一种改进的无线传感器网络节点部署方案

现有的无线传感器网络节点部署方案需要额外探测和修复边界上的覆盖空洞,增加了网络部署的成本,且节点分布不均匀.为此,提出一种改进的节点部署方案.通过边界部署保证边界上的完全覆盖和连通,在感应区域内生成一个新的凸多边形区域,在新区域上递归调用边界部署算法,直到整个感应区域被完全覆盖.理论分析和仿真实验结果表明,该方案能够保证网络的1-连通性和完全覆盖,消除任务区域存在的边界效应,在部署节点个数和可扩展性方面均优于已有的部署方案.     

基于随机森林的5G无线通信信号弱覆盖区域识别方法

目前,5G无线通信信号弱覆盖区域多采用单层级的识别模型,能够在合理范围内获取识别结果,但是精准度不高,导致误识率增大,因此提出对基于随机森林的5G无线通信信号弱覆盖区域识别方法的分析与研究。首先,对信号识别的需求和标准进行数据采集与识别环境预处理,设定定向识别节点;其次,综合随机森林技术,构建多层级的信号识别结构,提升识别结果的精准度;最后,构建随机森林通信信号识别模型,采用多阶信号集成处理实现区域识别。测试结果表明,本文所设计的随机森林通信信号弱覆盖区域识别测试组最终得出的误识率相对较低,表明在实际应用过程中识别速度快、误差可控、识别范围较大,具有实际的应用价值。     

基于机器视觉的工业机器人定位系统

建立了一个主动机器视觉定位系统,用于工业机器人对零件工位的精确定位.采用基于区域的匹配和形状特征识别相结合的图像处理方法,该方法经过阈值和形状判据,识别出物体特征.经实验验证,该方法能够快速准确地得到物体的边界和质心,进行数据识别和计算,再结合机器人运动学原理控制机器人实时运动以消除此误差,满足工业机器人自定位的要求.     

基于Mask R-CNN的车位状态智能识别算法

在大多数城市和小区,停车位非常紧张,因此一种高效的车位状态识别算法极为重要.提出基于Mask R-CNN的车位状态智能识别算法,分析Mask R-CNN网络模型框架,检测视频图像中的汽车,通过目标识别与分割过程得到视频图像内的汽车对象的边界框和坐标位置;由于汽车边界框与停车位边界框存在部分交叉区域,因此可采用IOU法判断两个边界框交叉区域的像素数量,同时求得两个汽车目标覆盖区域像素总量的商值,确定视频图像中停车位边界框与汽车边界框之间的交叉程度,实现对停车位状态智能识别.实验结果显示,整个过程所需时间显著少于对比算法,说明该算法具有较高的识别精度与识别效率.     

电子倍增CCD多模式驱动设计

针对L3Vision电子倍增CCD图像传感器,设计了多工作模式的驱动方案,以实现微光环境下目标物体的精确识别与快速定位。通过电子倍增调节,降低读出噪声,提高图像信噪比;调节曝光时间,获取合适的图像灰度值,以帮助目标识别;通过开窗模式,选取感兴趣区域图像进行输出,提高帧频,实现快速定位;通过曝光同步,令图像采集系统与控制系统保持节奏一致。实验结果表明:通过多种方案的配合,可在低照度环境下快速获取清晰图像。     

Ad Hoc网络区域最小覆盖方法研究

覆盖控制作为无线传感器网络中的一个基本问题,反映了网络所能提供的“感知”服务质量,可以使无线传感器网络的空间资源得到优化分配,进而更好地完成环境感知、信息获取和有效传输的任务.如何计算同时满足“覆盖要求”（工作节点必须能够完全覆盖目标区域）和“连通性要求”（工作节点组成的通信网络必须是连通的）的最小节点集合,是一个NP难问题.本文设计了一种基于目标区域Voronoi划分的改进集中式近似算法,用于计算完全覆盖目标区域所需要的近似最小节点集.     

移动机器人的边界识别和遍历矫正

设计和研究了移动机器人的磁边界识别和遍历矫正。采用烧结NdFeB作为磁边界材料;对于边界磁场信息的识别,选用UGN3503霍尔传感器来实现;对于机器人的遍历,采用迂回式遍历。对于通常移动机器人的遍历中产生的累计误差问题,提出一种矫正的方案,采用磁边界作为机器人纠正误差的标志,该磁边界同时也作为机器人工作区域的边界。这与当前其他矫正遍历误差方式相比,有效地降低了边界系统的复杂性,同时也提高整个运作系统的稳定性。     

基于分弧段角度法的雷达网重叠覆盖区域研究

合理规划雷达网的重叠覆盖区域以提升其整体能量利用率,是雷达网布站的核心要素之一。为了有效获取雷达网系统中重叠覆盖区域的具体特性,提出一种基于分弧段角度法的雷达网重叠覆盖区域计算方法,在各雷达的边界交点信息基础上,将各雷达的边界分割为一条条弧段,根据这些弧段采用分弧段角度法的搜索算法实现对雷达网重叠覆盖区域特性的计算。仿真结果表明,上述方法在多部雷达组成的雷达网区域范围内,可快速获取重叠覆盖区域的重叠层数、覆盖面积、覆盖雷达编号等信息,并分析获得各雷达及雷达网的重叠率,为雷达网布站提供有效的理论依据。     

