基于视觉处理的Tripod机器人的几何定位与颜色识别

文章旨在基于贝加莱Tripod机器人工作平台,进行机器人视觉的研究,给机器人增添不可或缺的眼睛。通过捕捉平台上图像传给计算机进行处理,进行高斯滤波、灰度化、二值化、霍夫变换等一系列操作实现几何体的几何定位与颜色识别。实验结果表明,该视觉系统实现了Tripod机器人对于物体的精准定位和颜色辨识。     

超宽带定位研究与应用:回顾和展望

 超宽带(Ultra-Wideband,UWB)技术能获得比现有无线定位技术更高的测距定位精度.本文主要讨论UWB定位技术的研究和应用,包括TOA/TDOA(Time/Time Difference of Arrival)等UWB定位方法、多径时延估计理论、非视距定位和协作式定位、多带OFDM(Orthogonal Frequency Division Multiplexing)定位和其他超宽带信号定位方式等方面,对其发展历程和现状进行了充分的叙述和分析,最后指出了仍存在的问题和值得进一步探讨的方向.     

基于仿真机器人的双目视觉定位

为了提升仿真机器人的视觉定位功能,对基于机器人系统的双目视觉定位功能的实现进行了研究.首先通过实验求解摄像机镜头内外参数,通过软件仿真验证算法和标定结果的正确性,然后将内参数配置到仿真机器人平台的软件系统中,在此系统中实现双目视觉定位功能.实验结果表明,仿真机器人的双日视觉定位结果精确,提升了机器人视觉系统的定位功能.     

自恢复蒙特卡罗定位算法

机器人定位技术作为智能机器人领域的重要技术,是机器人进行自主规划和导航的重要前提。为解决机器人运动过程中的绑架问题,在蒙特卡罗定位(Monte Carlo localization, MCL)算法的基础上,提出了基于激光雷达似然域模型的定位可靠度评判算法以及基于惯性导航单元的定位自恢复模型。定位可靠度评判算法对机器人是否发生绑架问题进行判定,当发生绑架问题后,首先基于惯性导航单元的测量数据进行位姿预估计,然后基于预估计的位姿构建粒子重分布模型,最后进行粒子滤波得到重定位的结果,达到了对机器人绑架判定和自恢复定位的目的。经过实验测试和对比,该算法可以对绑架问题进行高效的判断,具有更高的恢复效率和准确度。     

基于改进SURF算法的双目视觉定位

随着机器人技术的发展,机器人视觉方面的研究也越来越受到人们的重视。在机器人视觉系统中,双目视觉应用最为广泛。在利用双目视觉对物体进行定位时,文中采用了各方面性能都较有优势的SURF算法,来对图像中的特征点进行提取与匹配。由于客观因素的影响,在SURF匹配过程中存在特征点误匹配现象,为了消除误匹配,文中对SURF算法做了改进,加入了剔除误匹配的RANSAC算法。实验结果表明,改进后的SURF算法,能够大大提高双目视觉定位的精准度。     

基于激光定位的机器人加工轨迹规划

随着机械化生产的逐渐实现,加工机器人的应用范围越来越广泛。在此背景下,为提高机器人加工精度,针对基于机器视觉、COMS工业相机以及CAM的三种机器人加工轨迹规划方法无法准确跟踪给定轨迹,导致规划轨迹与期望轨迹存在较大误差的问题,提出一种基于激光定位的机器人加工轨迹规划方法。该方法首先利用激光雷达方法测量机器人与待加工目标之间的相关信息,然后利用激光跟踪仪进行误差标定,最后将误差标定结果作为改进蚁群算法迭代条件,通过多次迭代生成机器人加工轨迹。实验测试结果表明:与基于机器视觉的规划方法、基于COMS工业相机的规划方法以及基于CAM的规划方法相比,基于激光定位的机器人加工轨迹规划方法应用下,规划轨迹与期望轨迹拟合优度提高0. 07、0. 05、0. 08,轨迹定位误差较小,基本达到预期目标。     

自主足球机器人的单目视觉自定位方法

全自主机器人足球系统中，每个自主足球机器人都是一个独立的智能机器人，有自己的决策、通信、视觉和感知、机电控制等子系统，但是系统不具备全局视觉。为了使系统能够了解场上全局信息，足球机器人的自定位是必须解决的关键问题之一。文章介绍了一种在颜色场地信息已经视觉识别的前提下，通过单目视觉测距，实现全自主足球机器人自定位的几何方法：对摄像机CCD像面的量化、镜头的非线性畸变和摄像机定标等可能引起误差的因素进行了分析．实验结果表明方法可行。     

一种手眼机器人视觉伺服控制方法的设计

针对手眼机器人视觉伺服系统中的末端摄像机速度确定问题,文章采用一般模型法,通过求解最优化控制问题来设计控制器,即摄像机的速度,基于图像误差实现了定位控制,该方法能够使机器人在工作空间内从任一初始位置运动到期望位置,确保了系统全局渐近稳定且不需要物体的几何模型.最后给出的仿真实例表明了本方法的有效性.     

