Positioning and obstacle avoidance of automatic guided vehicle in partially known environment

This paper presents positioning and obstacle avoidance of Automatic Guidance Vehicle (AGV) in partially known environment. To do this task, the followings are done. Firstly, the system configuration of AGV is described. Secondly, mathematical kinematic modeling of the AGV is presented to understand its characteristics and behavior. Thirdly, the Simultaneous Localization and Mapping (SLAM) algorithm based on the laser measurement system and encoders is proposed. The encoders are used for detecting the motion state of the AGV. In a slippery environment and a high speed AGV condition, encoder positioning method generates big error. Therefore, Extended Kalman Filter (EKF) is used to get the best position estimation of AGV by combining the encoder positioning result and landmark positions obtained from the laser scanner. Fourthly, to achieve the desired coordinate, D* Lite algorithm is used to generate a path from the start point to the goal point for AGV and to avoid unknown obstacles using information obtained from laser scanner. A backstepping controller based on Lyapunov stability is proposed for tracking the desired path generated by D* Lite algorithm. Finally, the effectiveness of the proposed algorithms and controller are verified by using experiment. The experimental results show that the AGV successfully reaches the goal point with an acceptable small error.     

基于改进UKF的GPS非线性动态滤波研究

为了提高动态定位精度,将一种改进的UKF(Unscented galman Filter)算法应用在GPS非线性动态定位解算中.将UKF算法与IEKF(Iterated Improved Kalman Filter)算法相结合,因此保持了基本UKF算法易于实现和收敛速度快的优点,同时由于滤波值是通过迭代扩展的卡尔曼滤波机制得到,进而更新值能更准确的逼近非线性系统状态概率密度函数,具有更高的精度.应用于GPS非线性动态滤波定位中,仿真结果表明:与UKF算法相比,算法能够明显提高定位精度.     

Navigation of automated guided vehicles using magnet spot guidance method

Automated guided vehicle systems (AGVSs) are used to transport goods and products in most manufacturing systems. In this research, we use a cylindrical magnet spot, which is widely used in industrial AGVSs, to develop a guidance system for indoor AGV navigation. This paper describes the navigation and control system of an AGV by magnet spot guidance with a differential drive. Furthermore, Hall-effect sensors, encoders, and counters are employed to achieve control and continuous guidance. Existing guidance methods use a gyro sensor and dead reckoning with encoders to calibrate against steering angle errors. Here, the maximum value of the magnetic flux density of the magnet spot, which is obtained by the Hall-effect sensor, is used to calibrate against steering angle errors and as a navigation guide for the AGV. Furthermore, real-time corrections for wheel-skidding errors are accomplished with a fuzzy controller. Thus, high-precision continuous guidance with stable and satisfactory navigation at high speeds is achieved. This guidance method was applied to real manufacturing processes in a ceramic plant and steel-bar reinforcement plant to examine its control ability, stability, and effectiveness. The proposed method was found to be robust to disturbances and uncertainty problems during tracking.     

自主导航小车AGV定位方法的研究

AGV是一种用于FMS中以实现物料自动运输的轮式移动机器人,对其进行定位是实现物料准确运输的基本保证.通过对移动机器人现有定位方法及其定位精度的分析,提出了利用安装于AGV侧两模拟量超声波传感器为其实现定位的定位方法.理论分析证明了该方法是可行的,实验验证了该定位方法是可靠的.实验结果表明,采用所设计的定位方法为AGV进行定位,其定位精度可满足FMS对AGV定位精度的要求.     

基于超声波传感器的AGV定位方法的实验研究

自动导航小车(AGV)是一种用于柔性加工系统中以实现物料搬运的移载用轮式移动机器人。为完成其任务,AGV的控制系统必须具备定位、路径规划和导航等功能。考虑到其工作环境的特殊性,为自制的XAUT.AGV100设计了一种定位系统。理论分析和实验均表明:位置精度≤±3 mm,姿态精度≤±0.5°,完全满足生产车间对操作机的精度要求。     

基于WT-UKF 的PDR/GPS 组合定位算法

针对行人航迹推算(PDR) 与全球定位系统(GPS) 组合定位问题, 提出一种基于小波变换(WT) 的无迹卡尔曼滤波(UKF) 改进算法, 对PDR 和GPS 定位结果进行数据融合. 建立PDR/GPS 组合定位系统数学模型, 采用小波变换对运动加速度信号噪声特性进行在线估计, 以更新UKF 的协方差矩阵. 所提出的WT-UKF 滤波算法弥补了传统UKF 算法因人为假定信号噪声为高斯白噪声而影响滤波效果和精度的缺陷. 实验结果表明, 使用WT-UKF 滤波算法对PDR/GPS 进行数据融合时稳定性更强, 精度更高.     

