代进行,获得较为理想的模糊核;最后引入联合双边滤波器,采用改进的拉普拉斯与正则化图像复原算法抑制图像复原的振铃效应。实验结果表明,本文方法对遥感图像复原效果较好,恢复的图像边缘清晰,振铃伪影得到抑制且模糊核较为理想;客观评价指标峰值信噪比(PSNR)较前沿复原算法平均提高约1.40 dB,结构相似度(SSIM)平均提高约0.02。

基于人工磁场的防疫机器人路径规划

针对传统人工势场法在防疫机器人路径规划过程中容易陷入局部极小值和目标不可达的问题,提出一种基于人工磁场的路径规划算法。首先,对障碍物周围建立人工磁场,引入洛伦兹力,洛伦兹力与引力保持垂直,避免机器人陷入局部极小值点;其次,在洛伦兹力函数中引入机器人与目标点的距离影响因子,解决目标不可达问题;再次,通过建立目标点与障碍物之间的虚拟直线优化洛伦兹力的方向,避免迂回路径,减少路径规划的步数。最后,在MATLAB环境下对传统人工势场法和人工磁场法进行了仿真实验,实验结果表明,人工磁场法可以克服局部极小值和目标不可达问题,规划的路径对障碍物进行了避障,避免了徘徊振荡的情况出现,提高了路径规划的质量。

改进人工势场法解决局部最小值路径规划研究

当移动机器人在行进过程中使用传统人工势场法(artificial potential field method, APF)进行路径规划时,通常会陷入局部最优困境,无法顺利到达目标点。为解决这一问题,首先,对APF算法规划路径失败原因进行分析,其次设置情况判断条件,判断当机器人陷入局部最小值时,通过在合适位置增加临时引导点的方法,引导其跳出局部极小值点;其次,引入分数阶微积分思想方法结合APF算法,提出混合阶次的分数阶梯度下降法进行位置信息迭代,优化算法的收敛速度和收敛精度;最后,用MATLAB软件对该方法进行仿真,实验结果表明提出的方法可以有效解决局部最小值问题,而且在速度、精度上都有明显的提高,且能适应较为复杂的多障碍物环境,验证了改进方法的有效性、正确性。

动态环境下自主机器人的双机制切向避障

针对机器人工作环境的动态随机性,提出面向双机制切向避障的改进人工势场法.针对传统人工势场法的局部极小值陷阱问题,提出静态避障机制,在规划开始前对地图进行预处理,预测局部极小值点并将障碍物分成连通与非连通障碍物,结合切向避障实现静态切向避障.以静态避障机制为基础,针对动态障碍物,提出动态避障机制,通过实时调整碰撞风险系数并选择系数最大的障碍物进行避障角补偿,实现动态切向避障.通过状态决策统筹静态、动态切向避障机制与全局路径规划,实现混合规划与设计.设计仿真和全向移动平台,对所提方法进行验证测试.结果表明,所提方法在不同环境复杂下均有效解决了传统人工势场法的局部极小值陷阱问题,实现了动态环境下快速自主避障.对比3种方法避开不同类型障碍物的平均耗时,所提方法比动态窗口法(DWA)提升55%,比时间弹性带法(TEB)提升40%;对比3种方法导航不同复杂度地图的平均耗时,所提方法比DWA提升39%,比TEB提升22%.

车载毫米波雷达多径假目标分析与消除方法

针对城市道路环境下车载毫米波雷达存在的多径假目标干扰真实目标定位的问题,提出了一种多径假目标消除方法,以实现真实目标的准确定位。首先分析了简化的城市道路多径场景的电磁传播路径并建立回波模型,然后应用恒虚警率检测方法及最小方差无失真响应测角定位算法得到目标的初始定位结果,最后通过理论分析多径假目标空间位置分布规律并将初始定位点迹与多径假目标进行匹配消除,保留了真实目标定位点迹。实测试验结果表明,所提出的多径假目标消除方法能够有效消除假目标定位点,获得真实目标的定位结果。

