基于四足机器人的工业仪表数字识别方法研究

在工业现场自主巡检中,由于定位误差和光线角度等因素的影响,使得四足机器人仅依靠机器视觉难以实现高精度的仪表数字识别。针对上述问题,提出一种结合移动机器人运动的工业仪表数字识别方法。该方法首先基于图像感知的四足机器人控制策略实现仪表对准,来获取大小适中的仪表图片,进而使用改进自动色彩均衡(ACE)算法提高图片清晰度,并使用改进高效准确的场景文本(EAST)检测器来优化仪表数字漏检情况,最后获得仪表数字识别结果。在基于四足机器人的工业巡检实验平台中验证了该识别方法,实验结果表明上述方法对工业仪表数字识别准确率达97.75%。     

基于场景匹配的移动机器人避障

研究环境未知情况下移动机器人的避障问题，提出一种基于模糊场景匹配的移动机器人避障方法．该方法对多种传感器的信息进行融合，生成当前环境的场景并与场景库中的场景进行匹配，利用匹配结果并通过模糊控制器得到机器人的运动参数，对机器人的避障进行控制,实验结果表明了该方法的正确性和有效性。     

基于直线和单特征点的移动机器人视觉推算定位法

提出了一种基于直线和单特征点的视觉推算定位方法．将摄像机固定安装在移动机器人上,并使摄像机的方向垂直于天花板．提取天花板图像中的两条相交直线及其交点作为特征,利用直线的方向估计机器人的方位,再结合相交点的像素坐标变化量推算出机器人的位置．机器人运动过程中,用作特征的交点接近图像边缘时,更换新的交点作为特征点．室内定位实验结果验证了该方法的有效性．     

多传感器苹果采摘机器人定位系统研究

本文研究了一种适用于苹果园中自主移动机器人的定位系统.该系统采用以CCD传感器为感应元件的数码相机获取苹果园场景视频信息,以作者开发的图象处理软件实现路标的提取和识别,并利用激光测距传感器测定机器人与路标的精确距离,结合以上信息,应用直接推理的路标全局定位方法,实现苹果园中移动采摘机器人的定位.仿真实验表明,该系统具有较高的定位精度和鲁棒性,定位精度介于95.6～99.0%之间,符合苹果采摘机器人定位要求.     

基于多传感器信息融合的机器人导航与定位技术研究

移动机器人已经服务于各个领域,作为室内移动机器人服务的前提是实现机器人精准定位。文章介绍了一种射频识别（Radio Frequency IDentification,RFID）和即时定位与地图构建（Simultaneous Localization and Mapping,SLAM）多传感器融合库存盘点机器人精准导航与定位的方法。通过读取布置在环境中的RFID标签,获取机器人的大概位置信息和准确运动状态,然后结合SLAM技术实现精准定位。实验结果表明,该方法可以使库存盘点机器人精准定位,且系统搭建简单、成本低,对室内机器人定位具有参考价值。     

基于车体对称及颜色聚类的车牌自动定位方法*

对车牌区域的准确定位是实现自动车牌识别的一个关键问题。提出了一种综合考虑车体对称特性以及采用颜色聚类的高效车牌定位算法。该算法首先对待分析图像所属的场景进行识别,自动将图像分类为白天场景类或夜晚场景类;然后针对不同场景图像使用不同的车牌定位方案进行处理。对夜间场景中的车牌采用二次定位法,而对白天场景中的车牌采用结合车体对称轴定位并考虑车牌区域文本特征的方法。实验结果显示,该方法对图像场景分类以及车牌定位的总体准确率都达到了95.7%。     

改进直接线性标定的移动机器人视觉定位算法

在移动机器人视觉定位中,图像处理技术为摄像机标定和视觉定位奠定了基础.本文首先对机器人采集得到的目标图像进行灰度变换,将彩色的图像转换为灰度图像,利用中值滤波滤除目标图像中的孤立噪声点,再使用sobel算子提取图像的边缘,由于提取的边缘不清晰,最终使用改进的hough变换得到图像的清晰边缘.实验中利用matlab 7.5软件对图像进行处理,获取到准确的目标点像素.而要得到机器人在实际坐标系中的坐标,需要对摄像机进行标定,本文对直接线性标定算法进行改进,利用最小二乘法简化计算的过程,实现机器人视觉定位过程.实验结果表明,该方法简单且有效,提高了定位的准确度,验证了该方法在视觉定位中的有效性和可行性.     

