视觉传感系统的自主移动机器人路径规划方法

在未知环境下,机器人很难快速获取周边环境信息并建立实时环境地图,实现自主运行.为此提出基于视觉导航的方法,利用全景摄像机作为机器人的视觉传感器系统采集环境信息,将彩色地图进行HSI空间下模糊聚类图像分割,得到环境二值图像;将图像进行栅格化处理来构建环境地图,运用8方向连接的Dijkstra进行全局路径规划,计算出最优路径,从而实现移动机器人的快速、自主运动.经过仿真实验证明,该方法有效且可行.     

基于单目视觉的移动机器人导航算法研究进展

基于单目视觉的移动机器人导航的研究,涵盖了机器视觉、模式识别和多目标跟踪多个领域．其算法框架不仅成功应用于移动机器人导航,还为目标检测、识别与跟踪领域的研究提供了可供参考的模型．该综述将以算法发展历史为脉络,结合一些典型系统,通过对关键技术和算法结构的分析比较,总结算法本身的发展前景和由此发展起来的可供相关研究参考的算法框架．     

基于视觉的机器人自主定位与障碍物检测方法

针对稀疏型同时定位与地图构建（SLAM）算法环境信息丢失导致无法检测障碍物问题，提出一种基于视觉的机器人自主定位与障碍物检测方法。首先，利用双目相机得到观测场景的视差图。然后，在机器人操作系统（ROS）架构下，同时运行定位与建图和障碍物检测两个节点。定位与建图节点基于ORB-SLAM2完成位姿估计与环境建图。障碍物检测节点引入深度阈值，将视差图二值化；运用轮廓提取算法得到障碍物轮廓信息并计算障碍物凸包面积；再引入面积阈值，剔除误检测区域，从而实时准确地解算出障碍物坐标。最后，将检测到的障碍物信息插入到环境的稀疏特征地图当中。实验结果表明，该方法能够在实现机器人自主定位的同时，快速检测出环境中的障碍物，检测精度能够保证机器人顺利避障。     

移动机器人最优路径规划方法

针对移动机器人路径规划效率低的问题,提出了一种基于改进的最短路径快速算法的移动机器人最优路径规划方法。该方法在障碍Voronoi图基础上根据规则将起始点与终点加入该图以得到无碰撞路径图,然后采用改进的最短路径快速算法搜索从起始点到终点的最优无碰撞路径。仿真结果表明,采用该改进算法后,移动机器人能够沿着最优无碰撞路径前进,快速达到终点。     

带精英策略和视觉探测蚁群算法的机器人路径规划

算法中加入精英蚂蚁,拓展了蚂蚁搜索的范围,加强了蚂蚁通过狭长空间和复杂空间的能力。引入视觉探视功能,扩大了蚂蚁的视野,使蚂蚁在探索的过程中具备了“千里眼”,减少了蚂蚁探索的步数。运用路径优化功能,使蚂蚁运动路径拉直、变短,使路径可用性增强。采用栅格划分环境、栅格之间的边表示信息素和距离信息减少了算法计算量。     

Object tracking for mobile robot based on intelligent method

This paper proposed the real-time tracking algorithm in the active camera system, which is based on the intelligent method. To separate the object from background, the similarity of the color is analyzed through the fuzzy inference engine. And after segmentation, the local difference method between the continuous images is used to track the moving object. The experiment is performed using the developed embedded camera system with an ARM processor. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Robust RBPF for mobile robot using panoramic vision

We present a novel vision-based approach to self-localization that uses an improved scheme to integrate feature-based matching of panoramic images with a Rao-Blackwellized Particle Filter （RBPF） for mobile robot Simultaneous Localization and Mapping （SLAM）. The matching for SIFT multi-dimension features is implemented with a KD-tree which introduce the Mahalanobis distance instead of the Euclidean distance for matching features. The particle filter is combined with Gaussian Mixture Unscented Particle Filters （GMUPF） to for initializing landmarks and a Single-Cluster Graph-Partitioning algorithm for outlier rejection. The landmark position estimation and update is also implemented through GMUPF by which a single update step from moving and sensing can be done and the change to the map certainty can be done in constant time. Experiment results on real robot in our indoor environment show the advantages of our methods over previous approaches.     

