QR码导航的室内目标搜寻机器人研究

针对室内的目标搜寻问题, 开发研制了一套使用Android手机作为主要处理设备、可远程操控和使用语音控制的机器人系统。介绍了该系统的结构组成, 提出了一套基于人工路标的导航方案。该方案应用QR码作为人工路标引导机器人运动到指定区域, 然后识别出目标位置, 最后通过判断目标区域面积是否达到阈值的方法停在目标附近位置, 基于有限状态机实现机器人的模式切换。对目标检测和人工路标识别分别进行了实验, 结果验证了该方案的有效性和实用性。     

多传感器信息融合在移动机器人定位中的应用

机器人自定位是实现自主导航的关键问题之一。为了满足机器人在导航时精确定位的要求,提出一种基于多传感器信息融合的自定位算法。根据对机器人运动机构的分析和运动机构间的刚体约束,建立起机器人的运动学模型;由传感器的工作原理建立里程计和超声波传感器的观测模型;利用扩展卡尔曼滤波(EKF)算法将里程计和超声波传感器采集的数据进行融合;最后,由匹配的环境特征对机器人的位置进行修正,得到精确的位置估计。实验结果表明:该算法明显地消除了里程计的累计误差,有效地提高了定位精度。     

基于UWB和IMU的煤矿机器人紧组合定位方法研究

针对煤矿井下环境复杂,现有煤矿机器人定位方法受非视距误差等因素影响导致定位精度低、实时性不高等问题,提出了一种基于UWB(超宽带)和IMU(惯性测量单元)的煤矿机器人紧组合定位方法。首先利用UWB模块测量煤矿机器人与UWB基站之间的距离,使用煤矿机器人与UWB基站之间的距离真实值和实测值训练最小二乘支持向量机(LSSVM)模型,得到LSSVM修正模型;然后将煤矿机器人定位过程中UWB模块测得的实测值作为LSSVM修正模型的输入,通过LSSVM修正模型对UWB实测值进行修正,减小非视距误差对定位精度的影响,得到较为准确的距离信息;最后将经过LSSVM修正模型修正后的测距信息作为误差状态卡尔曼滤波(ESKF)的量测输入,与惯性导航解算出的位置信息构成量测方程,使用ESKF对UWB测距修正值与惯性导航解算的距离信息紧组合,完成状态更新,得到更为精确的位置信息,实现煤矿机器人的精确定位。UWB基站不同布置方案下的模拟实验结果表明：使用LSSVM修正模型可使UWB测距信息更为准确,进而提高定位精度。静态定位实验时,当4个UWB基站等高对称布置时,定位的均方根误差由0.146 4 m减小到0.13...     

液下搅拌机器人的超声波导航定位研究

针对研究在水煤浆环境下工作的机器人,提出了采用超声波进行导航定位的方法;详细介绍了该定位系统的硬件布置方案、定位数学模型,并在水煤浆介质中做了超声波传播实验。     

车载INS/GPS组合导航系统建模与仿真

为了准确地对车辆进行定位,实现导航功能,将两种常用的导航定位技术GPS定位导航技术与惯性导航技术进行组合,介绍了INS/GPS组合导航系统的仿真方案。因车载组合导航系统对精度的要求不是很高,采用低成本的机械陀螺和加速度计作为惯性导航系统的测量器件。INS/GPS组合导航系统采用位置和速度组合模式,分别给出纯INS,纯GPS,组合导航系统的位置误差比较。结果分析表明,组合导航系统精度高于INS和GPS分别独立工作时的精度。     