基于刚性约束的双移动机器人协同定位

准确、快速的状态估计是保证多机器人顺利完成协作搬运任务的关键.然而,大部分现有多机器人协同定位方法都存在一定的局限性,往往无法同时兼顾定位精度与计算复杂度.因此,本文从协作搬运任务的特点出发,将距离与方位的刚性约束条件引入协同定位中,同时根据机器人之间的紧密耦合关系建立起通用有效的运动模型和量测模型.最终在此刚性约束系统建模的基础上,提出一种基于高斯-厄米特求积分卡尔曼滤波（Quadrature Kalman Filter,QKF）的双移动机器人协同定位方法.仿真实验结果表明：与基于无约束模型的QKF协同定位方法相比,本文所提方法不但具有更高的定位精度,而且计算复杂度大大降低,有助于实现多机器人实时协同定位.     

动态环境下基于深度学习的视觉SLAM研究综述

目前的同时定位与地图构建(Simultaneous Localization and Mapping,SLAM)研究大多是基于静态场景的假设,而实际生活中动态物体是不可避免的。在视觉SLAM系统中加入深度学习,可以协同剔除场景中的动态物体,有效提升视觉SLAM在动态环境下的鲁棒性。文章首先介绍了动态环境下基于深度学习的视觉SLAM分类,然后详细介绍了基于目标检测、基于语义分割和基于实例分割的视觉SLAM,并对它们进行了分析比较。最后,结合近年来视觉SLAM的发展趋势,通过对动态环境下基于深度学习的视觉SLAM存在的主要问题进行分析,总结了未来可能的发展方向。     

一种基于网络密度分簇的移动信标辅助定位方法

现有移动信标辅助定位算法未充分利用网络节点分布信息,存在移动路径过长及信标利用率较低等问题。该文把网络节点分簇、增量定位与移动信标辅助相结合,提出了一种基于网络密度分簇的移动信标辅助定位算法(MBL(ndc))。该算法选择核心密度较大的节点作簇头,采用基于密度可达性的分簇机制把整个网络划分为多个簇内密度相等的簇,并联合使用基于遗传算法的簇头全局路径规划和基于正六边形的簇内局部路径规划方法,得到信标的优化移动路径。当簇头及附近节点完成定位后,升级为信标,采用增量定位方式参与网络其它节点的定位。仿真结果表明,该算法定位精度与基于HILBERT路径的移动信标辅助定位算法相当,而路径长度不到后者的50％。     

Sensitivity-based data fusion for optical localization of a mobile robot

Acoustic-based techniques are the standard for localization and communication in underwater environments, but due to the challenges associated with this medium, it is becoming increasingly popular to find alternatives such as using optics. In our prior work we developed an LED-based Simultaneous Localization and Communication (SLAC) approach that used the bearing angles, needed for establishing optical line-of-sight for LED-based communication between two beacon nodes and a mobile robot, to triangulate and thereby localize the position of the robot. Our focus in this paper is on how to optimally fuse measurement data for optical localization in a network with multiple pairs of beacon nodes to obtain the target location. We propose the use of a sensitivity metric, designed to characterize the level of uncertainty in the position estimate with respect to the bearing angle error, to dynamically select a desired pair of beacon nodes. The proposed solution is evaluated with extensive simulation and experimentation, in a setting of three beacons nodes and one mobile node. Comparison with multiple alternative approaches demonstrates the efficacy of the proposed approach.     

Reference node placement and selection algorithm based on trilateration for indoor sensor networks

The key problem of location service in indoor sensor networks is to quickly and precisely acquire the position information of mobile nodes. Due to resource limitation of the sensor nodes, some of the traditional positioning algorithms, such as two‐phase positioning (TPP) algorithm, are too complicated to be implemented and they cannot provide the real‐time localization of the mobile node. We analyze the localization error, which is produced when one tries to estimate the mobile node using trilateration method in the localization process. We draw the conclusion that the localization error is the least when three reference nodes form an equilateral triangle. Therefore, we improve the TPP algorithm and propose reference node selection algorithm based on trilateration (RNST), which can provide real‐time localization service for the mobile nodes. Our proposed algorithm is verified by the simulation experiment. Based on the analysis of the acquired data and comparison with that of the TPP algorithm, we conclude that our algorithm can meet real‐time localization requirement of the mobile nodes in an indoor environment, and make the localization error less than that of the traditional algorithm; therefore our proposed algorithm can effectively solve the real‐time localization problem of the mobile nodes in indoor sensor networks. Copyright © 2008 John Wiley & Sons, Ltd.     