基于改进UKF算法的移动机器人定位方法研究

针对移动机器人在多传感器融合定位过程中因噪声统计特性未知或不准确引起的定位精度不高的问题,提出了一种基于Sage-Husa滤波改进的无损卡尔曼滤波(UKF)移动机器人定位算法。首先建立了移动机器人定位相关模型;然后根据噪声统计特性时变特点利用Sage-Husa中的噪声估计器,对状态噪声和量测噪声进行自适应地估计,减小扰动噪声给定位解算带来的误差;接着在状态更新时引入收敛因子,加快算法收敛速度;最后将UKF算法和改进的UKF算法应用到实验室移动机器人中进行仿真实验。实验结果表明,所提出的算法对状态扰动具有较强的抵制能力,对机器人定位的准确性与稳定性的提升具有显著效果。     

基于UKF的环境自适应UWB/DR室内定位方法

针对复杂室内环境下超宽带（Ultra WideBand,UWB）信号传播的非视距（Non Line Of Sight,NLOS）误差问题,本文提出了一种基于无迹卡尔曼滤波（Unscented Kalman Filter,UKF）的环境自适应UWB/DR室内定位方法.该方法通过建立自适应UKF滤波模型,将UWB定位信息和航迹推算（Dead Reckoning,DR）定位信息进行融合.依据新息和高斯分布的3σ原则来对UWB定位结果进行非视距检测,再通过新息的实时估计协方差和理论协方差来构建环境适应系数,进而用此系数动态修正UWB定位的观测噪声,使得观测噪声自适应真实环境,降低NLOS误差对融合定位结果的影响.实验结果表明,该方法能有效减小UWB定位的NLOS误差,并且由于环境适应系数的创新引入,比UKF定位和粒子滤波定位（Particle Filtering,PF）有更高的定位精度和更强的抗NLOS误差性能.     

自动导引车导引技术研究现状与发展趋势

导引技术是自动导引车（AGV）的关键技术之一。电磁感应导引已广泛应用;激光导引是目前采用的主流技术;视觉导引是研究的热点;惯性导引、超声波导引和磁钉一陀螺导引正被关注。单一导引技术不能解决成本、可靠性、灵活性和定位精度等性能指标之间的矛盾,低成本高可靠性的传感器、组合导引及信息融合技术需要进一步研究。     

一种针对全海深载人潜水器的异步融合组合导航算法

全海深载人潜水器（HOV）组合导航中会产生异步融合现象,传统的组合导航算法在处理时会产生较大的误差．针对这一问题,提出了一种基于机器学习和无迹卡尔曼滤波（UKF）的异步融合组合导航算法．首先建立了针对超短基线（USBL）声学定位系统预测的机器学习模型,通过USBL声学定位系统的观测数据集来训练该模型,并用得到的模型来预测更新间隔内的数据．最后使用UKF将已更新的数据集进行融合．仿真结果表明,相比传统的组合导航算法,本文的异步融合组合导航算法可以将USBL声学定位系统数据异步问题所引起的误差降低17%,有效提高了组合导航系统的精度．     

