基于视觉的移动机器人自主定位导航

自主导航足移动机器人的关键技术,机器视觉由于信息量大和人性化特征．在移动机器人自主导航得到广泛应用。本文介绍了基于视觉导航的基本概念、算法及新技术的应用,在分析近年来较成功视觉导航系统基础上,提出了视觉导航数据处理的发展方向。     

基于多传感器信息融合移动机器人导航定位研究

移动机器人的导航及定位是机器人自主导航的关键技术之一。为提高移动机器人的导航及定位能力,提出以多种导航定位传感器组合为融合单元,设计扩展卡尔曼滤波算法,将陀螺仪、里程计和电子罗盘采集的数据进行融合。设计模糊神经网络对所融合的数据进行训练处理,提高数据处理的精度和效率,实现对移动机器人精确的控制。并进行了仿真分析,结果证明：所提出的多传感器信息融合算法既可使移动机器人在复杂环境中自主定位,又实现有效避障,有实际参考价值。     

基于运动学约束的移动机器人组合导航算法

为提高GNSS受扰情况下移动机器人的组合导航性能,研究一种基于增强型运动学约束辅助的组合导航方法。分析移动机器人正常行驶过程中加速度特点构建组合导航系统状态模型,并在卫星/惯导位置、速度组合观测模型基础上,根据移动机器人侧向和垂向速度为零的约束条件构建虚拟观测模型,采用卡尔曼滤波进行组合导航算法设计。仿真验证结果表明,该方法可在不进行系统硬件更新的前提下,进一步提高GNSS受扰情况下移动机器人的组合导航性能,对工程应用具有重要的参考价值。     

基于视觉的移动机器人导航系统

以自主开发的XK-1型履带式移动机器人为实验平台,分析移动机器人的运动学模型,给出了基于视觉和模糊控制的直线跟踪方法及其简化算法,研究了移动机器人的定位和路径规划,实现了基于视觉的移动机器人的导航.     

一种基于激光雷达的移动机器人环境建模新方法

环境建模技术是自主式移动机器人导航研究中的一个重要环节.本文在分析当前普遍采用的一些环境建模方法及其缺点的基础上,针对复杂环境下移动机器人的导航任务提出了一种结合栅格法和改进AVBN法的环境建模方法.该方法首先读取激光雷达数据来建立栅格地图,再通过栅格扩张形成一个由可行路径所组成的网络拓扑图,从而可利用图搜索优化算法得到从起始点到终止点之间的一条最短路径.此方法的鲁棒性和有效性通过笔者自行研制的移动机器人IMR01及其HOMING策略得到了验证.     

基于机器视觉的室内移动机器人导航偏转角的确定

室内移动机器人需要在结构化的环境中定位和导航。常用的方法是采用电子罗盘获得机器人的偏转角。但是电子罗盘极易受到磁干扰。为此,提出了采用图像识别技术来获取机器人的导航偏转角。首先,利用机器人上搭载的摄像装置获取原始图像,进而提取图像中特定颜色分量;然后,利用改进的霍夫变换算法检测机器人正向矢量;最后,获得正向矢量与北向矢量间的夹角数据值。实验结果表明,该文所提出的基于机器视觉的确定室内移动机器人导航偏转角的方式可以避免硬磁干扰,具有较高的精度,保证准确性、实时性。     

多传感器信息融合在移动机器人导航中的应用

通过实例描述了多传感器在移动机器人导航中的应用，建立了基于信息融合的导航体系结构，并详细描述了各个模块及其功能，最后通过MATLAB／SIMULINK环境下进行仿真实验，结果表明了多传感器信息融合方法在导航中的有效性。     

基于ROS的全向移动机器人控制系统的设计与实现

文章基于开源机器人操作系统(ROS)设计了一套低成本、高性能的移动机器人控制系统方案。该方案基于分层控制的理念将移动机器人的控制系统分为应用层、决策与规划层和运动控制执行层,各层次功能明确,层次间低耦合,易于移植和维护。应用层融合了物联网技术解决了移动机器人平台与用户端的通信问题;决策与规划层基于ROS实现机器人的即时定位与地图构建,并根据机器人状态以及用户命令实现机器人室内导航功能;运动控制执行层基于Free RTOS实时操作系统实现对机器人运动状态的实时控制,并采用了三轮全向移动的控制方式,运动方式更加灵活。     

