逼近与跟踪空间翻滚目标的图像视觉伺服控制

在空间翻滚目标的最终逼近与跟踪任务段,追踪星对目标星的视觉相对导航会因进入盲区而失效,而基于间接相对导航的位置视觉伺服控制精度太差.针对该问题,基于图像视觉伺服思想,直接利用目标图像特征点坐标,建立了超近距离姿轨跟踪的成像运动模型,推导得到指数收敛的图像视觉伺服期望广义速度、相对位置估计修正量,进而引入基于双滑模面控制律的相对姿轨耦合控制闭环,从而提出了间接估计辅助的图像视觉伺服控制方法.数学仿真结果表明:该方法的相对位置和姿态控制精度显著提高,可为空间翻滚目标最终逼近与跟踪任务提供安全、准确、稳定的相对姿态和轨道条件.     

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.     

Selection of features and evaluation of visual measurements during robotic visual servoing tasks

This paper presents some of the computer vision techniques that were employed in order to automatically select features, measure features' displacements, and evaluate measurements during robotic visual servoing tasks. We experimented with a lot of different techniques, but the most robust proved to be the Sum-of-Squared Differences (SSD) optical flow technique. In addition, several techniques for the evaluation of the measurements are presented. One important characteristic of these techniques is that they can also be used for the selection of features for tracking in conjunction with several numerical criteria that guarantee the robustness of the servoing. These techniques are important aspects of our work since they can be used either on-line or off-line. An extension of the SSD measure to color images is presented and the results from the application of these techniques to real images are discussed. Finally, the derivation of depth maps through the controlled motion of the handeye system is outlined and the important role of the automatic feature selection algorithm in the accurate computation of the depth-related parameters is highlighted.The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the funding agencies. Tel. (612) 625-0163; e-mail address: npapas@cs.umn.edu.     

基于自适应免疫整定的机器人无标定自抗扰视觉伺服控制

研究了机器人无标定自抗扰视觉伺服控制问题. 针对系统中所用的自抗扰控制器参数选取困难问题, 提出了基于自适应免疫算法的自抗扰控制器参数整定方法. 证明了系统中所用的非线性离散二阶扩张状态观测器稳定的充要条件, 并将该条件应用在参数整定过程中. 6 自由度工业机器人的实验结果表明了该方法的可行性和有效性.     

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 based visual servo control for a class of aerial robotic systems

An image-based strategy for visual servo control of a class of dynamic systems is proposed. The class of systems considered includes dynamic models of unmanned aerial vehicles capable of quasi-stationary flight (hover and near hover flight). The control strategy exploits passivity-like properties of the dynamic model to derive a Lyapunov control algorithm using backstepping techniques. The paper extends earlier work (Hamel, T., & Mahony, R. (2002). Visual servoing of an under-actuated dynamic rigid-body system: An image based approach. IEEE Transactions on Robotics and Automation, 18(2), 187-198) where partial pose information was used in the construction of the visual error. In this paper the visual error is defined purely in terms of the image features derived from the camera input. Local exponential stability of the system is proved. An estimate of the basin of attraction for the closed-loop system is provided.     

基于视觉的服务机器人轨迹跟踪控制

根据移动服务机器人的运动学模型和针孔摄像机成像原理,完成了机器人的速度向量由视觉空间到任务空间的变换。采用视觉伺服控制方式,结合反步设计思想,设计了具有全局渐近稳定的轨迹跟踪控制器,并利用Lyapunov函数进行稳定性分析。仿真结果验证了所设计控制器的有效性和正确性。     

Dynamic visual servo control of a 4-axis joint tool to track image trajectories during machining complex shapes

A large part of the new generation of computer numerical control systems has adopted an architecture based on robotic systems. This architecture improves the implementation of many manufacturing processes in terms of flexibility, efficiency, accuracy and velocity. This paper presents a 4-axis robot tool based on a joint structure whose primary use is to perform complex machining shapes in some non-contact processes. A new dynamic visual controller is proposed in order to control the 4-axis joint structure, where image information is used in the control loop to guide the robot tool in the machining task. In addition, this controller eliminates the chaotic joint behavior which appears during tracking of the quasi-repetitive trajectories required in machining processes. Moreover, this robot tool can be coupled to a manipulator robot in order to form a multi-robot platform for complex manufacturing tasks. Therefore, the robot tool could perform a machining task using a piece grasped from the workspace by a manipulator robot. This manipulator robot could be guided by using visual information given by the robot tool, thereby obtaining an intelligent multi-robot platform controlled by only one camera.     

