基于图像雅可比矩阵的智能机器人视觉跟踪

根据智能机器人视觉伺服的要求,建立了固定眼结构的MOTOMAN-SV3XL型工业机器人无标定视觉伺服系统.研究了目前在机器人视觉伺服研究领域中使用最为广泛的一类方法,即基于图像雅可比矩阵的方法.针对图像雅可比矩阵的特点,设计了一种基于简化Sage-Husa自适应滤波的图像雅可比矩阵在线估计算法,并将其应用于机器人视觉反馈控制任务中,实现了对二维平面上运动目标的跟踪,实验结果表明了该算法的有效性.     

Visual servoing tracking control of uncalibrated manipulators with a moving feature point

The paper is concerned with the problem of uncalibrated visual servoing robots tracking a dynamic feature point along with the desired trajectory. A nonlinear observer and a nonlinear controller are proposed, which allow the considered uncalibrated visual servoing robotic system to fulfil the desired tracking task. Based on this novel control method, a dynamic feature point with unknown motion parameters can be tracked effectively along with the desired trajectory, even with multiple uncertainties existing in the camera, the kinematics and the manipulator dynamics. By the Lyapunov theory, asymptotic convergence of the image errors to zero with the proposed control scheme is rigorously proven. Simulations have been conducted to verify the performance of the proposed control scheme. The results demonstrated good convergence of the image errors.     

GA-pattern matching-based manipulator control system for real-time visual servoing

_—In robotic applications, tasks of picking and placing are the most fundamental ones. Also, for a robot manipulator, the recognition of its working environment is one of the most important issues to do intelligent tasks, since this aptitude enables it to work in a variable environment. This paper presents a new control strategy for robot manipulators, which utilizes visual information to direct the manipulator in its working space, to pick up an object of known shape, but with arbitrary position and orientation. During the search for an object to be picked up, vision-based control by closed-loop feedback, referred to as visual servoing, is performed to obtain the motion control of the manipulator hand. The system employs a genetic algorithm (GA) and a pattern matching technique to explore the search space and exploit the best solutions by this search technique. The control strategy utilizes the found results of GA-pattern matching in every step of GA evolution to direct the manipulator towards the target object. We named this control strategy step-GA-evnlution. This control method can be applied for manipulator real-time visual servoing and solve its path planning problem in real-time, i.e. in order for the manipulator to adapt the execution of the task by visual information during the process execution. Simulations have been performed, using a two-link planar manipulator and three image models, in order to find which one is the best for real-time visual servoing and the results show the effectiveness of the control method.     

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.     

Visual Servoing Path Planning for Cameras Obeying the Unified Model

Abstract

This paper proposes a path planning visual servoing strategy for a class of cameras that includes conventional perspective cameras, fisheye cameras and catadioptric cameras as special cases. Specifically, these cameras are modeled by adopting a unified model recently proposed in the literature and the strategy consists of designing image trajectories for eye-in-hand robotic systems that allow the robot to reach a desired location while satisfying typical visual servoing constraints. To this end, the proposed strategy introduces the projection of the available image features onto a virtual plane and the computation of a feasible image trajectory through polynomial programming. Then, the computed image trajectory is tracked by using an image-based visual servoing controller. Experimental results with a fisheye camera mounted on a 6-d.o.f. robot arm are presented in order to illustrate the proposed strategy.     

Image-based visual servoing using improved image moments in 6-DOF robot systems

This paper addresses the challenges of choosing proper image features for planar symmetric shape objects and designing visual servoing controller to enhance the tracking performance in image-based visual servoing (IBVS). Six image moments are chosen as the image features and the analytical image interaction matrix related to the image features are derived. A controller is designed to efficiently increase the robustness of the visual servoing system. Experimental results on a 6-DOF robot visual servoing system are provided to illustrate the effectiveness of the proposed method.     

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.     