基于ROS的全向移动机器人定位导航系统研究

针对传统移动机器人自主运行能力不足及系统设计困难的问题,以自行设计的四麦克纳姆轮全向移动机器人为研究对象,根据层次化和模块化的思想建立了基于混合硬件架构的全向移动机器人定位导航系统的总体设计框架,建立了基于机器人操作系统ROS(Robot Operating System)的实验平台,分析了基于激光雷达的移动机器人Fast-SLAM算法,通过搭建软硬件系统,对机器人的即时定位与地图构建功能进行了测试,对路径规划能力进行了实验。结果表明,该系统能够实现全向移动机器人在未知环境下地图的实时构建,且拥有良好的全局导航及局部避障能力。研究成果对全向移动机器人定位导航系统的研究具有一定参考价值。     

移动机器人自主定位和导航系统设计与实现

为实现未知环境下,移动机器人自主定位和导航,构建精准的环境地图,提出移动机器人导航系统设计方法。采用蒙特卡洛算法对机器人进行定位,通过栅格地图扫描匹配方法进行地图构建,利用Dijkstra算法对当前地图进行路径规划。将上位机和下位机串口通信,把上位机路径规划的Twist消息发送给下位机,采用底层控制器来控制电机,实现移动机器人自主运动。实验结果表明:在实际环境中,移动机器人能够从起始点出发,自主绕过障碍物到达目的地,验证了该移动机器人自主导航系统设计方法的有效性。     

自动喂养机器人导航定位系统研究

盒养动物自动化喂养可以减少人工作业强度,降低人力成本,提高养殖的自动化水平,而自动喂养机器人的导航定位系统的研究是实现自动化喂养的关键。为此,研究了基于磁带和RFID标签的导航定位技术,设计开发了磁导航传感器的信息处理及导航纠正算法,以及利用RFID标签的站点定位技术,此外,对自动喂养机器人的避障系统进行了分析与设计。开发了自动喂养机器人导航定位控制系统,最后通过样机验证了导航定位算法的可行性和正确性,满足了自动化喂养的现场要求。     

On the identification and establishment of topological spatial relations by autonomous systems

Human beings use spatial relations to describe many daily tasks in their language. For a mobile robot to be useful in daily life, it is necessary to have navigation algorithms capable of identifying and establishing spatial relations. To date in robotics, the navigation problem has been thoroughly researched as a task of guiding a robot from one spatial coordinate to another. Therefore, there is a difference in degree of abstraction between the language of human beings and the algorithms used in robot navigation. This article introduces a piece of research performed on the use of topological relations for the formalisation of spatial relations and navigation. So far, topological relations have been applied widely in geographical information systems and also in spatial logics. There are some proposals in robot navigation which use them for planning but there is no research about making decision in robot navigation. Our research focuses on decision-making methods to establish spatial relations. The main result is a new heuristic, called the Heuristic of Topological Qualitative Semantics (HTQS), which allows the identification and establishment of spatial relations decision-making from a set of actions. To demonstrate its effectiveness, HTQS has been implemented in the form of agents that can move in a two-dimensional virtual environment. HTQS opens a new door to designing algorithms for navigation based on the identification and establishment of spatial relations.     