两种高效局部搜索算法求解RB模型实例

RB(revised B)模型是一种在约束可满足问题中具备精确相变增长域的随机实例模型,提出两种高效的启发式局部搜索算法用于解决RB模型生成的大值域约束可满足问题。首先为基于权重指导搜索的W-MCH算法,该算法通过约束判断和违反约束数计分来进行搜索,并引入了基于约束违反概率的权重计算公式,根据其关联的约束权重进行修正,再对变量进行迭代调整。然后提出最小化值域的MDMCH算法,该算法通过记录违反约束和逐步消除已违反约束变量的启发式策略来减少搜索空间,并在最小化后的变量域内重新校准变量赋值,进而有效提高算法的收敛速度。此外,还提出了融入模拟退火策略的WSCH和MDSCH算法,这两种算法都能根据变量的表征特点对变量域进行针对性的搜索。实验结果表明,与多种启发式算法相比,这两种算法在精度与时间效率方面均呈现明显提升,在复杂难解的实例中能够提供高效的求解效率,验证了算法的有效性和优越性。

基于改进U-Net的小型机械零件识别与定位方法研究

针对基于机器视觉的小型机械零件识别速度慢、定位不精确等问题,文章提出一种改进UNet(improve U-Net,IU-Net)和最小外接矩阵(minimum bounding rectangle,MBR)结合的小型机械零件识别和定位方法(IU-Net-MBR)。首先,搭建视觉分拣试验平台,制作小型机械零件数据集;其次,为了提高特征提取效率,将U-Net的特征提取网络替换成轻量级MobilenetV2网络,降低模型的参数和计算量;然后,为了提高U-Net的分割精度和鲁棒性,在网络结构中引入SE(squeeze and excitation)注意力模块;最后,使用最小外接矩阵得到零件的长宽基本参数,实现零件的识别和定位。试验表明,IU-Net相对于U-Net在平均交并比Miou(mean intersection over union)和像素准确率PA(pixel accuracy)分别提高4.39%和3.82%。在处理图像时,IU-Net相对于U-Net速度提升76.92%。与主流分割模型相比,IU-Net实现了更好的分割效果,有效地提高了小型机械零件的分割精度。在抓取试验中,IU-Net-MBR在识别率和抓取率上分别达到了100%和96.67%。

基于正交拉丁方的局部修复码构造

针对目前具有(r,t)局部性的局部修复码码率较低且构造过程中计算复杂度过高的问题,提出基于正交拉丁方的二元局部修复码构造方法.根据正交拉丁方元素与矩阵位置的对应关系构造关联矩阵,得到具有全符号局部性的局部修复码(AS-LRCs),该码的码率和码长渐近边界条件,且最小距离较大.利用关联矩阵级联单位矩阵构造信息位具有(r,t=2)局部性的单校验局部修复码,该码的最小距离和码率均满足最优边界条件,为最优局部修复码.考虑到实际分布式存储系统中存在高故障率节点,利用正交拉丁方完备组构造具有信息位局部性的高可用性单校验局部修复码(IS-LRCs),可以灵活选择可用性t,提高了系统的鲁棒性与灵活性.

A self-organized locator obtaining method driven by local minima

The scalability of routing architectures for large networks is one of the biggest challenges that the Internet faces today.Greedy routing,in which each node is assigned a locator used as a distance metric,recently received increased attention from researchers and is considered as a potential solution for scalable routing.In this paper,LMD—a local minimum driven method is proposed to compute the topology-based locator.To eliminate the negative effect of the " quasi" greedy property—transfer routes longer than the shortest routes,a two-stage routing strategy is introduced,which combines the greedy routing with source routing.The greedy routing path discovered and compressed in the first stage is then used by the following source-routing stage.Through extensive evaluations,based on synthetic topologies as well as on a snapshot of the real Internet AS(autonomous system)topology,it is shown that LMD guarantees 100%delivery rate on large networks with low stretch.