Salient human detection for robot vision

In this paper, we propose a salient human detection method that uses pre-attentive features and a support vector machine (SVM) for robot vision. From three pre-attentive features (color, luminance and motion), we extracted three feature maps and combined them as a salience map. By using these features, we estimated a given object’s location without pre-assumptions or semi-automatic interaction. We were able to choose the most salient object even if multiple objects existed. We also used the SVM to decide whether a given object was human (among the candidate object regions). For the SVM, we used a new feature extraction method to reduce the feature dimensions and reflect the variations of local features to classifiers by using an edged-mosaic image. The main advantage of the proposed method is that our algorithm was able to detect salient humans regardless of the amount of movement, and also distinguish salient humans from non-salient humans. The proposed algorithm can be easily applied to human robot interfaces for human-like vision systems.

一种改进的基于机器视觉的边缘检测算法

图像的边缘保留了图像最基本也是最重要的特征，边缘检测减少了数据量，在机器视觉系统中得到广泛的应用，是图形图像领域里的重要研究方向之一．本文提出了一种基于小波变换技术和数学形态学的边缘检测方法，并用智能图像传感器获得的图像教据对所提出的方法进行了验证．实验结果表明：该方法可较好地提取视觉图像的边缘信息．     

Building facade detection,segmentation, and parameter estimation for mobile robot stereo vision

Building facade detection is an important problem in computer vision, with applications in mobile robotics and semantic scene understanding. In particular, mobile platform localization and guidance in urban environments can be enabled with accurate models of the various building facades in a scene. Toward that end, we present a system for detection, segmentation, and parameter estimation of building facades in stereo imagery. The proposed method incorporates multilevel appearance and disparity features in a binary discriminative model, and generates a set of candidate planes by sampling and clustering points from the image with Random Sample Consensus (RANSAC), using local normal estimates derived from Principal Component Analysis (PCA) to inform the planar models. These two models are incorporated into a two-layer Markov Random Field (MRF): an appearance- and disparity-based discriminative classifier at the mid-level, and a geometric model to segment the building pixels into facades at the high-level. By using object-specific stereo features, our discriminative classifier is able to achieve substantially higher accuracy than standard boosting or modeling with only appearance-based features. Furthermore, the results of our MRF classification indicate a strong improvement in accuracy for the binary building detection problem and the labeled planar surface models provide a good approximation to the ground truth planes.     

基于里程计和PTZ视觉的移动机器人自定位

针对机器人长距离运行时里程计定位存在累积误差问题,提出一种基于里程计和PTZ视觉的移动机器人自定位算法。提出了中断式S形搜索策略的概念,设计了基于有限自动机的视觉定位方法;分析了里程计和视觉定位误差来源,分别建立了其定位信度模型;并基于该模型建立里程计和PTZ视觉定位的框架。针对视觉定位及里程计视觉复合定位分别进行了实验,结果验证了该方法的有效性和实用性。     

基于单目视觉的移动机器人测距方法

本文针对多机器人编队过程中的跟踪控制,提出了一种跟踪机器人采用单目视觉技术获取前方被跟踪机器人距离信息的方法.该方法首先对跟踪机器人摄像机进行内参数标定,并在目标机器人背部设置视觉标记.然后系统获取目标机器人的含有视觉标记的单帧图像,预处理此图像,并识别出图像中的视觉标记所在的目标区域,用Hu氏不变矩计算该区域形心.最后推导出单目测距算法,利用图像信息和其它参数可以计算出两机器人之间的距离.实验结果表明,所设计的单目测距系统能得到准确的距离,为跟踪控制提供了重要的反馈信息.     