A 3D indoor positioning system based on low-cost MEMS sensors

A positioning system in the absence of GPS is important in establishing indoor directional guidance and localization. Inertial Measuring Units (IMUs) can be used to detect the movement of a pedestrian. In this paper, we present a three-dimensional (3D) indoor positioning system using foot mounted low cost Micro-Electro-Mechanical System (MEMS) sensors to locate the position and attitude of a person in 3D view, and to plot the path travelled by the person. The sensors include accelerometers, gyroscopes, and a barometer. The pedestrians motion information is collected by accelerometers and gyroscopes to achieve Pedestrian Dead-Reckoning (PDR) which is used to estimate the pedestrian’s rough position. A zero velocity update (ZUPT) algorithm is developed to detect the standing still moment. A Kalman filter is combined with the ZUPT to eliminate non-linear errors in order to obtain accurate positioning information of a pedestrian. The information collected by the barometer is integrated with the accelerometer data to detect the altitude changes and to obtain accurate height information. The main contribution of this research is that the approach proposed fuses barometer and accelerometer in Kalman filter to obtain accurate height information, which has improved the accuracy at x axis and y axis. The proposed system has been tested in several simulated scenarios and real environments. The distance errors are around 1%, and the positioning errors are less than 1% of the total travelled distance. Results indicate that the proposed system performs better than other similar systems using the same low-cost IMUs.     

Pose alignment of aircraft structures with distance sensors and CCD cameras

A precise transshipment system is developed for automatic transporting material between an automated guided vehicle (AGV) and a load transfer station. In order to implement the alignment of the fixture base on board the AGV with that of the station, it's necessary to measure the pose of the AGV with respect to the station. The pose measurement system combined four distance sensors with two CCD cameras, with the distance sensors to measure the yaw, pitch angle and longitudinal deviation, and the cameras to determine the roll angle and the position deviation in the reference plane. A 6-degree of freedom (DOF) pose alignment system based on 3-DOF positioners is used to correct the pose deviation of the current pose with respect to the goal pose, lowering the demand on locating precision of AGV on the ground. The method to calibrate the whole measurement system is elaborately stated. The transshipment experiments have been conducted and the results show that the pose alignment system integrated with the proposed multi-sensor pose measurement system can meet the accuracy and repeatability required in industrial application.     

Steering rules for AGV based on primitive function and elementary movement control

The complete structure of an AGV control system is described in the first part of this paper. The AGV control system is hierarchical and consists of five levels. The structure of one level does not depend on the structures of the other levels. This means that the control system depends on the design of the AGV at the lowest level only, at the actuator servo-control level and its coordination in realizing AGV primitive functions.The second part of the paper describes rules applicable to AGV steering. The structure of these rules depends on two groups of factors. The first group is dependent on information groups fed to the AGV processor by the position sensor. The second group of factors represents aims and conditions and AGV steering such as positioning accuracy, positioning time, allowed room for maneuver, the shape of the given trajectory, etc. The AGV steering rules contain sequences of primitive functions. These primitive functions are of such types as “turn left”, “straighten” (correct), “go straight on”, etc. Trajectory, as one of the basic factors, is defined at the level of controlling an elementary movement. The term “to control an elementary movement” means to select a transport road throughout the transport network and to code it using “elementary movement” such as “go straight” (relating to road section), “turn left” (relating to turning at a crossroad) etc.The results of the AGV steering simulation are presented in the third part of the paper. An exact kinematic AGV model used for stimulating control models is also presented.     

Laser interferometry-based guidance methodology for high precision positioning of mechanisms and robots

Laser interferometry-based sensing and measurement (LISM) technique was originally investigated to perform dynamic measurements of the end effector of a robot manipulator in motion. This technique can provide dynamic position measurements in real time and has high accuracy, large working space, high sampling rate and automatic target tracking. In this paper, a methodology using LISM technique is proposed to perform laser interferometry-based guidance (LIG) for accurate positioning of a robot manipulator in high precision manufacturing operations. The methodology utilizes the LISM apparatus to guide the robot's end effector to a desired location or along a desired path by directing the robot to follow the trajectory mapped by the laser beam. This is accomplished through the establishment of techniques for path generation, sensing and data acquisition and guidance error determination and compensation in the control algorithm. The algorithms for this methodology, together with the measurement and analysis techniques are described. A number of experiments are carried out to examine and validate the proposed LIG technique. Experimental results show that the established technique can effectively improve the positioning accuracy of the robot manipulator.     