Detection and tracking for robotic visual servoing systems

Robot manipulators require knowledge about their environment in order to perform their desired actions. In several robotic tasks, vision sensors play a critical role by providing the necessary quantity and quality of information regarding the robot's environment. For example, “visual servoing” algorithms may control a robot manipulator in order to track moving objects that are being imaged by a camera. Current visual servoing systems often lack the ability to detect automatically objects that appear within the camera's field of view. In this research, we present a robust “figureiground” framework for visually detecting objects of interest. An important contribution of this research is a collection of optimization schemes that allow the detection framework to operate within the real-time limits of visual servoing systems. The most significant of these schemes involves the use of “spontaneous” and “continuous” domains. The number and location of continuous domains are. allowed to change over time, adjusting to the dynamic conditions of the detection process. We have developed actual servoing systems in order to test the framework's feasibility and to demonstrate its usefulness for visually controlling a robot manipulator.     

焊接机器人曲线焊缝的自动跟踪控制

为实现焊接机器人对曲线焊缝的自动跟踪,提出一种简便的位姿实时调整策略和协调视觉跟踪与机器人运动的视觉伺服控制方法。建立了曲线焊缝视觉跟踪过程中焊接机器人期望位姿的数学模型;设计了一种上下层结构的模糊视觉伺服控制器,通过建立焊缝特征点像素坐标偏差与末端轴旋转角度之间的关系模型,动态确定模糊论域的大小,在机器人期望位姿的基础上仅仅通过调整末端轴的旋转量来保证图像特征点始终存在于相机视场内。通过模拟焊接机器人自动跟踪曲线焊缝的实验,验证了所提策略与方法的有效性。     

垂直起降无人机基于图像的目标跟踪控制

针对安装有惯性测量单元和摄像机的低成本四旋翼无人机,研究无位置、速度、航向测量情况下的机动目标基于图像的跟踪控制方法.首先,结合无人机的动力学方程在图像空间中推导了系统的误差方程.其次,为克服无航向测量的问题,设计了一种位置控制器,使用图像矩作为反馈输入并输出油门和姿态指令.最后,针对缺少图像速度测量问题,设计了一种super-twisting滑模观测器和控制器,生成的期望姿态和拉力指令无颤振,并通过李雅普诺夫理论证明了控制系统的稳定性.最终无人机通过调整倾斜姿态实现了跟踪飞行,且避免了响应慢的航向调整.跟踪机动目标的仿真结果验证了所提出方法的有效性.     

基于视觉SLAM–伺服框架的移动机器人指令滤波反步控制

针对移动机器人位姿镇定问题, 本文提出基于视觉同时定位与建图(simultaneous localization and mapping, SLAM)–伺服框架的指令滤波反步控制策略. 具体而言, 通过加速度层控制器设计进而积分得到的光滑速度信号, 减小SLAM视觉模块的预测位姿误差; 继而应用指令滤波器简化控制器设计的复杂求导运算, 减轻计算负担; 此外, SLAM模块利用运动信息与视觉信息的融合解决未知尺度问题, 降低未知深度造成的控制器设计复杂度. 通过李雅普诺夫理论可以证明闭环系统的稳定性. 仿真和实验结果最终验证了本文算法的有效性.     

The use of active deformable models in model-based robotic visual servoing

This paper presents a new approach for visual tracking and servoing in robotics. We introduce deformable active models as a powerful means for tracking a rigid or semi-rigid (possibly partially occluded) object in movement within the manipulator's workspace. Deformable models imitate, in real-time, the dynamic behavior of elastic structures. These computer-generated models are designed to capture the silhouette of objects with well-defined boundaries, in terms of image gradient. By means of an eye-in-hand robot arm configuration, the desired motion of the end-effector is computed with the objective of keeping the target's position and shape invariant with respect to the camera frame. Optimal estimation and control techniques (LQG regulator) have been successfully implemented in order to deal with noisy measurements provided by our vision sensor. Experimental results are presented for the tracking of a rigid or semi-rigid object. The experiments performed in a real-time environment show the effectiveness and robustness of the proposed method for servoing tasks based on visual feedback.     

直线特征的交互矩阵求取

直线特征在视觉跟踪、视觉伺服中具有重要作用, 但目前的直线交互矩阵的求取受到制约, 需要已知含有直线的平面在摄像机坐标系中的方程参数. 为摆脱含有直线的平面参数的约束, 本文利用两点的极坐标推导出直线的交互矩阵, 并给出直线交互矩阵求取方法. 经分析得知, 对于与摄像机光轴接近垂直的直线, 其在成像平面上的角度变化主要受摄像机姿态变化的影响, 对摄像机的位置变化不敏感. 对于与摄像机光轴平行的直线, 其在成像平面上的角度变化受摄像机旋转以及垂直于光轴平移 的影响较大. 实验结果验证了本文方法的有效性.     