室内移动机器人平台设计与实现

针对目前智能移动机器人只能在单一楼层间工作的现状,对智能移动机器人在不同楼层间切换工作环境的问题进行了研究。基于机器人操作系统(ROS),设计了一套智能移动机器人研究平台IIMR-I,使用自身携带的激光雷达传感器、惯性测量元件(IMU)、超声波和深度相机感知外界环境信息,使用里程计获取自身位移信息,并通过卡尔曼滤波对传感器数据进行滤波处理。使用即时定位与建图(SLAM)技术,构建分辨率为5 cm的高精度栅格地图,并在此基础上进行全局和局部路径规划,实现了机器人的导航和壁障功能。使用蒙特卡洛定位的方法,在机器人导航过程中可以实时确定机器人位姿,其相对定位精度可达10 cm。通过使用深度相机能够识别出电梯按钮的三维坐标,并使用机械臂按下目标楼层按钮,实现了机器人按电梯的功能。     

组合导航定位系统研究

掘进机是煤矿生产的主要机械装备之一,为使其高效、安全工作,满足人工化和智能化的发展趋势,设计了基于惯性导航系统和全站仪的悬臂式掘进机机身导航定位系统。本文将掘进机简化成两条履带,其它部件略去,建立掘进机机器人化数学模型。分别分析了基于全站仪和惯性导航系统的掘进机自动导航和定位技术的原理和特点,结合两者优点,将惯性导航系统和全站仪有机结合,介绍两系统相组合的巷道导航系统,并对组合系统在掘进机在使用中的导航和定位原理进行说明。     

移动机器人自主导航矩阵二维码辅助定位研究

移动机器人自主导航过程中,为机器人提供精确地位置信息十分重要,只有机器人有了精确地位置信息才能准确地导航到目标点。由于单纯的惯性和里程测量系统的相对定位方式都不能消除长时间的累计定位误差,因此需要一种绝对位置信息加以辅助修正累计误差。二维码是一种很好的可以存储绝对位置信息的方式,且信息获取简单易用,本文提出了一种将矩阵二维码作为绝对位置标签辅助修正里程和惯性测量系统导航过程中产生的累计误差的导航定位方法。通过实验对比验证了该方法的有效性。     

基于ARM的建筑能效数据采集机器人设计

设计了一种应用于建筑能效数据采集的室内履带机器人。该履带机器人在室内环境中通过无线传感器网络和惯性导航系统联合定位的策略,到达目标位置采集无线传感器结点信息,最终完成建筑物内部的建筑能效数据采集任务。采用ARM系统的设计方案,使用电子罗盘、加速度和转速等传感器,结合无线传感器网络的RSSI技术实现了机器人的行驶和定位功能。     

建筑机器人自主定位及行走轨迹规划方法

为优化机器人行走规划路径,开展建筑机器人自主定位及行走轨迹规划方法的设计研究。将建筑机器人的整个工作平面划分为数千个小区域,标定摄像机参数,进行建筑机器人空间坐标自主定位;将虚实耦合作为前提条件,利用BIM模型中各实体的位置信息,进行机器人行走导航地图的构建;引进Unty3D建模工具,使用Solidworks软件,构建机器人实验操作平台,将模型导入Unty3D的物理引擎中,生成建筑机器人运动学规范模型;引进A*算法,进行机器人运动空间全局路径的寻优,以此实现对机器人行走轨迹的规划。实验结果表明：设计方法可以在确保规划路线躲避空间中所有障碍物的基础上,保证规划的路线为最短路线。     

Hazardous area navigation with temporary beacons

A new area expansion algorithm for the localization scheme, using temporary beacons, is proposed in this paper. The effective area of the active beacons is limited by the strength of the ultrasonic signals in a noisy environment. When a mobile robot needs to move into a hazardous area or into an unstructured environment where the beacons with pre-specified position information are not available, the localization may solely rely on dead reckoning sensors such as encoders. To overcome the error accumulation by using dead-reckoning, a new scheme is developed, in this paper, in which the mobile robot carries a few temporary beacons which do not have any pre-stored position information. When the mobile robot encounters a dangerous or unstructured environment, it utilizes the temporary beacons to localize itself. An auto-calibration algorithm has been developed to provide the position information to the temporary beacons before they are used for the localization. With these temporary beacons and the auto-calibration algorithm, mobile robots can safely pass unstructured areas. The effectiveness of the temporary beacons and auto-calibration algorithm is verified through real experiments of mobile robot navigation.     