Restraint on the Effect of Friction by Localization Method

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基于U-Net网络的FWI地震低频恢复方法

由于实际地震资料中缺乏低频数据,使得全波形反演易陷入局部极小值,导致反演质量差,结果不可靠。为解决这一问题,本文利用数据驱动低频恢复映射思想,分别利用高通和低通滤波器从原始数据中分离出高频数据和低频数据,对其进行一系列数据预处理操作,将处理后的数据作为模型的训练集;利用U-Net网络为基础构建模型,建立高低频之间的映射关系。为了有效防止模型过拟合,本文在U-Net模型基础上添加了Dropout层和批处理层。利用训练后的模型从高频数据中预测对应低频数据并进行逆数据预处理,对比分析逆数据预处理后的预测低频数据和真实低频数据之间误差,并利用多尺度全波形反演在洼陷模型和Marmousi模型进行有效性验证。实验结果表明:训练与测试数据的预测低频与真实低频数据的平均相对误差为5.02%和13.32%,误差较小,数据吻合良好;洼陷模型、Marmousi模型以及实际数据的反演结果表明加入预测低频后反演质量得到显著提高,并且对处理含较大噪声的数据也有很好的效果。

Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

基于RBIBD的最优局部修复码构造

随着数据量的迅速增长,对存储海量数据的分布式存储系统的可靠性和有效性的要求日益增加。局部修复码(LRCs)具有良好的修复局部性,能够有效实现海量数据在分布式存储系统中的可靠高效存储,构造具有(r,t)局部性的局部修复码已经成为当前研究的热点。为此,提出了一种基于可分解均衡不完全区组设计(RBIBD)的最优局部修复码的构造方法,构造信息位具有(r,t)局部性的二元最优单校验LRCs。性能分析表明,构造的LRCs达到了最小距离最优边界,且在码率上表现得更优。

基于人工势场算法的路径规划研究

人工势场法作为一种经典局部避障算法,被广泛用于自主移动机器人的局部路径规划,具有原理简单、数学模型简洁、规划路径平滑以及计算速度快等优势;但算法本身存在着局部极小值、目标不可达以及复杂环境下路径规划能力不足等问题。因此,文章对局部极小值、目标不可达,以及复杂环境下路径规划能力不足问题进行阐述,并且归纳总结算法固有缺陷问题的改进策略和复杂环境下的规划能力不足问题的改善策略,分析了路径规划所得路径的质量。

Analyzing the Completeness(Relative Minimum Magnitude) of History Seismic Catalogue in Sichuan Province

Sichuan Province,East Western of China,within the area of east longitude 97°21′-108°31′,north latitude 26°03′-34°19′,which is on the Mediterranean seismic zone;with its complicated geological background,several strong shocks occurred in this district since ancient times.Earthquake catalogue of this area could date back to B.C.26,however,limited by the conditions—technology or funds,lots of minor earthquake have been lost,which could be

基于改进人工势场法的无人船路径规划

针对传统人工势场法无人船路径规划中无法到达目标点、局部极小值、路径振荡的问题,提出了一种改进人工势场法。修改斥力函数,解决无人船无法到达目标点问题;施加艏向角限制,增加随机扰动,解决无人船存在的两种局部极小值问题;进行路径简化,解决无人船路径振荡的问题。将改进人工势场法与传统人工势场法进行仿真比较,结果证明,改进人工势场法可以有效地克服无法到达目标点的问题,并能够在陷入局部极小值之后成功跳出,路径振荡的问题也得到改善。可为无人船的路径规划技术研究提供更多思路。

基于改进人工势场法的移动机器人局部路径规划

为解决传统人工势场法在移动机器人局部路径规划中存在的缺陷,提高其路径规划的性能,提出了改进的人工势场法。将引力作用阈值引入引力势场函数,解决引力过大问题;在斥力势场函数中引入目标点与移动机器人之间的距离,解决目标不可达问题;根据环境复杂度,提出了自适应速度调节机制;针对局部极小值问题,分别提出了APF-v1和APF-v2两种构建虚拟目标点的方法,引导移动机器人走出陷阱区域。最后在ROS机器人操作系统中对改进的算法进行了对比实验,结果表明,改进的算法可以克服目标不可达、局部极小值等问题,并且在计算量、路径规划时间和步数等方面具有一定的优越性。