一种新型的并联机器人位姿立体视觉检测系统

建立了一种并联机器人位姿立体视觉测量系统框架,主要包括图像采集与传输、摄像机标定、尺度不变量特征变换（SIFT）匹配、空间点重建和位姿测量五个部分。该系统基于SIFT,能够很好地处理图像在大视角有遮挡、平移、旋转、亮度和尺度变化时的特征点匹配,有较高的匹配精度,特别适用于对并联机器人多自由度和空间复杂运动的检测。最后使用该方法对并联机器人位姿检测做了仿真实验。     

空间机器人捕获非合作目标的测量与规划方法

提出了基于立体视觉的空间机器人捕获非合作目标的测量与规划方法,包括图像处理与特征提取、3D 重构及位姿测量、自主规划与控制等．同时,还建立了一套仿真系统,可进行包括图像采集、位姿测量、追踪星 GNC、空间机器人规划与控制、航天器动力学计算等在内的闭环仿真．仿真结果验证了算法的有效性．     

服务机器人目标同时识别与位姿判定研究

目前机器人尚难以自主建立起目标姿态的概念,通过人机交互方式辅助机器人对目标建模则不失为一种可行方案。提出了一种基于新型目标模型的离线/在线两步目标识别与位姿判定系统。离线阶段采用人机交互方式构建目标模型,在线阶段通过场景图像与目标模型的特征点对应即可自动发现目标,并计算其相对于机器人的位姿。实验证明,该系统能够有效完成对纹理目标的同时识别与位姿判定。     

基于视觉测量的液压支架位姿检测方法

针对目前综采工作面液压支架位姿测量方法存在实施成本高、精度和实时性较差的问题,提出了一种基于视觉测量的液压支架位姿检测方法。通过在液压支架上设置红外LED标识板,利用安装在采煤机上的防爆相机进行图像采集,基于4个共面特征点的视觉算法对图像进行处理与解算,得到液压支架底座的位姿,根据液压支架的位姿坐标可以得到其在多个方向的直线度。实验结果表明,该方法测量的液压支架位姿检测精度在0.7°以内,直线度精度在20mm以内,满足综采工作面液压支架的检测精度要求。     

机器视觉在物体位姿检测中的应用

研究了机器视觉技术在物体位姿检测中的应用.从两个方位架设工业相机采集目标图片,采用张正友基于2D平面靶标的相机标定方法对相机进行标定.使用目标图像减去背景图像和形态学运算的方法进行粗定位,初步检测目标区域.利用Hough变换检测目标直线边缘,依据极线约束原则、灰度相似性以及直线约束进行边缘直线及其端点的匹配.计算出各直线端点的空间三维坐标及其单位方向向量.在已知目标模板的条件下,目标的几何参数已知,利用简单的几何关系求取目标的位姿数据.     

基于Kinect的机器人辅助超声扫描系统研究

提出一种采用Kinect传感器作为视觉伺服的机器人辅助超声扫描系统,来规划引导机器人的扫描路线,以实现机器人辅助的超声扫描操作。系统由Kinect传感器、机器人和超声探头组成。采用Kinect实时获取超声探头的RGB图像和深度图像,并计算探头当前位姿,结合坐标系配准结果,得到机器人的位姿信息,再根据术前的机器人轨迹规划,引导机器人的超声扫描路径。开展腿部模型实验验证本系统的可行性,通过对Kinect传感器的相机标定实验,计算得到了RGB相机和深度相机的内外参数,通过对探头上标识物的定位,进而计算出探头当前位姿,结合Kinect与机器人坐标系的配准结果,得到了两者的转换矩阵,并对机器人的位置给出指令,引导机械臂夹持探头到达指定扫描位置。在机器人夹持超声探头扫描过程中,实时计算探头与腿部之间的距离,以保证所采集超声图像的质量及扫描操作的安全性。实验结果表明,在Kinect视觉系统的导航引导下,机器人可以夹持超声探头实现自主超声扫描,以减少超声医师的扫描时间,降低医师的劳动强度。     