基于激光测距AdaBoost分类的室内环境识别

移动机器人所处环境的地点语义信息能够提高机器人自主定位、路径规划和人机互动的能力.为了让机器人识别环境中不同地点类型,提出一种对机器人所处环境地点类型进行语义分类的方法.该方法对激光传感器的测距数据进行特征提取,通过提取的样本集利用强化学习AdaBoost方法构建分类器,对于环境中多类型地点分类识别,将获得的二分类器有顺序地排列建立分类列表形成多分类器,将获得的多分类器运用到房间、走廊和门口的分类识别中.实验结果表明:移动机器人通过该方法都能对环境下不同地点类型进行有效的分类识别.     

图像尺寸对机器人视觉定位实时性的影响研究

尺度不变特征变换（Scale Invariant Feature Transform,SIFT）算法由于具有尺度、旋转和光照不变等特性,被广泛应用到移动机器人视觉定位领域。由于定位计算中使用的特定标示物图像及背景图像的尺寸对定位计算时间影响较大,为提高移动机器人视觉定位的实时性能,分别改变待识别图像以及背景图像的尺寸,进行了图像匹配实时性测试实验。实验结果表明,特定标示物图像和背景图像的尺寸缩小到40%～60%时,移动机器人视觉定位实时性提高30%～40%左右,特征点数减少20%～30%左右。在满足定位精度要求的情况下,大大提高了定位的实时性。由此可知,通过合理选择图像的尺寸不仅可以满足匹配精度,还能提高移动机器人的视觉定位效率,对于类似该实验使用的移动机器人视觉定位具有广泛的指导意义。     

基于粒子滤波器的移动机器人自定位方法研究

首先,对粒子滤波器的原理进行了简要阐述。然后详细描述了基于粒子滤波器的移动机器人自定位算法——蒙特卡洛定位算法。在ROS(Robot Operating System)平台上对该算法进行了仿真实验并分析了其性能。最后,对蒙特卡洛粒子滤波定位方法用于移动机器人定位进行了总结。结果表明,MCL(蒙特卡洛)算法是一种精确鲁棒的移动机器人概率定位方法,可对解决移动机器人的定位问题提供有意义的参考。提出的机器人自定位方法为机器人在Robocup竞赛中自主执行各种作业提供定位支持,已在2013年中国机器人大赛获奖。     

Vision-based 3-D trajectory tracking for unknown environments

This paper describes a vision-based system for 3-D localization of a mobile robot in a natural environment. The system includes a mountable head with three on-board charge-coupled device cameras that can be installed on the robot. The main emphasis of this paper is on the ability to estimate the motion of the robot independently from any prior scene knowledge, landmark, or extra sensory devices. Distinctive scene features are identified using a novel algorithm, and their 3-D locations are estimated with high accuracy by a stereo algorithm. Using new two-stage feature tracking and iterative motion estimation in a symbiotic manner, precise motion vectors are obtained. The 3-D positions of scene features and the robot are refined by a Kalman filtering approach with a complete error-propagation modeling scheme. Experimental results show that good tracking and localization can be achieved using the proposed vision system.     

移动机器人编队视觉定位方法研究

该文提出一种基于视觉的移动机器人编队定位方法。该方法采用基于纹理的图象对机器人进行标识,然后使用纹理中的特征点对机器人的位置和姿态进行估计,使用最小二乘算法使估计结果误差最小。实验证明该方法能够有效地对编队的移动机器人定位,同时对环境干扰具有鲁棒性。     

Robot navigation using panoramic tracking

A vision-based navigation system is presented for determining a mobile robot's position and orientation using panoramic imagery. Omni-directional sensors are useful in obtaining a 360° field of view, permitting various objects in the vicinity of a robot to be imaged simultaneously. Recognizing landmarks in a panoramic image from an a priori model of distinct features in an environment allows a robot's location information to be updated. A system is shown for tracking vertex and line features for omni-directional cameras constructed with catadioptric (containing both mirrors and lenses) optics. With the aid of the panoramic Hough transform, line features can be tracked without restricting the mirror geometry so that it satisfies the single viewpoint criteria. This allows the use of rectangular scene features to be used as landmarks. Two paradigms for localization are explored, with experiments conducted with synthetic and real images. A working implementation on a mobile robot is also shown.     