Mobile robot navigation using grid line patterns via probabilistic measurement modeling

Mobile robots are generally equipped with proprioceptive motion sensors such as odometers and inertial sensors. These sensors are used for dead-reckoning navigation in an indoor environment where GPS is not available. However, this dead-reckoning scheme is susceptible to drift error in position and heading. This study proposes using grid line patterns which are often found on the surface of floors or ceilings in an indoor environment to obtain pose (i.e., position and orientation) fix information without additional external position information by artificial beacons or landmarks. The grid lines can provide relative pose information of a robot with respect to the grid structure and thus can be used to correct the pose estimation errors. However, grid line patterns are repetitive in nature, which leads to difficulties in estimating its configuration and structure using conventional Gaussian filtering that represent the system uncertainty using a unimodal function (e.g., Kalman filter). In this study, a probabilistic sensor model to deal with multiple hypotheses is employed and an online navigation filter is designed in the framework of particle filtering. To demonstrate the performance of the proposed approach, an experiment was performed in an indoor environment using a wheeled mobile robot, and the results are presented.     

Multisensor 3D Posture Determination of a Mobile Robot Using Inertial and Ultrasonic Sensors

This paper presents methodologies and techniques for fusing inertial and ultrasonic sensors to estimate the current posture of a mobile robot navigating over indoor uneven terrain. This new type of pose tracking system is developed by means of fusing an inertial navigation subsystem (INS) and an ultrasonic localization subsystem. Extended Kalman filtering (EKF)-based algorithm for integrating both the subsystems is proposed to obtain reliable attitude and position estimates of the vehicle and to eliminate the accumulation errors caused by wheel slippage and surface roughness. Experimental results are conducted to illustrate feasibility and effectiveness of the proposed system and method.     

Skeleton pruning as trade-off between skeleton simplicity and reconstruction error

The position and orientation of moving platform mainly depends on global positioning system and inertial navigation system in the field of low-altitude surveying, mapping and remote sensing and land-based mobile mapping system. However, GPS signal is unavailable in the application of deep space exploration and indoor robot control. In such circumstances, image-based methods are very important for self-position and orientation of moving platform. Therefore, this paper firstly introduces state of the art development of the image-based self-position and orientation method (ISPOM) for moving platform from the following aspects: 1) A comparison among major image-based methods (i.e., visual odometry, structure from motion, simultaneous localization and mapping) for position and orientation; 2) types of moving platform; 3) integration schemes of image sensor with other sensors; 4) calculation methodology and quantity of image sensors. Then, the paper proposes a new scheme of ISPOM for mobile robot — depending merely on image sensors. It takes the advantages of both monocular vision and stereo vision, and estimates the relative position and orientation of moving platform with high precision and high frequency. In a word, ISPOM will gradually speed from research to application, as well as play a vital role in deep space exploration and indoor robot control.     

Simultaneous localization and map building for mobile robotnavigation

Inexpensive ultrasonic sensors, incremental encoders, and grid-based probabilistic modeling are used for improved robot navigation in indoor environments. For model-building, range data from ultrasonic sensors are constantly sampled and a map is built and updated immediately while the robot is travelling through the workspace. The local world model is based on the concept of an occupancy grid. The world model extracted from the range data is based on the geometric primitive of line segments. For the extraction of these features, methods such as the Hough transform and clustering are utilized. The perceived local world model along with dead-reckoning and ultrasonic sensor data are combined using an extended Kalman filter in a localization scheme to estimate the current position and orientation of the mobile robot, which is subsequently fed to the map-building algorithm. Implementation issues and experimental results with the Nomad 150 mobile robot in a real-world indoor environment (office space) are presented     