助航灯具清洗效果的模糊预测及实验验证

以助航灯具灯发光口图像的平均灰度值为指标,研究了影响助航灯具清洗效果的几个关键因素。采用模糊理论对清洗压力、清洗时间、清洗距离、清洗角度、清洗介质含量和清洗效果进行了模糊化处理。通过正交试验建立了模糊预测规则库。在给定输入参数的条件下,采用基于规则的模糊推理实现了清洗率的预测。实验结果表明：预测误差小于6%。     

Learning Perception for Indoor Robot Navigation with a Hybrid Hidden Markov Model/Recurrent Neural Networks Approach

This paper introduces a hybrid system for modeling, learning and recognition of sequences of 'states' in indoor robot navigation. States are broadly defined as local relevant situations (in the real world) in which the robot happens to be during the navigation. The hybrid is based on parallel recurrent neural networks trained to perform a posteriori state probability estimates of an underlying hidden Markov model (HMM) given a sequence of sensory (e.g. sonar) observations. Discriminative training is accomplished in a supervised manner, using gradient-descent. Recognition is carried out either in a dynamic programming framework, i.e. searching the maximum a posteriori probability of state-posteriors along paths of the HMM, or in real time. The approach is suitable for navigation and for map learning. Experiments of learning and recognition of noisy sequences acquired by a mobile robot equipped with 16 sonars are presented.     

Navigation through unknown and dynamic open spaces using topological notions

Until now, most algorithms used for navigation have had the purpose of directing system towards one point in space. However, humans communicate tasks by specifying spatial relations among elements or places. In addition, the environments in which humans develop their activities are extremely dynamic. The only option that allows for successful navigation in dynamic and unknown environments is making real-time decisions. Therefore, robots capable of collaborating closely with human beings must be able to make decisions based on the local information registered by the sensors and interpret and express spatial relations. Furthermore, when one person is asked to perform a task in an environment, this task is communicated given a category of goals so the person does not need to be supervised. Thus, two problems appear when one wants to create multifunctional robots: how to navigate in dynamic and unknown environments using spatial relations and how to accomplish this without supervision. In this article, a new architecture to address the two cited problems is presented, called the topological qualitative navigation architecture. In previous works, a qualitative heuristic called the heuristic of topological qualitative semantics (HTQS) has been developed to establish and identify spatial relations. However, that heuristic only allows for establishing one spatial relation with a specific object. In contrast, navigation requires a temporal sequence of goals with different objects. The new architecture attains continuous generation of goals and resolves them using HTQS. Thus, the new architecture achieves autonomous navigation in dynamic or unknown open environments.     

基于激光SLAM的移动机器人导航算法研究

定位技术是机器人技术中导航控制和路径规划的关键问题,传统定位方式采用全球定位系统(GPS),难以完成精准的定位导航功能,不依赖于GPS的定位导航方法是目前机器人领域的研究热点。提出一种基于激光雷达采集的点云信息帧间匹配方法,根据改进式激光点云数据的位姿估计算法,结合非线性优化进行了校正和优化,完成移动机器人对未知环境的精确定位。通过ROS机器人操作系统搭建实验平台,对改进算法进行验证,证明改进后帧间匹配算法的建图和定位效果对应的鲁棒性与定位精度效果更佳,可满足工程要求。     

Biologically plausible visual homing methods based on optical flow techniques

Insects are able to return to important places in their environment by storing an image of the surroundings while at the goal, and later computing a home direction from a matching between this 'snapshot' image and the currently perceived image. Very similar ideas are pursued for the visual navigation of mobile robots. A wide range of different solutions for the matching between the two images have been suggested. This paper explores the application of optical flow techniques for visual homing. The performance of five different flow techniques and a reference method is analysed based on image collections from three different indoor environments. We show that block matching, two simple variants of block matching and two even simpler differential techniques produce robust homing behaviour, despite the simplicity of the matched features. Our analysis reveals that visual homing can succeed even in the presence of many incorrect feature correspondences, and that low-frequency features are sufficient for homing. In particular, the successful application of differential methods opens new vistas on the visual homing problem, both as plausible and parsimonious models of visual insect navigation, and as a starting point for novel robot navigation methods.     

基于到达时间差异的超声波AGV定位

为满足移动的AGV对大范围、近距离定位的要求，设计了一种新的主动信标式AGV超声导航系统。该系统采用信号到达时间差异来确定AGV位置，降低了对时间的同步要求。实验表明：在2m^2范围内，平均误差为2、15mm，这说明该系统的定位精度达到和优于现有的超声波定位系统，该导航方案可满足AGV的要求。