光纤振动传感系统相位调制模块的嵌入式设计

针对光纤振动传感系统测试与标定环节缺乏便携式振动信号驱动装置的问题,设计了一款基于FreeRTOS和STemWin嵌入式系统的压电陶瓷相位调制模块.该模块使用STM32嵌入式微控制器,结合H桥功率驱动对信号进行功率放大.利用FreeRTOS嵌入式操作系统进行多任务功能部件开发,并设计了STemWin人机交互界面.随后搭建分布式光纤振动传感系统对嵌入式相位调制模块进行测试,结果表明该模块可实现驱动信号频率、占空比、输出极性和电压的调节,并可在总长为11.769km的传感光纤上对冲击信号进行实时采集,且振动信号定位误差在41m以内,从而可满足各类光纤振动传感系统在长距离不同位置处进行振动测试与标定的需求.     

基于多传感器融合的移动机器人定位算法研究

针对移动机器人在室外环境下全局位姿定位精度低、定位耗时长的问题,提出一种基于多传感器融合的机器人定位算法。首先构建移动机器人的运动模型,并选用里程计、惯性测量单元IMU和激光雷达作为移动机器人的基础传感器;然后采用自适应蒙特卡罗定位算法对传感器融合位姿进行位姿误差计算,获取移动机器人初始位姿;最后进行激光点云匹配,获取全局地图,并利用基于全局正态分布地图的NDT算法进行初始位姿修正,最终实现全局位姿校正和高精度定位。结果表明,基于多传感器融合的移动机器人定位误差控制在0.04 m范围内,定位时长均值为0.045 s,定位误差较小,定位损耗时间较少。由此说明,本定位算法可提升移动机器人的定位精度和定位效率,可实现移动机器人全局位姿快速、精确定位,提出的定位算法具备一定的有效性。     

UHF-RFID环境下的移动机器人定位方法

研究UHF-RFID环境中移动机器人的定位问题,提出一种基于自适应UKF滤波器组的移动机器人定位方法,融合UHF-RFID和机器人内部传感器信息,以实现初始位姿未知的移动机器人定位.首先,利用UHF-RFID系统对移动机器人进行初始定位,并根据其初始位置信息随机生成移动机器人的初始状态估计集;然后,考虑UHF-RFID系统定位的量化误差,应用自适应UKF方法对机器人的状态估计集进行预测和更新,并对状态估计集进行有效地裁剪、筛选以及更新,以提高滤波器的估计精度和稳定性.仿真结果表明,相比于标准UKF滤波方法,自适应UKF滤波器组方法具有更高的定位精度和更快的收敛速度.     

基于移动机器人的GPS轨迹生成及定位研究

根据GPS工作原理,设计了GPS定位模块,并将其应用于移动机器人的定位及导航中。分别在室外和室内对安装有GPS定位模块的移动机器人进行实验,设定起点和终点,使机器人通过实验路段。将室外实验结果文件导入谷歌地图中,可在谷歌地图上生成机器人运动轨迹并实时观察到机器人所处的位置;采用Matlab对室内实验结果进行处理,结果表明GPS定位模块可在室内实现移动机器人路径回放功能。     

基于嵌入式Linux的移动机器人控制系统设计

介绍了一个以嵌入式Linux系统为核心的移动机器人控制系统的设计与实现，阐述了运动控制与传感模块、主控制模块、人机交互界面和无线通信模块。该系统具有良好的可扩展性和可移植性。在无线通信模块中，集成了Zigbee协议，从而为无线传感器网络与移动机器人的协作性研究提供了可能。实验表明，该系统可以实现机器人的复杂控制。     

嵌入式移动机器人视觉定位及地图构建系统设计

设计了一种具有定位和导航功能的嵌入式移动机器人,采用双控制器协同工作模式并具有多种感知模块;在设计的嵌入式平台上进行了单目视觉定位和导航研究;通过彩色路标和电子罗盘实现对机器人的定位,分析了摄像机成像原理,给出了世界坐标系和图像坐标系的映射关系,简化了机器人定位的难度;通过超声波传感器旋转测距获得周围环境信息,对环境信息处理后建立地图的栅格模型;实验表明该定位方法能够准确提取路标的重心,具有较好的定位精度,减少了计算时间;通过超声波数据可以比较准确的建立环境模型,能够满足避障要求。     

移动机器人自主返航控制系统设计与实验

针对遥控移动机器人在通信信号中断后,无法对其进行控制的问题,设计了移动机器人自主返航控制系统,实现信号中断后移动机器人按原路径自主返航直至恢复控制信号或返回初始位置;提出了基于控制意图融合里程计和光纤陀螺仪的定位算法,对移动机器人运动学模型的结构参数进行了校正,大幅度提高了移动机器人定位精度;基于嵌入式Linux平台设计了返航控制软件系统,在信号正常的情况下,启动遥控模式,遥控机器人移动并记录路径坐标点,信号中断则开启返航模式,采用线性控制率和PID控制率实现对移动机器人路径跟踪控制;基于实际应用环境开展自主返航实验验证,实验结果表明该控制系统能够在通信信号中断后以较高的精度控制移动机器人实现自主返航;该系统能够解决移动机器人在应用中的实际问题,且代码可移植性高、通用性强。     