复合机构移动机器人红外和超声阵列信息处理方法

传感器是移动机器人认识和了解外部环境的重要途径。在导航过程中,移动机器人要对当前环境进行实时感知和快速理解,并加以识别从而准确避开障碍物。论文提出一种适用于复合机构移动机器人的红外阵列和超声阵列传感器信息采集和处理方法。结合笔者研制的“基于复合机构的非结构环境移动机器人”,给出了系统具体的软、硬件的设计和局部路径规划实现方法。实验验证了该方法的可靠性和有效性。     

Localization of a mobile robot based on an ultrasonic sensor using dynamic obstacles

In this article, we propose a localization scheme for a mobile robot based on the distance between the robot and moving objects. This method combines the distance data obtained from ultrasonic sensors in a mobile robot, and estimates the location of the mobile robot and the moving object. The movement of the object is detected by a combination of data and the object’s estimated position. Then, the mobile robot’s location is derived from the a priori known initial state. We use kinematic modeling that represents the movement of a robot and an object. A Kalman-filtering algorithm is used for addressing estimation error and measurement noise. Throughout the computer simulation experiments, the performance is verified. Finally, the results of experiments are presented and discussed. The proposed approach allows a mobile robot to seek its own position in a weakly structured environment. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

模糊控制在移动机器人路径规划中的应用

针对移动机器人最优路径规划问题,设计了一种模糊智能控制方法。利用超声波传感器对机器人周围环境进行探测,得到关于障碍物和目标的信息。通过设计模糊控制器,把得到的障碍与目标位置信息模糊化,建立模糊规则并解模糊最终使机器人可以很好地避障,并且解决了模糊算法存在的死锁问题,从而实现了移动机器人的路径规划。仿真实验结果表明了模糊算法优于人工势场法,具有有效性和可行性。     

Continuous mobile robot localization by using structured light and a geometric map

A mobile robot needs to know its position and orientation with accuracy in order to decide the control actions that permit it to finish the entrusted tasks successfully. To obtain this information, dead-reckoning-based systems have been used, and more recently inertial navigation systems. However, these systems have some errors that grow bigger as time goes by, therefore a moment comes when the information provided is useless. Because of this, there should be a periodic process that updates the robot position and orientation of the vehicle. The process to determine the robot position and orientation by using information originated from the external sensors is defined as the mobile robot relocalization. It is obvious that the greater the frequency of this process, the better the knowledge of its position the robot will have, and therefore its movements will be better directed to the point it must reach. The algorithm to achieve this can be classified in two large groups: relocalization through an a priori map of the environment and relocalization through the detection of landmarks present in that environment. The algorithm presented in the paper belongs to the first case. The sensor used is a combination of a laser diode and a CCD camera. The sensorial information is modelled as straight lines that will be matched with an a priori map of the environment. With this, the position of the mobile robot is estimated. The matching process is accomplished within an extended Kalman filter. The algorithm is able to work in real time, and it actualizes the position of the robot in a continuous way.     

An enhanced topological map for efficient and reliable mobile robot navigation with imprecise sensors

An enhanced topological mapping system for efficient and reliable navigation is presented. The map has a topological framework and some additional features. Firstly, it utilizes such rough metrical information as the length and orientation of the links. Secondly, it provides a reliable localization algorithm with which the robot first finds the interval describing the robot’s probable location by estimating the projected traveled distance using dead reckoning and then fine-tunes the estimation using landmark detection modules. Finally, it provides a planning algorithm with which the robot’s path is chosen so that the robot reaches the goal location as fast as possible without losing its way despite using such imprecise sensors as ultrasonic range finders.We have implemented and tested the proposed mapping system both on a simulator and a real mobile robot, the CAIR-2. This paper also describes landmark detection modules that utilize ultrasonic range finders. Although landmark detection modules are too simple and imprecise to estimate position by themselves, these experiments show that the proposed mapping system can reliably guide robots.