机器人视觉伺服中CMAC学习控制系统研究

根据小脑模型关联控制器（CMAC）收敛速度快,适于实时控制系统的特点,设计了一种基于CMAC学习控制方法的机器人视觉伺服系统。在该系统中,CMAC被用作前馈视觉控制器对常规反馈控制器进行补偿。所提出的CMAC控制器替代图像雅可比矩阵来获得目标图像特征和机器人关节运动之间2D/3D变换关系,通过其在线学习,可以使系统对摄像机标定误差不敏感,从而提高系统的鲁棒性。实验证明了所设计控制系统的有效性。     

可视化背景提取和粒子滤波的行为检测研究

在智能视频监控中,目标所在场景的复杂性和光照变化使得运动目标的检测难度加大.采用可视化背景提取(ViBe)算法来检测运动目标,对于运动目标在运动过程中的交叉、遮挡等情况,采用ViBe算法和粒子滤波运动目标跟踪算法相结合,完成对运动目标的检测与跟踪;在运动行为检测中,进一步定义目标的运动方向,从而判断目标的运动状态.实验表明,ViBe算法能够准确检测复杂场景中的运动目标,并能够适应噪声干扰和光照变化,将其和粒子滤波结合能够准确跟踪实际场景中的运动目标,并能够准确判断目标的运动状态.     

具有深度自适应估计的视觉伺服优化

在手眼机器人视觉伺服中,如何确定机器人末端摄像机移动的速度和对物体的深度进行有效的估计还没有较好的解决方法.本文采用一般模型法,通过求解最优化控制问题来设计摄像机的速度,同时,利用物体初始及期望位置的深度估计值,提出了一种自适应估计的算法对物体的深度进行估计,给出了深度变化趋势,实现了基于图像的定位控制.该方法能够使机器人在工作空间范围内从任一初始位置出发到达期望位置,实现了系统的全局渐近稳定且不需要物体的几何模型及深度的精确值.最后给出的仿真实例表明了本方法的有效性.     

Visual Object Tracking and Servoing Control of a Nano-Scale Quadrotor: System,Algorithms, and Experiments

There are two main trends in the development of unmanned aerial vehicle(UAV)technologies:miniaturization and intellectualization,in which realizing object tracking capabilities for a nano-scale UAV is one of the most challenging problems.In this paper,we present a visual object tracking and servoing control system utilizing a tailor-made 38 g nano-scale quadrotor.A lightweight visual module is integrated to enable object tracking capabilities,and a micro positioning deck is mounted to provide accurate pose estimation.In order to be robust against object appearance variations,a novel object tracking algorithm,denoted by RMCTer,is proposed,which integrates a powerful short-term tracking module and an efficient long-term processing module.In particular,the long-term processing module can provide additional object information and modify the short-term tracking model in a timely manner.Furthermore,a positionbased visual servoing control method is proposed for the quadrotor,where an adaptive tracking controller is designed by leveraging backstepping and adaptive techniques.Stable and accurate object tracking is achieved even under disturbances.Experimental results are presented to demonstrate the high accuracy and stability of the whole tracking system.     

Simultaneous tracking and regulation visual servoing of wheeled mobile robots with uncalibrated extrinsic parameters

This paper presentsa global adaptive controller that simultaneously solves tracking and regulation for wheeled mobile robots with unknown depth and uncalibrated camera-to-robot extrinsic parameters. The rotational angle and the scaled translation between the current camera frame and the reference camera frame, as well as the ones between the desired camera frame and the reference camera frame can be calculated in real time by using the pose estimation techniques. A transformed system is first obtained, for which an adaptive controller is then designed to accomplish both tracking and regulation tasks, and the controller synthesis is based on Lyapunov's direct method. Finally, the effectiveness of the proposed method is illustrated by a simulation study.     

荷载不确定移动机器人视觉伺服系统鲁棒预测控制

考虑具有可见性约束和执行器约束的载荷不确定移动机器人视觉伺服系统,提出一种鲁棒视觉伺服预测控制策略.首先将该移动机器人视觉伺服系统建模为关于视觉伺服误差和驱动的不确定系统.其次,对约束的视觉伺服误差子系统,设计基于半正定规划的速度规划预测控制算法.该算法分为离线计算和在线调度两个部分,降低预测控制算法的在线计算量.而对...