Feature analysis for human recognition and discrimination: Application to a person-following behaviour in a mobile robot

One of the most important abilities that personal robots need when interacting with humans is the ability to discriminate amongst them. In this paper, we carry out an in-depth study of the possibilities of a colour camera placed on top of a robot to discriminate between humans, and thus get a reliable person-following behaviour on the robot. In particular we have reviewed and analysed the possibility of using the most popular colour and texture features used in object and texture recognition, to identify and model the target (person being followed). Nevertheless, the real-time restrictions make necessary the selection of a reduced subset of these features to reduce the computational burden. This subset of features was selected after carrying out a redundancy analysis, and considering how these features perform when discriminating amongst similar human torsos. Finally, we also describe several scoring functions able to dynamically adjust the relevance of each feature considering the particular conditions of the environment where the robot moves, together with the characteristics of the clothes worn by the persons that are in the scene. The results of this in-depth study have been implemented in a novel and adaptive system (described in this paper), which is able to discriminate between humans to get reliable person-following behaviours in a mobile robot. The performance of our proposal is clearly shown through a set of experimental results obtained with a real robot working in real and difficult scenarios.     

未知环境中基于视觉显著区域的拓扑定位

针对移动机器人在未知环境中的导航问题,提出并实现一个新的基于视觉显著区域的拓扑定位系统。首先采用中心—周围差方法在多尺度图像空间中计算颜色及纹理对比,根据检测出的显著线索构造适宜尺寸的显著区域。然后将这些场景中的视觉显著区域利用隐马尔科夫模型组织成为拓扑图中的一个顶点,从而将定位问题转化为隐马尔科夫模型(HMM)的估值问题。该系统支持机器人在线建立环境的拓扑模型,同时进行定位。实验结果表明,该方法能够在机器人移动过程中发生尺度、2维旋转、视角等变化时稳定地检测出显著视觉区域,场景识别率较高。实验证明该定位系统有能力保证机器人在未知环境中的安全导航。     

Omnidirectional Vision and Inertial Clues for Robot Navigation

The structural features inherent in the visual motion field of a mobile robot contain useful clues about its navigation. The combination of these visual clues and additional inertial sensor information may allow reliable detection of the navigation direction for a mobile robot and also the independent motion that might be present in the 3D scene. The motion field, which is the 2D projection of the 3D scene variations induced by the camera‐robot system, is estimated through optical flow calculations. The singular points of the global optical flow field of omnidirectional image sequences indicate the translational direction of the robot as well as the deviation from its planned path. It is also possible to detect motion patterns of near obstacles or independently moving objects of the scene. In this paper, we introduce the analysis of the intrinsic features of the omnidirectional motion fields, in combination with gyroscopical information, and give some examples of this preliminary analysis. © 2004 Wiley Periodicals, Inc.     

Evolutionary computing based mobile robot localization

Evolutionary computing techniques, including genetic algorithms (GA), particle swarm optimization (PSO) and ants system (AS) are applied to the localization problem of a mobile robot. Salient features of robot localization are that the system is partially dynamic and information for fitness evaluation is incomplete and corrupted by noise. In this research, variations to the above three evolutionary computing techniques are proposed to tackle the specific dynamic and noisy system. Their performances are compared based on simulation and experiment results and the feasibility of the proposed approach to mobile robot localization is demonstrated.     

Self-localization and obstacle avoidance for a mobile robot

This article presents a fast self-localization method based on ZigBee wireless sensor network and laser sensor, an obstacle avoidance algorithm based on ultrasonic sensors for a mobile robot. The positioning system and positioning theory of ZigBee which can obtain a rough global localization of the mobile robot are introduced. To realize accurate local positioning, a laser sensor is used to extract the features from environment, then the environmental features and global reference map can be matched. From the matched environmental features, the position and orientation of the mobile robot can be obtained. To enable the mobile robot to avoid obstacle in real-time, a heuristic fuzzy neural network is developed by using heuristic fuzzy rules and the Kohonen clustering network. The experiment results show the effectiveness of the proposed method.     

移动机器人基于多传感器信息融合的室外场景理解

本文研究了移动机器人多传感器信息融合技术,提出一种融合激光测距与视觉信息的实时室外场景理解方法．基于三维激光测距数据构建了高程图描述场景地形特征,同时利用条件随机场模型从视觉信息中获取地貌特征,并以高程图中的栅格作为载体,应用投影变换和信息统计方法将激光信息与视觉信息进行有效融合．在此基础上,对融合后的环境模型分别在地形和地貌两个层面进行可通过性评估,从而实现自主移动机器人实时室外场景理解．实验结果和数据分析验证了所提方法的有效性和实用性．