无线传感器网络区域覆盖的拟物拟人优化方法

建立了以节能为目标的无线传感器网络(WSN)区域覆盖优化模型,指出此模型是一个具有指数复杂度的NP问题,针对问题特点设计了三条拟物拟人策略,分别是“拟万有引力“模型、“拟库仑力“模型和“共同富裕“模型.以此为基础,设计了一个拟物拟人算法(QPQSA),此算法是一个基于节点局部信息的分布式迭代算法:仿真结果显示,对于边界复杂的目标区域,无线传感器网络可由随机初始覆盖出发,经过自组织移位调整和功率控制达到一个优化的覆盖方案,经过优化,网络寿命延长了45.03%,覆盖率从72.28%提高至98.79%.     

采用Hough变换的道路边界检测算法

基于计算机视觉的路况信息识别是当前智能交通领域的热点课题,对道路边界的检测能够有效去除路况图像中与路况特征无关的大面积区域,是提高路况信息获取实时性的关键技术之一,提出一种适用于一般场景中直线道路和弯曲道路的边界检测算法.该算法采用直方图均衡和Otsu二值分割增强道路边界特征,使用均值滤波降噪和二值形态学方法获取特征边界,最终利用分空间的Hough变换方法实现道路边界检测.实验结果表明,该方法能够准确地检测道路边界线并进行道路区域分割,为后续的路况分类工作提供了有效保障.     

Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm

This paper aims to propose a novel design approach for on-line path planning of the multiple mobile robots system with free collision. Based on the artificial bee colony (ABC) algorithm, we propose an efficient artificial bee colony (EABC) algorithm for solving the on-line path planning of multiple mobile robots by choosing the proper objective function for target, obstacles, and robots collision avoidance. The proposed EABC algorithm enhances the performance by using elite individuals for preserving good evolution, the solution sharing provides a proper direction for searching, the instant update strategy provides the newest information of solution. By the proposed approach, the next positions of each robot are designed. Thus, the mobiles robots can travel to the designed targets without collision. Finally, simulation results of illustration examples are introduced to show the effectiveness and performance of the proposed approach.     

Motion-based identification of multiple mobile robots using trajectory analysis in a well-configured environment with distributed vision sensors

Networked mobile robots are able to determine their poses (i.e., position and orientation) with the help of a well-configured environment with distributed sensors. Before localizing the mobile robots using distributed sensors, the environment has to have information on each of the robots?? prior knowledge. Consequently, if the environment does not have information on the prior knowledge of a certain mobile robot then it will not determine its current pose. To solve this restriction, as a preprocessing step for indoor localization, we propose a motion-based identification of multiple mobile robots using trajectory analysis. The proposed system identifies the robots by establishing the relation between their identities and their positions, which are estimated from their trajectories related to each of the paths generated as designated signs. The primary feature of the proposed system is the fact that networked mobile robots are quickly and simultaneously able to determine their poses in well-configured environments. Experimental results show that our proposed system simultaneously identifies multiple mobile robots, and approximately estimates each of their poses as an initial state for autonomous localization.     

Efficient terrain coverage for deploying wireless sensor nodes on multi-robot system

Coverage and connectivity are the two main functionalities of wireless sensor network. Stochastic node deployment or random deployment almost always cause hole in sensing coverage and cause redundant nodes in area. In the other hand precise deployment of nodes in large area is very time consuming and even impossible in hazardous environment. One of solution for this problem is using mobile robots with concern on exploration algorithm for mobile robot. In this work an autonomous deployment method for wireless sensor nodes is proposed via multi-robot system which robots are considered as node carrier. Developing an exploration algorithm based on spanning tree is the main contribution and this exploration algorithm is performing fast localization of sensor nodes in energy efficient manner. Employing multi-robot system and path planning with spanning tree algorithm is a strategy for speeding up sensor nodes deployment. A novel improvement of this technique in deployment of nodes is having obstacle avoidance mechanism without concern on shape and size of obstacle. The results show using spanning tree exploration along with multi-robot system helps to have fast deployment behind efficiency in energy.     

湍流烟羽环境下多机器人主动嗅觉实现方法研究

给出了一种用于实现主动嗅觉(也称气味/气体源定位或化学烟羽跟踪)的多机器人协同搜索策略. 将蚁群算法与逆风搜索相结合用于协调多机器人的运动方向. 蚁群算法可有效调动机器人朝信息素高的区域运动且保证机器人之间的距离不会过大; 逆风搜索可降低算法过早地陷入局部最优的概率. 为正确判断转移方向, 蚁群算法中还增加了对历史信息的考虑. 在源头确认方面, 本文提出了气味/气体浓度持久性判断结合机器人旋转计算流体质量通量散度的方法. 仿真表明, 本文的主动嗅觉搜索策略可适用于湍流烟羽环境, 且可有效地逃脱浓度局部最优和风场的漩涡, 另外可最终确认源头位置.     