自主移动机器人室内定位方法研究综述

自主移动机器人的室内定位作为机器人研究领域中最基本的问题已被广泛研究。根据定位技术和传感器的不同,将室内定位方法分为航迹推算定位、地图匹配定位和基于信标定位三类。详细介绍了超声波网络定位系统和基于无线射频识别（RFID）的定位方法。对几种基于概率的定位算法做了分析和对比,并对自主移动机器人室内定位方法的研究方向做了展望。     

基于地图的移动机器人自定位与导航系统

针对地图已知情况下的移动机器人大范围导航问题，研制了一个由地图编辑器模块、地图匹配与定位模块以及多层递阶规划模块三部分组成的移动机器人导航系统．地图编辑器负责导航地图的编辑；地图匹配与定位模块利用里程计和激光雷达数据实现基于地图匹配的自定位；多层递阶规划模块将基于拓扑地图的全局规划、基于栅格地图的局部规划和底层的行为控制功能有机结合．通过室内定位和大范围导航实验评估了本系统的有效性和准确性．     

农业试验田数据采集系统开发

系统以云服务为依托,以集成ZigBee协调器模块、GPS模块、GPRS模块、RFID读写模块的ARM-Linux嵌入式终端为媒介,以分布于试验田中的RFID电子标签和温、湿度传感器为基础.GPS和RFID电子标签组成组合定位模式,对实验田中的采样植株进行标定.ZigBee协调器模块用于管理试验田温、湿度传感器网络,通过此网络采集试验田温、湿度数据;GPRS模块用于与云服务器建立远程无限网络连接,并实现各种数据的传输.使用嵌入式QT开发了终端上的应用模块,实现了终端的人机交互和控制管理.借助对云服务器的模拟验证表明,系统达到了设计要求.     

Self-calibration of environmental camera for mobile robot navigation

An environmental camera is a camera embedded in a working environment to provide vision guidance to a mobile robot. In the setup of such robot systems, the relative position and orientation between the mobile robot and the environmental camera are parameters that must unavoidably be calibrated. Traditionally, because the configuration of the robot system is task-driven, these kinds of external parameters of the camera are measured separately and should be measured each time a task is to be performed. In this paper, a method is proposed for the robot system in which calibration of the environmental camera is rendered by the robot system itself on the spot after a system is set up. Specific kinds of motion patterns of the mobile robot, which are called test motions, have been explored for calibration. The calibration approach is based upon executing certain selected test motions on the mobile robot and then using the camera to observe the robot. According to a comparison of odometry and sensing data, the external parameters of the camera can be calibrated. Furthermore, an evaluation index (virtual sensing error) has been developed for the selection and optimization of test motions to obtain good calibration performance. All the test motion patterns are computed offline in advance and saved in a database, which greatly shorten the calibration time. Simulations and experiments verified the effectiveness of the proposed method.     

一种高效的视觉导航摄像机标定方法

针对移动机器人视觉导航中的摄像机标定问题,提出一种高效的摄像机标定方法。考虑透镜畸变的影响,利用Hough变换提取矩形棋盘格的角点,求出摄像机参数初始值,运用Levenberg-Marquardt算法对初始解进行非线性迭代优化。通过重投影方法将求出的角点重新投影到矩形棋盘格像平面上,并与原提取的角点相比较,分析产生位置误差的原因。实验结果表明,该方法抗噪声能力强,对实验条件要求相对较低,标定精度满足移动机器人实际导航的要求。     

Real-time monocular image-based path detection

In this work, we present a new real-time image-based monocular path detection method. It does not require camera calibration and works on semi-structured outdoor paths. The core of the method is based on segmenting images and classifying each super-pixel to infer a contour of navigable space. This method allows a mobile robot equipped with a monocular camera to follow different naturally delimited paths. The contour shape can be used to calculate the forward and steering speed of the robot. To achieve real-time computation necessary for on-board execution in mobile robots, the image segmentation is implemented on a low-power embedded GPU. The validity of our approach has been verified with an image dataset of various outdoor paths as well as with a real mobile robot.     

Automatic work objects calibration via a global–local camera system

In a human–robot collaborative manufacturing application where a work object can be placed in an arbitrary position, there is a need to calibrate the actual position of the work object. This paper presents an approach for automatic work-object calibration in flexible robotic systems. The approach consists of two modules: a global positioning module based on fixed cameras mounted around robotic workspace, and a local positioning module based on the camera mounted on the robot arm. The aim of the global positioning is to detect the work object in the working area and roughly estimate its position, whereas the local positioning is to define an object frame according to the 3D position and orientation of the work object with higher accuracy. For object detection and localization, coded visual markers are utilized. For each object, several markers are used to increase the robustness and accuracy of the localization and calibration procedure. This approach can be used in robotic welding or assembly applications.     