基于自组织LMBPNN的移动机器人路径规划器

提出一种自组织LMBP神经网络,并将之用于移动机器人免碰路径规划。该算法首先用基于距离传感器的底层局部路径规划器生成初始路径,然后用自组织神经网络将该路径进行样本数据分类,之后将自组织神经网络的权值作为LMBP的输出样本,移动机器人的起始点与目标点作为LMBP神经网络的输入样本进行学习。这样,不但解决了三层LMBP样本若庞大则增加存贮、运行成本,以及数据冗余问题,并且随着机器人对未知环境探索的增多,所构建的地图越趋丰满。仿真结果说明该方法很好效。     

An observer-controller combination for a unicycle mobile robot

This paper considers the problem of output feedback trajectory tracking with a unicycle mobile robot system. A state-feedback controller for the non-linear error dynamics of the robot is combined with an observer that estimates the orientation error based on available trajectory information and measurement of the position coordinates. A stability analysis, derivation and implementation of the resulting controller is given and tested on an experimental mobile robot.     

自主机器人视觉子系统关键技术

本文介绍了自主机器人视觉系统的体系结构,并对视觉处理技术和主要算法进行了研究.同时提出了目已的改进算法.     

基于CPLD的彩色视觉移动机器人路径跟踪系统

路径跟踪是机器人视觉导航控制基本技术之一,为使机器人沿地面彩色引导线自主运动,并能在适时离线执行任务后自动返航,提出了一种用可编程逻辑器件(CPLD)实现的视觉伺服PID控制方法.该方法利用图像特征反馈对其所跟踪的路经进行实时识别跟踪.仿真结果表明,该方法改善了控制算法的实时性,提高了移动机器人的路径跟踪精度与速度.     

Centering behavior with a mobile robot using monocular foveated vision

A system for corridor following based on properties of the human visual system is presented. The robot extracts image features using an interest operator to compute sparse optical flow induced by the translatory motion of the robot. The available status information from the robot is used to compensate for the known rotatory movement of the image. Control of the robot is done by transforming the optical flow to ego-motion complex log space. The difference between the median flow extracted from the left and right peripheral visual areas is used to control the heading of the robot. Forward velocity is controlled by trying to keep the perceived optical flow constant.     

基于角点聚类的移动机器人自然路标检测与识别

针对未知环境中机器人视觉导航的自然路标检测,提出了一种基于角点聚类的自然路标局部特征提取、不变性表示及其匹配算法.用SUSAN算子提取左右视图中的角点,在极线约束下对左右视图的角点进行匹配,消除遮挡或噪声引起的角点;同时应用立体视觉计算角点视差,进一步筛选角点.根据角点聚类策略提取自然路标局部特征,并提出不随距离、角度变化的局部特征不变性表示及匹配方法.理论分析和实验结果表明,该算法具有较好的鲁棒性,在一定距离和角度变换下能够对路标进行正确识别.     

移动机器人目标分割及测距方法研究

对于移动机器人单目视觉避障导航问题,研究了室内环境中多障碍物目标图像分割与目标定位。提出一种将HSI彩色图像空间序列分割与Otsu法选取阈值相结合的图像分割方法,并采用基于亮度均值的幂次变换方法改进亮度空间的对比度,从背景环境中分割提取出多个目标所在区域的像素坐标。基于透视投影原理,应用目标定位的几何方法得到目标的空间坐标。该方法在Pioneer-2移动机器人平台上进行了实验,论证了所提出方法的实用性和有效性。     

An automated method for type synthesis of planar linkages based on a constrained subgraph isomorphism detection

We present a method to enumerate and codify all non-isomorphic solutions, for the problem of synthesizing the type of single-DOF linkage mechanisms that satisfy structural requirements for a given kinematic problem. The method is based on the construction of an “initial graph” taking into account prescribed parts (such as fixations, moving bodies, joints and their interconnections) and the kinematic constraints imposed on them. This initial graph containing structural characteristics of the problem is used as a pattern to search inside a selected atlas of one-DOF mechanism also represented by graphs. A new graph-matrix representation of mechanisms and a mechanism identifier based on the degree code concept was developed to avoid isomorphic occurrences of the initial graph inside each mechanism of the atlas. The same tools were used to enumerate various atlases specialized in a non-isomorphic way from basic kinematic chains. This enumeration takes into account different types of links (rigid, flexible) and joints (revolute, prismatic, flexible, clamped), and proper restrictions were designed to avoid kinematically invalid topologies. The methodology is illustrated with examples for several kinematic tasks.