Identification and tracking of robots in an intelligent space using static cameras and an XPFCP

This paper tackles the problem of identification and tracking of multiple robots in an intelligent space using an array of cameras placed in fixed positions within the environment. Several types of agent can be found in an intelligent space: controlled agents (mobile robots) and uncontrolled ones (users and obstacles). The information transferred between the controlled agents and the intelligent space is limited to the control commands sent to the robots and the measurements of the odometers received from the robots. The proposed solution allows the localization of mobile agents, even if they are not robots; however, we have focused on the controlled agents. The proposal does not require prior knowledge or invasive landmarks on board the robots. It starts from the segmentation of different agents in motion that allows obtaining the boundaries of all robots and an estimation of all 3D points that define those boundaries. Then, the identification of the robots is obtained by comparing the components of the linear velocity estimated by the motion segmentation algorithm and received from the odometers. In order to track the robots, an eXtended Particle Filter with Classification Process (XPFCP) is employed. Several experimental tests have been carried out, and the results obtained validate the proposal.     

基于建筑特征及二维地图的复杂城市场景中移动机器人视觉定位算法

针对城市环境中全球定位系统(GPS)信号易受到高层建筑遮挡而无法提供准确位置信息的问题,提出了一种基于建筑物竖直侧平面特征及建筑物二维轮廓地图的移动机器人定位方法。该方法利用车载视觉,首先对两视图间的竖直直线特征进行匹配;然后基于匹配的竖直线特征对建筑物的竖直侧平面进行重建;最后,利用建筑物竖直侧平面特征及建筑物二维俯视轮廓地图,设计了一种基于随机采样一致性(RANSAC)的移动机器人视觉定位算法,从而解决了在建筑物方向任意的复杂城市环境中的机器人定位问题。实验结果表明,算法的平均定位误差约为3.6m,可以有效地提高移动机器人在复杂城市环境中自主定位的精度及鲁棒性。     

移动机器人扩展卡尔曼滤波定位与传感器误差建模

针对里程计在定位过程中存在累积误差的问题,建立了一种通用的移动机器人里程计误差模型,对里程计误差进行实时反馈补偿.在利用激光雷达进行环境特征提取过程中,根据激光雷达原始数据存在的误差,建立了激光雷达的观测误差模型,并根据环境特征和机器人的相对位置关系,建立了移动机器人观测模型.最后,结合里程计和激光雷达误差模型,利用扩展卡尔曼滤波(EKF)实现了基于环境特征跟踪的移动机器人定位.实验结果验证了里程计和激光雷达误差模型的引入,在增加较短定位时间的情况下,可以有效地提高移动机器人的定位精度.     

State-feedback stabilisation for stochastic non-holonomic mobile robots with uncertain visual servoing parameters

The stabilising problem of stochastic non-holonomic mobile robots with uncertain parameters based on visual servoing is addressed in this paper. The model of non-holonomic mobile robots based on visual servoing is extended to the stochastic case, where their forward velocity and angular velocity are both subject to some stochastic disturbances. Based on backstepping technique, state-feedback stabilising controllers are designed for stochastic non-holonomic mobile robots. A switching control strategy for the original system is presented. The proposed controllers guarantee that the closed-loop system is asymptotically stabilised at the zero equilibrium point in probability.     

基于RGB-D信息的移动机器人SLAM和路径规划方法研究与实现

移动机器人在各种辅助任务中需具备自主定位、建图、路径规划与运动控制的能力。本文利用RGB-D信息和ORB-SLAM算法进行自主定位,结合点云数据和GMapping算法建立环境栅格地图,基于二次规划方法进行平滑可解析的路径规划,并设计非线性控制器,实现了由一个运动底盘、一个RGB-D传感器和一个运算平台组成的自主移动机器人系统。经实验验证,这一系统实现了复杂室内环境下的实时定位与建图、自主移动和障碍物规避。由此,为移动机器人的推广应用提供了一个硬件结构简单、性能良好、易扩展、经济性好、开发维护方便的解决方案。     

Reliable and efficient landmark-based localization for mobile robots

This paper describes an efficient and robust localization system for indoor mobile robots and AGVs. The system utilizes a sensor that measures bearings to artificial landmarks, and an efficient triangulation method. We present a calibration method for the system components and overcome typical problems for sensors of the mentioned type, which are localization in motion and incorrect identification of landmarks. The resulting localization system was tested on a mobile robot. It consumes less than 4% of a Pentium4 3.2 GHz processing power while providing an accurate and reliable localization result every 0.5 s. The system was successfully incorporated within a real mobile robot system which performs many other computational tasks in parallel.