多相机与IMU融合的室外机器人定位方法研究

提高SLAM(simultaneous localization and mapping)算法的精度和鲁棒性是解决室外移动机器人自主定位问题的关键.针对单目相机在室外复杂环境下易受到遮挡、相机移动过快、图像模糊以及机器人纯旋转下算法精度和鲁棒性下降、低精度IMU(inertial measurement unit)的累...     

CCD摄像机标定

在基于单目视觉的农业轮式移动机器人自主导航系统中,CCD摄像机标定是农业轮式移动机器人正确和安全导航的前提和关键。摄像机标定确立了地面某点的三维空间坐标与计算机图像二维坐标之间的对应关系,机器人根据该关系计算出车体位姿值自主导航。因此,根据CCD摄像机针孔成像模型,利用大地坐标系中平面模板上已知的各点坐标,建立与计算机图像空间中各对应像素值之间的关系方程组,在Matlab环境下拟合出摄像机各内外参数。实验结果表明：该方法可以正确完成CCD摄像机标定。     

Neuro-evolutionary mobile robot egomotion estimation with a 3D ToF camera

An innovative neuro-evolutionary approach for mobile robot egomotion estimation with a 3D ToF camera is proposed. The system is composed of two main modules following a preprocessing step. The first module is a Neural Gas network that computes a Vector Quantization of the preprocessed camera 3D point cloud. The second module is an Evolution Strategy that estimates the robot motion parameters by performing a registration process, searching on the space of linear transformations, restricted to the translation and rotation, between the codebooks obtained for successive camera readings. The fitness function is the matching error between the predicted and the observed codebook corresponding to the next camera readings. In this paper, we report results of an implementation of this system tested on data from a real mobile robot, and provide several comparisons between our and other well-known registration algorithms.     

SLAM in Indoor Environments using Omni-directional Vertical and Horizontal Line Features

An autonomous mobile robot must have the ability to navigate in an unknown environment. The simultaneous localization and map building (SLAM) problem have relation to this autonomous ability. Vision sensors are attractive equipment for an autonomous mobile robot because they are information-rich and rarely have restrictions on various applications. However, many vision based SLAM methods using a general pin-hole camera suffer from variation in illumination and occlusion, because they mostly extract corner points for the feature map. Moreover, due to the narrow field of view of the pin-hole camera, they are not adequate for a high speed camera motion. To solve these problems, this paper presents a new SLAM method which uses vertical lines extracted from an omni-directional camera image and horizontal lines from the range sensor data. Due to the large field of view of the omni-directional camera, features remain in the image for enough time to estimate the pose of the robot and the features more accurately. Furthermore, since the proposed SLAM does not use corner points but the lines as the features, it reduces the effect of illumination and partial occlusion. Moreover, we use not only the lines at corners of wall but also many other vertical lines at doors, columns and the information panels on the wall which cannot be extracted by a range sensor. Finally, since we use the horizontal lines to estimate the positions of the vertical line features, we do not require any camera calibration. Experimental work based on MORIS, our mobile robot test bed, moving at a human’s pace in the real indoor environment verifies the efficacy of this approach.     

基于主动视觉和超声的机器人目标跟踪系统

运动目标跟踪技术是未知环境下移动机器人研究领域的一个重要研究方向。该文提出了一种基于主动视觉和超声信息的移动机器人运动目标跟踪设计方法,利用一台SONY EV-D31彩色摄像机、自主研制的摄像机控制模块、图像采集与处理单元等构建了主动视觉系统。移动机器人采用了基于行为的分布式控制体系结构,利用主动视觉锁定运动目标,通过超声系统感知外部环境信息,能在未知的、动态的、非结构化复杂环境中可靠地跟踪运动目标。实验表明机器人具有较高的鲁棒性,运动目标跟踪系统运行可靠。     

Wide-baseline stereo vision for terrain mapping

Terrain mapping is important for mobile robots to perform localization and navigation. Stereo vision has been used extensively for this purpose in outdoor mapping tasks. However, conventional stereo does not scale well to distant terrain. This paper examines the use of wide-baseline stereo vision in the context of a mobile robot for terrain mapping, and we are particularly interested in the application of this technique to terrain mapping for Mars exploration. In wide-baseline stereo, the images are not captured simultaneously by two cameras, but by a single camera at different positions. The larger baseline allows more accurate depth estimation of distant terrain, but the robot motion between camera positions introduces two new problems. One issue is that the robot estimates the relative positions of the camera at the two locations imprecisely, unlike the precise calibration that is performed in conventional stereo. Furthermore, the wide-baseline results in a larger change in viewpoint than in conventional stereo. Thus, the images are less similar and this makes the stereo matching process more difficult. Our methodology addresses these issues using robust motion estimation and feature matching. We give results using real images of terrain on Earth and Mars and discuss the successes and failures of the technique.