一种新的机器人手眼关系标定方法

通过控制装有摄像机的机械手的运动,给出了一种新的机器人手眼系统标定方法．与以往算法的不同之处在于,在计算手眼关系的平移向量时,对摄像机坐标系进行虚设旋转变换使旋转转化为平移问题．该方法需要机械手平台做两次平移运动和一次旋转运动,只需要场景中两个特征点,所以具有方便性和实用性．同时,也给出了基于主动视觉的空间点深度值计算方法．     

动态环境下基于旋转-平移解耦的立体视觉里程计算法

在实际应用中,若图像中的动态特征数量多且运动方向一致,这些特征会对视觉里程计的估计结果产生严重的影响.本文针对这类问题提出一种根据图像特征点位置解耦估计摄像机旋转-平移的立体视觉里程计算法.算法通过立体视觉系统将特征点划分成"远点"和"近点".在随机抽样一致性算法(RANSAC)框架下,采用"远点"估计视觉系统的姿态;进而在姿态已知的条件下,通过"近点"估计摄像机平移,实现旋转-平移解耦计算.这样处理可以通过姿态约束减少近距离运动物体对视觉里程计的影响.实验表明,在实际道路环境中,本文基于旋转-平移解耦估计的算法较之传统的同时估计旋转-平移的算法,能有效剔除动态特征.所提出算法对动态特征的抗干扰能力更好,鲁棒性更强,精度更高.     

旋翼飞行机械臂系统的混合视觉伺服控制

旋翼飞行机械臂是一种具有强耦合特性的机器人系统,借助视觉进行自主作业还存在诸多问题,如实时深度估计、目标极易丢失以及目标笛卡尔空间模型重建等.本文针对传统的基于图像与基于位置的视觉伺服的缺陷以及系统自身欠驱动等问题,建立了运动学模型和提出了基于力平衡原理的动力学联合建模,并通过欧几里得单应性矩阵分解设计出旋翼飞行机械臂系统的混合视觉伺服控制方法,在图像空间控制平移、笛卡尔空间控制旋转,减弱了平移与旋转之间的相互影响实现解耦效果,改善了系统对非结构因素的抗扰性能和全局稳定性.通过仿真和实验检验了系统鲁棒性和算法优越性.     

基于任务函数方法的机器人视觉伺服特性研究

为实现在统一的理论框架下对机器人视觉伺服基础特性进行细致深入的研究,本文基于任务函数方法,建立了广义的视觉伺服系统模型.在此模型基础之上,重点研究了基于位置的视觉伺服(PBVS)与基于图像的视觉伺服(IBVS)方法在笛卡尔空间和图像空间的动态特性.仿真结果表明,在相同的比较框架结构下,PBVS方法同样对摄像机标定误差具有鲁棒性.二者虽然在动态系统的稳定性、收敛性方面相类似,但是在笛卡尔空间和图像空间的动态性能上却有很大的差别.对于PBvS方法,笛卡尔轨迹可以保证最短路径,但是对应的图像轨迹是不可控的,可能会发生逃离视线的问题;对于IBVS方法,图像空间虽然能保证最短路径,但是由于缺乏笛卡尔空间的直接控制,在处理大范围旋转伺服的情况时,会发生诸如摄像机退化的笛卡尔轨迹偏移现象.     

一种机器人手眼关系自标定方法

设计了一种基于场景中单个景物点的机器人手眼关系标定方法．精确控制机械手末端执行器做 5 次以上平移运动和2 次以上旋转运动,摄像机对场景中的单个景物点进行成像．通过景物点的视差及深度 值反映摄像机的运动,建立机械手末端执行器与摄像机两坐标系之间相对位置的约束方程组,线性求得摄像 机内参数及手眼关系．标定过程中只需提取场景中的一个景物点,无需匹配,无需正交运动,对机械手的运 动控制操作方便、算法实现简洁．模拟数据实验与真实图像数据实验结果表明该方法可行、有效．     

场景摄像机构型下图像空间内的无标定路径规划研究

基于图像的视觉伺服可用于对机械臂的运动进行有效的控制。然而,正如许多研究者指出的,当初始位置和期望位置相距较远时,此种控制策略将因其局部特性而存在收敛性、稳定性问题。通过在图像平面内定义充分的图像特征轨迹,并对这些轨迹进行跟踪,我们可以充分利用基于图像的视觉伺服所固有的局部收敛性及稳定性特性这一优势,从而避免初始位置与期望位置相距较远时所面临的问题。因此,近年来,图像空间路径规划已成为机器人领域的一个热点研究问题。但是,目前几乎所有的有关结果均是针对手眼视觉系统提出的。本文将针对场景摄像机视觉系统提出一种未标定视觉路径规划算法。此算法在射影空间中直接计算图像特征的轨迹,这样可保证它们与刚体运动一致。通过将旋转及平移运动的射影表示分解为规范化形式,我们可以很容易地对其射影空间内的路径进行插值。在此之后,图像平面中的图像特征轨迹可通过射影路径产生。通过这种方式,此算法并不需要特征点结构和摄像机内部参数的有关知识。为了验证所提算法的可行性及系统性能,本文最后给出了基于PUMA560机械臂的仿真研究结果。     

带时延补偿的视觉跟踪控制

在实际的视觉伺服系统中, 由于摄像机到图像处理设备的传输延迟和图像处理本身占用的时间, 视觉信息的获取会产生时延. 对此, 给出了一个带有时延补偿的视觉跟踪控制方法. 通过实时拟合图像雅可比矩阵, 实现了对机械手末端执行器图像特征信息的实时预测, 从而减小了估计误差. 在此基础上, 设计了一个带有时延补偿的控制方案. 通过对运动目标进行跟踪的仿真实验, 验证了本文时延补偿方法的有效性.     