机器人柔性视觉检测系统现场标定技术

针对江淮车身厂的轿车车身提出了一种基于工业机器人的柔性视觉检测系统．通过控制机器人在空间的位姿变换,立体视觉传感器能够依次到达空间指定测量位置采集空间特征点的图像信息,并通过计算图像数据获得该点的三维坐标数据．利用双经纬仪和精密靶标对机器人视觉测量系统进行整体标定,同时完成了测试试验数据的采集及计算,取得了较好的测量效果．该系统柔性好、测量无死角,能适应生产线多车型检测的需要．     

基于立体视觉的串联机器人跟踪检测系统

建立一种六自由度串联机器人视觉跟踪检测系统框架,包括图像采集、摄像机标定、机器臂跟踪检测、机器臂位姿建模与计算等。提出利用CamShift算法对机器人进行在线粗跟踪,搜寻和画定出机器臂操作器在当前窗口的区域位置。对跟踪到的机器臂按照SURF算法进行特征提取与立体匹配。该方法被用于对串联机器人位姿检测进行实验。实验结果表明,2种算法的结合适用于六自由度串联机器人在空间复杂运动的跟踪检测。     

基于实例分割网络与迭代优化方法的3D视觉分拣系统

针对工业上常见的弱纹理、散乱堆叠的物体的检测和位姿估计问题,提出了一种基于实例分割网络与迭代优化方法的工件识别抓取系统.该系统包括图像获取、目标检测和位姿估计3个模块.图像获取模块中,设计了一种对偶RGB-D相机结构,通过融合3张深度图像来获得更高质量的深度数据;目标检测模块对实例分割网络Mask R-CNN(region-based convolutional neural network)进行了改进,同时以彩色图像和包含3维信息的HHA(horizontal disparity,height above ground,angle with gravity)特征作为输入,并在其内部增加了STN(空间变换网络)模块,提升对弱纹理物体的分割性能,结合点云信息分割目标点云;在目标检测模块的基础上,位姿估计模块利用改进的4PCS(4-points congruent set)算法和ICP(迭代最近点)算法将分割出的点云和目标模型的点云进行匹配和位姿精修,得到最终位姿估计的结果,机器人根据此结果完成抓取动作.在自采工件数据集上和实际搭建的分拣系统上进行实验,结果表明,该抓取系统能够对不同形状、弱纹理、散乱堆叠的物体实现快速的目标识别和位姿估计,位置误差可达1 mm,角度误差可达1°,其性能可满足实际应用的要求.     

P3P位姿测量方法的误差分析

从工程应用的角度出发,在三个控制点构成等腰三角形的条件下,研究P3P位姿测量方法的误差与输入参数误差的关系。首先在理论上推导出测量位姿误差与输入参数误差间的关系,在此基础上通过误差统计分析和直接仿真验证的方式得到在输入误差中,图像坐标的检测误差和像机内参数标定误差对测量位姿误差的影响较大,而目标模型的测量误差对测量位姿误差的影响可以忽略不计的结论。这对位姿测量系统的设计与实现具有一定的指导意义。     

A new monocular vision measurement method to estimate 3D positions of objects on floor

A new visual measurement method is proposed to estimate three-dimensional (3D) position of the object on the floor based on a single camera. The camera fixed on a robot is in an inclined position with respect to the floor. A measurement model with the camera’s extrinsic parameters such as the height and pitch angle is described. Single image of a chessboard pattern placed on the floor is enough to calibrate the camera’s extrinsic parameters after the camera’s intrinsic parameters are calibrated. Then the position of object on the floor can be computed with the measurement model. Furthermore, the height of object can be calculated with the paired-points in the vertical line sharing the same position on the floor. Compared to the conventional method used to estimate the positions on the plane, this method can obtain the 3D positions. The indoor experiment testifies the accuracy and validity of the proposed method.     