Mobile anchor assisted particle swarm optimization (PSO) based localization algorithms for wireless sensor networks

Node localization is essential to wireless sensor networks (WSN) and its applications. In this paper, we propose a particle swarm optimization (PSO) based localization algorithm (PLA) for WSNs with one or more mobile anchors. In PLA, each mobile anchor broadcasts beacons periodically, and sensor nodes locate themselves upon the receipt of multiple such messages. PLA does not require anchors to move along an optimized or a pre‐determined path. This property makes it suitable for WSN applications in which data‐collection and network management are undertaken by mobile data sinks with known locations. To the best of our knowledge, this is the first time that PSO is used in range‐free localization in a WSN with mobile anchors. We further derive the upper bound on the localization error using Centroid method and PLA. Simulation results show that PLA can achieve high performance in various scenarios. Copyright © 2011 John Wiley & Sons, Ltd.     

无线自组网节点定位算法综述

节点移动性使无线自组网络在军事、医学、环境保护等领域展现出广阔的应用前景。然而随着人们对移动节点位置信息的需求,节点定位问题也成为一个研究的热点问题,到目前为止,己有许多有关无线自组网的自定位系统和算法。在对这些定位算法进行分类的基础上,把节点定位过程分为4个基本步骤,着重分析阐述各类申具有代表性的定位算法的原理和各自特点,并提出一个对定位算-法I陛能评价的标准。     

Moving anchor node localization algorithm based on network connectivity

In order to better solve the contradiction between precision of localization and the number of anchor nodes in wireless sensor network,a mobile anchor node localization technology based on connectivity was proposed.First,the coverage characteristic of the network nodes was analyzed,and a critical value was found between the mobile step and the anchor node communication radius,mobile anchor nodes＇ coverage characteristic would change when near this critical value.Second,a mobile anchor node followed a planning path to form a positioning area seamless coverage was used.Finally,when there was no need for high-precision technology,node position would been estimated according with the connectivity of the network and the receiving information of the node.The simulation results show that the proposed algorithm can realize coarse-grained localization,and paths perform complete localization.     

无线传感器网络中基于刚性的移动锚节点路径规划

在移动锚节点辅助定位中,锚节点通常沿着预设轨迹扫描或采用随机方式移动,当节点分布不均匀、待定位区域形状复杂时,存在锚节点冗余移动、位置冗余广播及定位性能难以确保等问题。该文提出了利用网络拓扑信息进行移动路径规划的新思路,首先利用刚性理论将网络划分为多个唯一可定位的单元,之后寻找一条遍历各单元的最短路径作为锚节点的移动轨迹,锚节点沿着该轨迹移动、依次定位各单元。大规模仿真结果表明,该策略可以有效避免锚节点的冗余移动和位置广播,灵巧适应各种布设情形,与经典的扫描式路径相比,大大降低了路径长度和位置数目。     

Seamless Routing and Cooperative Localization of Multiple Mobile Robots for Search and Rescue Application

In particular, for a practical mobile robot team to perform such a task as that of carrying out a search and rescue mission in a disaster area, the network connectivity and localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a Global Positioning System is unavailable. This paper proposes the new collective intelligence network management architecture of multiple mobile robots supporting seamless network connectivity and cooperative localization. The proposed architecture includes a resource manager that makes the robots move around and not disconnect from the network link by considering the strength of the network signal and link quality. The location manager in the architecture supports localizing robots seamlessly by finding the relative locations of the robots as they move from a global outdoor environment to a local indoor position. The proposed schemes assuring network connectivity and localization were validated through numerical simulations and applied to a search and rescue robot team.     

Monte Carlo localization for mobile wireless sensor networks

Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors’ radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy.     

Distributed localization using mobile beacons in wireless sensor networks

A new distributed node localization algorithm named mobile beacons-improved particle filter (MB-IPF) was proposed. In the algorithm, the mobile nodes equipped with globe position system (GPS) move around in the wireless sensor network (WSN) field based on the Gauss-Markov mobility model, and periodically broadcast the beacon messages. Each unknown node estimates its location in a fully distributed mode based on the received mobile beacons. The localization algorithm is based on the IPF and several refinements, including the proposed weighted centroid algorithm, the residual resampling algorithm, and the markov chain monte carlo (MCMC) method etc., which were also introduced for performance improvement. The simulation results show that our proposed algorithm is efficient for most applications.