利用平面单应分解实现服务机器人视觉伺服

智能空间中家庭服务机器人所需完成的主要任务是协助人完成物品的搜寻、定位与传递。而视觉伺服则是完成上述任务的有效手段。搭建了由移动机器人、机械臂、摄像头组成的家庭服务机器人视觉伺服系统,建立了此系统的运动学模型并对安装在机械臂末端执行器上的视觉系统进行了内外参数标定,通过分解世界平面的单应来获取目标物品的位姿参数,利用所获取的位姿参数设计了基于位置的视觉伺服控制律。实验结果表明,使用平面单应分解方法来设计控制律可简单有效地完成家庭物品的视觉伺服任务。     

Visual Homing: Surfing on the Epipoles

We introduce a novel method for visual homing. Using this method a robot can be sent to desired positions and orientations in 3D space specified by single images taken from these positions. Our method is based on recovering the epipolar geometry relating the current image taken by the robot and the target image. Using the epipolar geometry, most of the parameters which specify the differences in position and orientation of the camera between the two images are recovered. However, since not all of the parameters can be recovered from two images, we have developed specific methods to bypass these missing parameters and resolve the ambiguities that exist. We present two homing algorithms for two standard projection models, weak and full perspective.Our method determines the path of the robot on-line, the starting position of the robot is relatively not constrained, and a 3D model of the environment is not required. The method is almost entirely memoryless, in the sense that at every step the path to the target position is determined independently of the previous path taken by the robot. Because of this property the robot may be able, while moving toward the target, to perform auxiliary tasks or to avoid obstacles, without this impairing its ability to eventually reach the target position. We have performed simulations and real experiments which demonstrate the robustness of the method and that the algorithms always converge to the target pose.     

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。     

曲线焊缝跟踪的视觉伺服协调控制

为实现工业机器人自动跟踪曲线焊缝,提出了协调焊枪运动和视觉跟踪的视觉伺服控制方法．建立了特征点的数学模型,并在此基础上确定机器人运动的旋转轴．设计了一种双层结构的模糊视觉伺服控制器,通过动态确定控制量有效范围来保证图像特征存在于视场中．为准确确定有效范围,设计了带模型动态补偿的Kalman滤波器．曲线焊缝的自动跟踪实验验证了所提方法的有效性．     

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.     

Finite-time tracking control for multiple non-holonomic mobile robots based on visual servoing

This paper investigates finite-time tracking control problem of multiple non-holonomic mobile robots via visual servoing. It is assumed that the pinhole camera is fixed to the ceiling, and camera parameters are unknown. The desired reference trajectory is represented by a virtual leader whose states are available to only a subset of the followers, and the followers have only interaction. First, the camera-objective visual kinematic model is introduced by utilising the pinhole camera model for each mobile robot. Second, a unified tracking error system between camera-objective visual servoing model and desired reference trajectory is introduced. Third, based on the neighbour rule and by using finite-time control method, continuous distributed cooperative finite-time tracking control laws are designed for each mobile robot with unknown camera parameters, where the communication topology among the multiple mobile robots is assumed to be a directed graph. Rigorous proof shows that the group of mobile robots converges to the desired reference trajectory in finite time. Simulation example illustrates the effectiveness of our method.     

基于Adept机器人的视觉伺服控制系统

机器人视觉伺服控制在理论和应用等方面还有许多问题需要研究,例如特征选择、系统标定和伺服控制算法等.针对Adept机器人,提出了一种简单快速的不需要精确标定摄像机内外部参数的摄像机标定方法,完成了从被观测物体表面所在的视觉平面坐标系到机器人基坐标系的坐标变换.使用图像的全局特征,即图像矩特征进行伺服跟踪;利用所推导的图像雅可比矩阵,设计了由图像反馈与目标运动自适应补偿组成的视觉伺服控制器.将算法对静态目标的定位实验进行了验证,然后又将其应用到移动目标的跟踪上,通过调节和优选控制参数,实现了稳定的伺服跟踪和抓取.实验结果表明采用图像矩作为图像特征能够避免复杂的特征匹配过程,并且能够获得较好的跟踪精度.     