机器人手眼协调的图象直接反馈方法*

本文从视觉与控制集成观点出发，提出了一种机械人手眼协调的新方法，利用手部机于期望抓取（操作）位置获取待待抓（加工）物体图象，以此作为期望图象，而后可以对任意位置的同类物体进行实时抓取，首先将期望图象与实时采样图象进行比较生成差图象，从而依据差图象进行实时伺服反馈，最终达到消除图象差的目的。     

Adaptive position and orientation regulation for the camera‐in‐hand problem

This paper considers the camera‐space position and orientation regulation problem for the camera‐in‐hand problem via visual serving in the presence of parametric uncertainty associated with the robot dynamics and the camera system. Specifically, an adaptive robot controller is developed that forces the end‐effector of a robot manipulator to move such that the position and orientation of an object are regulated to a desired position and orientation in the camera‐space, despite parametric uncertainty throughout the entire robot‐camera system. An extension is also provided that illustrates how slight modifications can be made to the camera‐in‐hand control law to achieve adaptive position and orientation tracking of the end‐effector in the camera‐space for a fixed‐camera configuration. Simulation results are provided to illustrate the performance of the adaptive, camera‐in‐hand controller. © 2005 Wiley Periodicals, Inc.     

Position estimation of a mobile robot using images of a moving target in intelligent space with distributed sensors

Latest advances in hardware technology and state-of-the-art of mobile robots and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. A mobile service robot requires the perception of its present position to co-exist with humans and support humans effectively in populated environments. To realize this, a robot needs to keep track of relevant changes in the environment. This paper proposes localization of a mobile robot using images recognized by distributed intelligent networked devices in intelligent space (ISpace) in order to achieve these goals. This scheme combines data from the observed position, using dead-reckoning sensors, and the estimated position, using images of moving objects, such as a walking human captured by a camera system, to determine the location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the ISpace. Using the a priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates for a moving object and the estimated robot's position are derived. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot, and the Kalman filtering scheme is used for the estimation of the mobile robot location. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in determining the location of a mobile robot, and its performance is verified by computer simulation and experiment.     

Robust exponential stabilization of nonholonomic wheeled mobile robots with unknown visual parameters

The visual servoing stabilization of nonholonomic mobile robot with unknown camera parameters is investigated.A new kind of uncertain chained model of nonholonomic kinemetic system is obtained based on the visual feedback and the standard chained form of type (1,2) mobile robot.Then,a novel time-varying feedback controller is proposed for exponentially stabilizing the position and orientation of the robot using visual feedback and switching strategy when the camera parameters are not known.The exponential stability of the closed-loop system is rigorously proven.Simulation results demonstrate the effectiveness of the method proposed in this paper.     

A three-dimensional measurement system for robot applications

This paper presents a 3D noncontacting sensor system designed to measure the position and orientation of a robot end effector. This measurement system includes two parts: a tridimensional object including four spheres placed along the axes of a tetrahedron and a set of three orthogonally pointed cameras. The purpose is to design a measurement system characterized by easy relationships in order to satisfy real-time constraints. The system has been used in two experiments: first, to calibrate a parallel robot and validate the geometrical control performance, then as an exteroceptive sensor in an assembly task. The system computes position and orientation of the tetrahedron in 100 ms time. The position and orientation accuracy are, respectively, 0.6 mm and 0.2 deg in a workspace, being a cube with 0.3 m sides.     

基于双目视觉的移动机器人空间测距研究

空间测距是移动机器人实时避障和路径规划的基础.研究了移动机器人双目摄像头由一般布置形式变换到标准布置形式进而实现空间测距的方法.首先对摄像头进行标定(对双目摄像头进行立体标定);然后基于标定结果对摄像头一般布置下所获得立体图像进行极线校正,并得到校正后新的摄像机相互位置参数;最后基于已校正图像和新的位置参数实现移动机器人移动过程中深度与特定目标空间尺寸测量.实验结果证实了本文方法能获得比较精确的测量结果.     

三维立体视觉机械臂智能抓取分类系统的开发

采用工业相机、工业投影机、普通摄像头、计算机和机械臂开发了一套具有三维立体视觉的机械臂智能抓取分类系统。该系统采用自编软件实现了对工业相机、工业投影机的自动控制和同步，通过前期研究提出的双波长条纹投影三维形貌测量法获取了物体的高度信息，结合opencv技术和普通摄像头获取的物体二维平行面信息，实现了物体的自动识别和分类；利用串口通信协议，将上述处理后的数据传送至机械臂，系统进行几何姿态解算，实现了智能抓取，并能根据抓手上压力反馈自动调节抓手张合程度，实现自适应抓取。经实验证明该系统能通过自带的快速三维形貌获取装置实现准确、快速的抓取工作范围内的任意形状的物体并实现智能分类。