架空输电线路巡线机器人越障视觉伺服控制

为解决巡线机器人越障问题,设计了基于图像的越障视觉伺服控制方案．采用傅里叶描述子构造了具有平移、旋转、尺度缩放及起始点不变性的轮廓形状特征向量,实现了驱动轮的识别;进而抽取图像特征和估计驱动轮—相线在图像空间中的相对位姿,并设计了带有死区的比例控制律来实现驱动轮—相线“对中”视觉伺服控制．模拟线路实验结果表明,该方案能可靠地完成巡线机器人驱动轮—相线“对中”控制任务．     

基于图像矩与神经网络的机器人四自由度视觉伺服

针对传统的视觉伺服方法中图像几何特征的标记、提取与匹配过程复杂且通用性差等问题,本文提出了一种基于图像矩的机器人四自由度(4DOF)视觉伺服方法.首先建立了眼在手系统中图像矩与机器人位姿之间的非线性增量变换关系,为利用图像矩进行机器人视觉伺服控制提供了理论基础,然后在未对摄像机与手眼关系进行标定的情况下,利用反向传播(BP)神经网络的非线性映射特性设计了基于图像矩的机器人视觉伺服控制方案,最后用训练好的神经刚络进行了视觉伺服跟踪控制.实验结果表明基于本文算法可实现0.5 mm的位置与0.5°的姿态跟踪精度,验证了算法的的有效性与较好的伺服性能.     

基于主动视觉系统的摄像机自定标方法研究

提出了一种新的基于主动视觉系统的摄像机自定标方法,其主要特点是可以线性求解 摄像机的所有5个内参数.该方法的基本原理是利用图像中平面场景的信息,通过控制摄像机 作多组平面正交平移运动,由平面场景图像的单应性(homography)矩阵建立摄像机内参数线性 约束方程组,来求解摄像机的内参数,同时给出约束方程组解的唯一性与平移运动组之间的关系.     

A new formulation of visual servoing based on cylindrical coordinate system

Image-based visual servoing is a flexible and robust technique to control a robot and guide it to a desired position only by using two-dimensional visual data. However, it is well known that the classical visual servoing based on the Cartesian coordinate system has one crucial problem, that the camera moves backward at infinity, in case that the camera motion from the initial to desired poses is a pure rotation of 1800 around the optical axis. This paper proposes a new formulation of visual servoing, based on a cylindrical coordinate system that can shift the position of the origin. The proposed approach can interpret from a pure rotation around an arbitrary axis to the proper camera rotational motion. It is shown that this formulation contains the classical approach based on the Cartesian coordinate system as an extreme case with the origin located at infinity. Furthermore, we propose a decision method of the origin-shift parameters by estimating a rotational motion from the differences between initial and desired image-plane positions of feature points.     

Image feature command generation of contour following tasks for SCARA robots employing Image-Based Visual Servoing—A PH-spline approach

Recently, visual servoing has been widely employed in industrial robots and has become an invaluable asset to enhance the functionality of the robot. However, the issue of image feature command generation in a visual servoing task receives little attention. In a contour following task that adopts Image-Based Visual Servoing (IBVS), it is crucial to perform motion planning on the desired image trajectory. Without proper motion planning, not only may the discrepancy between the target position and the current position on the image plane not converge, but also the flexibility of exploiting visual servoing for applications such as contour following will be limited. In order to cope with the aforementioned problem, this paper proposes a PH-spline based motion planning approach for systems that adopt IBVS. In particular, the exterior contour of an object is represented by a PH quantic spline. With proper acceleration/deceleration motion planning, a PH quantic spline interpolator is constructed to generate desired image feature commands so that IBVS can be applied to handle contour following problems of an object without a known geometric model. Furthermore, this paper also develops a depth estimation algorithm for the eye-to-hand camera structure, providing a convenient way to estimate the depth value that is essential in computing image Jacobian. Experimental results of several contour following tasks verify the effectiveness of the proposed approach.     