Photometric visual servoing for omnidirectional cameras

2D visual servoing consists in using data provided by a vision sensor for controlling the motions of a dynamic system. Most of visual servoing approaches has relied on the geometric features that have to be tracked and matched in the image acquired by the camera. Recent works have highlighted the interest of taking into account the photometric information of the entire image. This approach was tackled with images of perspective cameras. We propose, in this paper, to extend this technique to central cameras. This generalization allows to apply this kind of method to catadioptric cameras and wide field of view cameras. Several experiments have been successfully done with a fisheye camera in order to control a 6 degrees of freedom robot and with a catadioptric camera for a mobile robot navigation task.     

一种基于图像的移动机器人泊位方法

提出一种新的移动机器人泊位方法. 该方法采用一幅预先采集的参考图像定义机器人的期望泊位状 态（期望的位置和方向）,利用尺度不变特征变换（SIFT）算法和基于双向BBF 的特征匹配算法实现泊位站当前图 像与参考图像之间的匹配以获取视觉反馈信息,基于极点伺服策略根据参考图像准直机器人,采用质心跟踪法防止 目标图像出视场,采用RANSAC 算法求解当前图像与参考图像间的仿射变换,利用一个末段控制策略实现精确泊 位．本文方法不需要环境模型或人工标记．室内环境下的实验结果证实了该方法的有效性．     

Telerobotic visual servoing

This paper addresses the problem of integrating the human operator with autonomous robotic visual tracking and servoing modules. A CCD camera is mounted on the end-effector of a robot and the task is to servo around a static or moving rigid target. In manual control mode, the human operator, with the help of a joystick and a monitor, commands robot motions in order to compensate for tracking errors. In shared control mode, the human operator and the autonomous visual tracking modules command motion along orthogonal sets of degrees of freedom. In autonomous control mode, the autonomous visual tracking modules are in full control of the servoing functions. Finally, in traded control mode, the control can be transferred from the autonomous visual modules to the human operator and vice versa. This paper presents an experimental setup where all these different schemes have been tested. Experimental results of all modes of operation are presented and the related issues are discussed. In certain degrees of freedom (DOF) the autonomous modules perform better than the human operator. On the other hand, the human operator can compensate fast for failures in tracking while the autonomous modules fail. Their failure is due to difficulties in encoding an efficient contingency plan.     

Instance invariant visual servoing framework for part‐aware autonomous vehicle inspection using MAVs

Visual servoing approaches navigate a robot to the desired pose with respect to a given object using image measurements. As a result, these approaches have several applications in manipulation, navigation and inspection. However, existing visual servoing approaches are instance specific, that is, they control camera motion between two views of the same object. In this paper, we present a framework for visual servoing to a novel object instance. We further employ our framework for the autonomous inspection of vehicles using Micro Aerial Vehicles (MAVs), which is vital for day‐to‐day maintenance, damage assessment, and merchandising a vehicle. This visual inspection task comprises the MAV visiting the essential parts of the vehicle, for example, wheels, lights, and so forth, to get a closer look at the damages incurred. Existing methods for autonomous inspection could not be extended for vehicles due to the following reasons: First, several existing methods require a 3D model of the structure, which is not available for every vehicle. Second, existing methods require expensive depth sensor for localization and path planning. Third, current approaches do not account for the semantic understanding of the vehicle, which is essential for identifying parts. Our instance invariant visual servoing framework is capable of autonomously navigating to every essential part of a vehicle for inspection and can be initialized from any random pose. To the best our knowledge, this is the first approach demonstrating fully autonomous visual inspection of vehicles using MAVs. We have validated the efficacy of our approach through a series of experiments in simulation and outdoor scenarios.