Measurement error analysis and accuracy enhancement of 2D vision system for robotic drilling

Robotic drilling for aircraft structures demands higher accuracy on industrial robots than their traditional applications. Positioning error measurement and compensation based on 2D vision system is a cost-effective way to improve the positioning accuracy in robotic drilling. In this paper, we first discuss the principle of error measurement and compensation with a 2D vision system for robotic drilling and the determination of tool center point of the vision system so that the Abbe errors are eliminated in the measurement process. Measurement errors due to nonideal measurement conditions, i.e. nonperpendicularity of the camera optical axis to the workpiece surface and incorrect object distance, are mathematically modeled and experimentally verified. A method utilizing four laser displacement sensors is proposed to ensure perpendicularity of the camera optical axis to the workpiece surface and correct object distance in the measurement process, and hence to achieve high accuracy in 2D vision-based measurement. Experiments performed on a robotic drilling system show that the 2D vision system can achieve an accuracy of approximately 0.1 mm with the proposed method.     

Motion control of a magnetically levitated microrobot using magnetic flux measurement

Recent advancements in micro/nano domain technologies have led to a renewed interest in ultra-high resolution magnetic-based actuation mechanisms. This paper deals with the development of a novel research-made magnetic microrobotic station (MMS) with promising potential in biological/biomedical applications. The MMS consists of two separate basic components: a magnetic drive unit and a microrobot. The magnetic drive unit produces and regulates the magnetic field for non-contact propelling of the microrobot in an enclosed environment. Our previous research findings have reported that the MMS should be equipped with high accuracy laser sensors for the position determination of the microrobot in the workspace. However, the laser positioning techniques can be used only in highly transparent environments. This paper seeks to address microrobot position estimation in non-transparent environments. A novel technique based on real-time magnetic flux measurement has been proposed for position estimation of the microrobot in the case of the laser beam blockage. A combination of Hall-effect sensors is employed in the structure of the magnetic drive unit to find the microrobot’s position using the produced magnetic flux. The most effective installation position for the Hall-effect sensors has been determined based on the accuracy sensitivity of experimental measurements. We derived a mathematical function which relates Hall-effect sensors’ voltage output and the position of the microrobot. The motion control capability of the Hall-effect-based positioning method is experimentally verified in the horizontal axis, and it was demonstrated that the microrobot can be operated in most of the workspace range with an accuracy of 0.3?mm as the root-mean-square of the position error.     

家庭服务机器人自主定位控制

家庭服务机器人是目前机器人领域的一个研究热点,家庭服务机器人的研发可以减轻社会和家庭负担,缓解人口老龄化等问题.自主定位技术是家庭服务机器人研发的核心技术之一.本文针对家庭服务机器人定位技术研究,进行了其机械结构的设计以及传感器的选型提供方案,也对ROS编译环境进行介绍.最重要的是设计出一种基于激光SLAM技术为核心的传感器数据融合方法适用于家庭的微动态环境下的定位方法.在对系统进行运动学模型和坐标系模型建模的基础上,提出一种传统的Hector SLAM算法并对其进行改进成自适应无迹卡尔曼(AUKF)融合算法,提高了定位的稳定性和准确性.     

组合导航的AGV定位精度的改善

为了减小AGV惯性导航系统的累积误差,利用地埋标签辅助惯性导航系统构成一种自主式组合导航系统.在忽略陀螺仪误差的基础上,由惯性导航系统的运动方程推算出位置误差的计算式,得出影响误差的3个因素:加速度计偏差、AGV的行驶角度和地埋标签的数据更新时间.提出从影响误差的地埋标签数据更新时间入手,重新布放地埋标签以降低数据更新时间来提高AGV定位精度.通过Matlab对数据进行仿真,表明该方法能有效地降低AGV惯性导航系统的累积误差,进而改善了AGV的定位精度.     

工业机器人加工轨迹双目3D激光扫描成像修正方法

为了提高工业机器人作业柔性,本文提出了一种基于双目CCD激光扫描3D成像的"眼在外"(Eye-to-hand:ETH)工业机器人末端(tool center point,TCP)运动轨迹在线修正方法.以激光切割机器人视觉引导为研究背景,降低加工过程机器人对物理工装定位精度的依赖.首先,为提高机器人视觉控制精度,研究了目...