Model Predictive Control for Constrained Image‐Based Visual Servoing in Uncalibrated Environments

This paper presents a novel approach for image‐based visual servoing (IBVS) of a robotic system by considering the constraints in the case when the camera intrinsic and extrinsic parameters are uncalibrated and the position parameters of the features in 3‐D space are unknown. Based on the model predictive control method, the robotic system's input and output constraints, such as visibility constraints and actuators limitations, can be explicitly taken into account. Most of the constrained IBVS controllers use the traditional image Jacobian matrix, the proposed IBVS scheme is developed by using the depth‐independent interaction matrix. The unknown parameters can appear linearly in the prediction model and they can be estimated by the identification algorithm effectively. In addition, the model predictive control determines the optimal control input and updates the estimated parameters together with the prediction model. The proposed approach can simultaneously handle system constraints, unknown camera parameters and depth parameters. Both the visual positioning and tracking tasks can be achieved desired performances. Simulation results based on a 2‐DOF planar robot manipulator for both the eye‐in‐hand and eye‐to‐hand camera configurations are used to demonstrate the effectiveness of the proposed method.     

Uncalibrated Eye-to-Hand Visual Servoing Using Inverse Fuzzy Models

A new uncalibrated eye-to-hand visual servoing based on inverse fuzzy modeling is proposed in this paper. In classical visual servoing, the Jacobian plays a decisive role in the convergence of the controller, as its analytical model depends on the selected image features. This Jacobian must also be inverted online. Fuzzy modeling is applied to obtain an inverse model of the mapping between image feature variations and joint velocities. This approach is independent from the robot's kinematic model or camera calibration and also avoids the necessity of inverting the Jacobian online. An inverse model is identified for the robot workspace, using measurement data of a robotic manipulator. This inverse model is directly used as a controller. The inverse fuzzy control scheme is applied to a robotic manipulator performing visual servoing for random positioning in the robot workspace. The obtained experimental results show the effectiveness of the proposed control scheme. The fuzzy controller can position the robotic manipulator at any point in the workspace with better accuracy than the classic visual servoing approach.     

Global Path-Planning for Constrained and Optimal Visual Servoing

Visual servoing consists of steering a robot from an initial to a desired location by exploiting the information provided by visual sensors. This paper deals with the problem of realizing visual servoing for robot manipulators taking into account constraints such as visibility, workspace (that is obstacle avoidance), and joint constraints, while minimizing a cost function such as spanned image area, trajectory length, and curvature. To solve this problem, a new path-planning scheme is proposed. First, a robust object reconstruction is computed from visual measurements which allows one to obtain feasible image trajectories. Second, the rotation path is parameterized through an extension of the Euler parameters that yields an equivalent expression of the rotation matrix as a quadratic function of unconstrained variables, hence, largely simplifying standard parameterizations which involve transcendental functions. Then, polynomials of arbitrary degree are used to complete the parametrization and formulate the desired constraints and costs as a general optimization problem. The optimal trajectory is followed by tracking the image trajectory with an IBVS controller combined with repulsive potential fields in order to fulfill the constraints in real conditions.     

基于结构光的机器人弧焊混合视觉伺服控制

A novel hybrid visual servoing control method based on structured light vision is proposed for robotic arc welding with a general six degrees of freedom robot. It consists of a position control inner-loop in Cartesian space and two outer-loops. One is position-based visual control in Cartesian space for moving in the direction of weld seam, i.e., weld seam tracking, another is image-based visual control in image space for adjustment to eliminate the errors in the process of tracking. A new Jacobian matrix from image space of the feature point on structured light stripe to Cartesian space is provided for differential movement of the end-effector. The control system model is simplified and its stability is discussed. An experiment of arc welding protected by gas CO_2 for verifying is well conducted.     

Intelligent visual servoing with extreme learning machine and fuzzy logic

While visual servoing (VS) provides the ability of motion using vision for robot manipulators, the approaches for a better VS have to deal with three common problems: obtaining the interaction matrix and its pseudoinverse for defined feature points, finding an appropriate gain value for the VS controller and keeping the features in the field of view (FOV) for VS permanency.In this study, a new intelligent image-based visual servoing (IBVS) system for eye-in-hand configured robot manipulators using extreme learning machine (ELM) and fuzzy logic (FL) is proposed to solve these common problems of VS in a single system. As the first stage of the system, the pseudoinverse of the interaction matrix is approximated using trained ELMs which do not need hidden layer tuning. As the second stage, the classical IBVS controller is modified by a differential equation regarding initial velocity continuity and an appropriate gain in each loop is assigned by an FL unit to provide fast convergence within velocity limits. This unit also promotes manipulability of the manipulator to avoid singularities. As the last stage of the proposed system, regions are defined in the image plane to take precautions before feature missing. When a feature comes close to the edge of a restricted region, an FL unit is activated to obtain negative linear velocities in x and y direction which will be added to the instant velocities to drag the features towards the center of the FOV. In addition to these abilities, some VS metrics are redefined analytically to standardize the performance metric definitions of VS. To show the performance of the proposed system, simulation results of the classical and the proposed IBVS system under practical disturbances are presented for visual servoing of a Puma 560 arm. The advantages of singular matrix and joint configuration avoidance, adaptive gain with smooth gain surface, decreased convergence time within velocity limits, initial velocity continuity, FOV keeping with smooth velocity assurance, redefined VS metrics for standardization and robustness against disturbances are proved by variety of simulations. The simulation results also verify that the proposed system utilizing intelligent methods like ELM and FL is capable of dealing with common problems of VS and achieves sufficient results in terms of VS metrics.