结构光视觉测量机器人的标定

构建了由线结构光测头和机器人组成的视觉测量系统. 利用齐次坐标变换的方法建立了测量系统的数学模型. 针对数学模型中的手眼关系, 使用半径已知的球体, 采用定点变位姿的方法, 进行粗略的估计. 考虑到机器人的绝对精度较低, 将定点信息和尺度信息作为优化条件, 对计算机器人的运动学参数和手眼关系做进一步寻优. 实验表明文中的方法能够有效提高测量机器人的重复精度.     

简便高精度的机器人手眼视觉标定方法

对摄像机成像模型进行了分析,论述了机器人手眼系统标定原理。在此基础上,不改变机器人的外臂与基坐标系的旋转关系,设计了一种机器人手眼视觉的标定方法,与传统的方法比较,它不需要预先标定摄像机的内外参数。实验证明:该方法具有算法方便快捷、实验过程简单易行,且精度高等优点,可用于机器人进行运动目标定位与跟踪。     

三维视觉测量系统标定技术

在机器人视觉测量系统中,手眼标定问题一直是一个很重要也很难解决的问题.结合一个具体的实际系统,提出了一种将摄像机外参数矩阵和手眼矩阵合在一起标定的新方法,免去了传统的摄像机外参数标定和手眼标定的麻烦,并因此减小了误差,提高了测量精度.     

机器人运动学标定综述

从基于运动学模型的几何参数标定、机器人自标定、神经网络的正标定和逆标定三个方面,对机器人运动学标定方法及其研究现状进行了分析总结。详细介绍了每种标定方法的特点、存在的问题以及研究现状。最后对多机器人协作系统的标定以及运动学标定的发展方向进行了简要论述。     

基于ROS的机器人自动手眼标定系统设计

为了缩短标定的时间,设计了一种基于ROS的机器人自动手眼标定系统。通过分析坐标系间的转换关系,建立了手眼标定的数学模型,推导了两步法的具体解法。为了尽可能减小标定误差,提出了带约束的随机增量法,并根据角点检测结果实时剔除标定过程中出现的无效图像。基于ROS软件平台完成了驱动、运动空间规划、图像处理和标定解算等功能模块的逻辑设计和系统级联。以6自由度工业机械臂UR3和RGB-D相机Kinect2构建了手眼标定实验系统,实验结果表明,与传统标定流程相比,所设计的自动手眼标定系统便捷高效且精度高,具有较强的拓展性。     

一种新的三维测量机器人手眼标定方法

为了提高三维测量机器人的手眼标定精度以更好地满足工业需求,提出了一种采用动态改变变异率的自适应差异进化算法进行手眼标定的新方法;首先构建了由线结构光测头和机器人组成的视觉测量系统,利用齐次坐标变换的方法建立了测量系统的数学模型;然后针对数学模型中的手眼关系,使用半径已知的球体,采用定点变位姿的方法,进行粗略的估计,并对手眼关系求解的精度进行分析;最后采用动态改变变异率的自适应差异进化算法对手眼关系做进一步寻优;实验结果表明,算法具有较高的求解精度;该方法用于手眼标定是可行的、有效的,整个标定过程简单,便于实际应用。     

带激光笔的机器人主动视觉手眼标定

以固高GRB-400机器人和摄像机组成手眼系统,在手眼关系旋转矩阵的标定方面,分析了基于主动视觉的标定方法。为实现手眼关系平移向量的标定,提出以固定于机械臂末端的激光笔来获取工件平台上特征点的基坐标,并结合已标定的旋转矩阵来标定平移向量。最后,从图像求取多个特征点之间的距离并与实际值进行误差比较,平面特征点间的长度测量误差在±0.8 mm之间,表明手眼标定精度较高,可满足机器人进行工件定位与自动抓取的要求。     

基于M型标准块的视觉引导机器人系统手眼标定方法研究

针对线结构光传感器引导的机器人系统的手眼标定问题,提出了一种以M型标准块为标定物的方法。该M型标定物的两条平行的脊线作为约束,基于两条平行脊线的约束建立包含手眼关系、机器人运动学以及两条直线位姿参数误差的模型。首先基于定点约束求解手眼关系初值并以此为基础解算出直线位姿参数的初值,然后通过最小二乘法解算误差参数并补偿到模型中,不断迭代直至计算的误差参数小于阈值,最终得到最终的机器人手眼关系及运动学误差参数。为了验证标定方法的有效性,以某精加工平面为被测物,利用线结构光机器人系统对平面进行测量,得到平面点云;拟合最小二乘平面,计算点到平面距离的均方根值作为评价依据。分别对所述M型标准块和标准球两种方法进行了实验对比,结果表明,相较于标准球方法,所述M型标准块方法得到的均方根误差由0.152 mm减少到0.080 mm,均方根误差的标准差由0.043 mm减少到0.005 mm,其标定结果的精度及稳定性得到显著提高。     

基于服务型机器人的机械臂手眼标定的研究与应用

随着科技的进步，服务型机器人也广泛应用于生活的很多场景中，其中，机械臂的应用丰富了服务型机器人的功能。将手眼标定算法应用于机械臂，可以使机械臂完成智能取物等动作，更好地服务于人。提出了一种眼在手上的基于服务型机器人的机械臂手眼标定算法，并对其误差进行了分析，验证发现其可以满足服务型机器人的取物应用。     

机器人理论关节参数的标定方法及软件设计

本文研究了机器人机构理论控制模型中采用的D-H关节参数的标定方法,并编制了相应的计算机应用软件。应用这一方法和软件标定机器人的理论关节参数,对机器人实施误差综合补偿,提高机器人的精度,是特别重要的。对PUMA 562机器人进行了实测与标定。     

A robot hand-eye calibration method of line laser sensor based on 3D reconstruction

In the robotic eye-in-hand measurement system, a hand-eye calibration method is essential. From the perspective of 3D reconstruction, this paper first analyzes the influence of the line laser sensor hand-eye calibration error on the 3D reconstructed point clouds error. Based on this, considering the influence of line laser sensor measurement errors and the need for high efficiency and convenience in robotic manufacturing systems, this paper proposes a 3D reconstruction-based robot line laser hand-eye calibration method. In this method, combined with the point cloud registration technique, the newly defined error-index more intuitively reflects the calibration result than traditional methods. To raise the performance of the calibration algorithm, a Particle Swarm Optimization - Gaussian Process (PSO-GP) method is adopted to improve the efficiency of the calibration. The experiments show that the Root Mean Square Error (RMSE) of the reconstructed point cloud can reach 0.1256 mm when using the proposed method, and the reprojection error is superior to those using traditional hand-eye calibration methods.     

Calibration of 3D kinematic systems using orthogonality constraints

Processing images acquired by multi-camera systems is nowadays an effective and convenient way of performing 3D reconstruction. The basic output, i.e. the 3D location of points, can easily be further processed to also acquire information about additional kinematic data: velocity and acceleration. Hence, many such reconstruction systems are referred to as 3D kinematic systems and are very broadly used, among other tasks, for human motion analysis. A prerequisite for the actual reconstruction of the unknown points is the calibration of the multi-camera system. At present, many popular 3D kinematic systems offer so-called wand calibration, using a rigid bar with attached markers, which is from the end user’s point of view preferred over many traditional methods. During this work a brief criticism on different calibration strategies is given and typical calibration approaches for 3D kinematic systems are explained. In addition, alternative ways of calibration are proposed, especially for the initialization stage. More specifically, the proposed methods rely not only on the enforcement of known distances between markers, but also on the orthogonality of two or three rigidly linked wands. Besides, the proposed ideas utilize common present calibration tools and shorten the typical calibration procedure. The obtained reconstruction accuracy is quite comparable with that obtained by commercial 3D kinematic systems.     

双目视觉测量系统的标定及3维测量

针对结构光形位公差视觉测量系统,提出了一阶径向畸变的摄像机成像模型和标定方法,并对传统的直接线性变换(DLT变换)标定算法做了些改进。即先针对没有畸变的线性模型,利用传统的标定算法,解线性超定方程组求解摄像机的全部参数,再针对引入一阶径向畸变的成像模型,以线性模型的参数为初值,通过非线性迭代优化摄像机的图像中心,等效焦距,倾斜因子,畸变系数等内部参数。实验结果表明,该方法无需预标定,精度适中,是相对简单实用的标定方法。     

微操作机器人系统三维标定的研究与实现

提出三维标定方法 ,提出用这种方法标定的精度主要取决于三维标定块的加工精度和标定过程中对标定块的定位精度 ,给出实现三维标定的关键技术     

Camera Calibration for 3D Color Scanning System Using Virtual 3D Model

Instead of traditionally using a 3D physical model with many control points on it, a calibration plate with printed chess grid and movable along its normal direction is implemented to provide large area 3D control points with variable Z values. Experiments show that the approach presented is effective for reconstructing 3D color objects in computer vision system.     

新的单目视觉系统两步手眼标定方法

为了实现单目视觉系统的快速、精确的手眼标定,本文提出了一种新的两步式手眼标定方法,将手眼标定分为求解旋转关系和平移关系两步.首先机器人携带标定板进行两次平移运动求解旋转关系,然后机器人工具坐标系执行若干次旋转运动求解平移关系.该方法简单快速,不需要昂贵的外部设备,通过实验最终验证了该方法的可行性.     

Online robot calibration based on vision measurement

Robot calibration is a useful diagnostic method to improve positioning accuracy in robot production and maintenance. Unlike traditional calibration methods that require expensive equipment and complex steps, a vision-based online robot calibration method that only requires several reference images is presented in this paper. The method requires a camera that is rigidly attached to the robot end effector (EE), and a calibration board must be settled around the robot where the camera can see it. An efficient automatic approach to detect the corners from the images of the calibration board is proposed. The poses of the robot can be estimated from the detected corners. The kinematic parameters can be conducted automatically based on the known poses of the robot. Unlike in the existing self-calibration methods, the great advantage of this online self-calibration method is that the entire process of robot calibration is automatic and without any manual intervention, enabling the robot calibration to be completed online when the robot is working. Therefore, the proposed approach is particularly suitable for unknown environments, such as deep sea or outer space. In these high-temperature and/or high-pressure environments, the shapes of the robot links are easy to change. Thus, the robot kinematic parameters are changed by allowing the robot to grab objects with different qualities to verify the performance of the online robot calibration. Experimental studies on a GOOGOL GRB3016 robot show that the proposed method has high accuracy, convenience, and high efficiency.     

基于Solis&Wets算法的机器人最优测量构形研究

研究了机器人标定中最优测量构形的选择,采用奇异值分解方法获得了机器人误差传播矩阵的条件数,以该条件数为优化的目标函数,利用Solis&Wets算法来选择机器人的一系列最优测量构形,以最小化参数估计中测量和建模误差的影响。实验结果表明该方法的标定结果优于随机选择的标定构形的标定结果。     

Simultaneous kinematic calibration,localization, and mapping (SKCLAM) for industrial robot manipulators†

Recently, the demand for more accurate, productive, and economical robot manipulators is increasing in the robotics industry. However, a manipulator will produce kinematic errors during production. Thus low-cost kinematic calibration is demanded. Moreover, environmental mapping is also demanded to plan the motions of the manipulator. In this paper, we proposed a simultaneous kinematic calibration, localization, and mapping (SKCLAM) method, which can simultaneously calibrate the kinematic parameters of an industrial robot manipulator using a commercial RGB-D camera attached to its end effector to reconstruct its surroundings. In our method, the kinematic calibration is achieved with feature detection and epipolor geometry. Synthetic and real data experiments were conducted to verify the SKCLAM method. We succeeded in reducing the kinematic errors of the manipulator and reconstructing dense 3D maps of the workspace in the experiments.