小型无人机地面目标跟踪系统机载云台自适应跟踪控制

本文针对小型无人机地面目标跟踪系统,提出了一种机载云台自适应跟踪控制算法.该算法在摄像机外参数未知的情况下,利用图像信息和机载传感器得到的无人机状态进行反馈,最终实现了对云台摄像机姿态的控制,使得无人机在跟踪目标飞行过程中,地面目标可以始终保持在摄像机的图像中心.为此,论文首先通过分析无人机、目标和摄像机三者的相对位姿关系,建立了目标点在图像平面的运动学模型.在此基础上,基于李雅普诺夫稳定性理论设计了自适应控制算法.理论分析与仿真结果表明本文所设计的摄像机姿态控制器在摄像机外参数未知的情况下,可以使被跟踪目标始终保持在图像中心.     

基于单应性矩阵的移动机器人视觉伺服切换控制

针对具有单目视觉的移动机器人,本文提出了一种基于单应性矩阵的视觉伺服控制算法,在缺乏深度信息的情况下利用视觉反馈实现了移动机器人的控制目标,即给定机器人目标位姿下拍摄得到的图像,通过视觉伺服使机器人从初始位姿准确到达目标位姿.视觉反馈环节采用单应性矩阵中的元素构造状态变量,而非利用常见的单应性分解,此外,考虑到视野约束,本文提出的算法在计算单应性矩阵时结合了单应性的传递特性,从而避免了参考目标的实时可见性.伺服环节设计了切换控制器,在满足非完整约束的同时可驱动机器人到达期望位姿.理论分析及实物仿真验证了该算法的可行性和有效性.     

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.     

轮式移动机器人单目视觉的目标测距方法

为减小特征匹配以及摄像机光轴倾斜对单目测距造成的误差,提出一种基于单目视觉的轮式移动机器人目标测距方法,将测距目标从平面物体推广到立体物体,在无须校正的情况下提高测量精度。通过对摄像机进行标定测量其内外参数,建立小孔平面成像模型得到世界坐标系与像素坐标系的对应关系。针对坐标变换矩阵奇异的情况,引入面积这一特性进行求解,推导特定矩阵奇异情况下目标距离与像素面积的关系。实验结果表明,该方法可将综合误差率控制在0.7%以内,能够满足轮式移动机器人单目视觉测距实时性和可靠性的要求。     

基于单目视频路径移动机器人导航方法研究

以单目视觉中双视图定位为基础,提出了一种基于单目视频路径的导航方法.算法基于单目摄像机提取一组有序的关键图像,以代表视频路径,使用Harris角点算法提取图像特征点,利用零均值归一化互相关算法进行图像匹配,根据单目摄像机双视图匹配点之间的约束关系求出基础矩阵并优化,得到位姿参数,完成了移动机器人定位,使其在视频路径的指导下实现自主导航.验证表明:方法在室外环境中具有较好的实用性.     

基于2D三焦点张量的移动机器人视觉伺服镇定控制

针对单目视觉移动机器人系统, 本文提出了一种基于二维三焦点张量(2D trifocal tensor, 2DTT)的视觉伺服镇定控制方法. 具体而言, 首先描述了2D三焦点张量的导出过程, 并给出了基于图像特征点的估计方法. 在此基础上根据2D三焦点张量的元素, 设计了一种反馈线性化控制器以实现机器人的位置镇定, 以及一种比例控制器来实现姿态镇定, 因而在场景信息与平移信息均未知情况下完成了移动机器人的视觉镇定控制. 通过理论分析证明了本文设计的镇定控制算法具有指数收敛性能. 相比现有方法, 这种基于2D 三焦点张量的方法在图像特征识别方面具有更强的鲁棒性, 并且在平面场景与立体场景情况下均适用. 最后利用仿真与实验结果验证了本文提出的视觉伺服方法的优良性能.     

未校正摄像机的非完整机器人的自适应镇定

针对基于未校准顶置摄像机的非完整移动机器人视觉伺服镇定问题,首先从标准机器人运动学模型以及视觉空间与工作空间的转换得到视觉平面上的机器人运动学模型,而后根据视觉空间的运动学的速度误差以及视觉空间的机器人的动力学模型设计了一个自适应控制器,而且控制器具有鲁棒性,控制器中的鲁棒项函数用以抑制动力学的扰动,摄像机估计值用以估计未知的摄像机参数,动力学的惯性参数估计值用以消除动力学参数的不确定性.控制系统的稳定性以及参数估计值的有界性由李雅普诺夫定理证明.仿真结果用于说明控制律的有效性.     

不确定非完整移动机器人的轨迹跟踪控制

具有未校准视觉参数的非完整移动机器人的运动学系统具有参数不确定性,较一般的运动学系统更加复杂.基于视觉反馈、Barbalat's定理和Lyapunov直接方法,研究了具有未标定摄像机参数的非完整移动机器人的轨迹跟踪问题.首先,利用固定在天花板上的针孔摄像机透视投影模型,提出了一种新的基于视觉伺服的移动机器人运动学跟踪误差模型;基于这个模型,提出了一种新的与未知视觉参数无关的动态反馈跟踪控制器.该控制器不仅保证系统的状态渐近跟踪给定参考轨迹,而且控制器是全局的,通过Lyapunov方法严格证明了闭环系统的稳定性.在惯性系和图像坐标系下讨论跟踪问题,使问题变的简单且设计的控制器更加有用.最后,仿真结果证实了所提出的控制器的有效性.     

一类不确定非完整动力学系统的时变镇定

对于一类具有未知惯性参数的非完整动力学系统,提出了新的时变自适应镇定律、 将其用于一类移动机器人的位姿镇定中.仿真结果验证了所提控制方法的有效性.     

基于平面单应性的单目视觉里程计设计

针对如何准确获取位姿信息来实现移动机器人的避障问题,提出一种可用于实时获取移动机器人位姿的单目视觉里程计算法。该算法利用单目摄像机获取连续帧间图像路面SURF(Speeded Up Robust Features)特征点;并结合极线几何约束来解决路面特征点匹配较难的问题,通过计算平面单应性矩阵获取移动机器人的位姿变化。实验结果表明该算法具有较高的精度和实时性。     

Homography-based visual servo regulation of mobile robots.

A monocular camera-based vision system attached to a mobile robot (i.e., the camera-in-hand configuration) is considered in this paper. By comparing corresponding target points of an object from two different camera images, geometric relationships are exploited to derive a transformation that relates the actual position and orientation of the mobile robot to a reference position and orientation. This transformation is used to synthesize a rotation and translation error system from the current position and orientation to the fixed reference position and orientation. Lyapunov-based techniques are used to construct an adaptive estimate to compensate for a constant, unmeasurable depth parameter, and to prove asymptotic regulation of the mobile robot. The contribution of this paper is that Lyapunov techniques are exploited to craft an adaptive controller that enables mobile robot position and orientation regulation despite the lack of an object model and the lack of depth information. Experimental results are provided to illustrate the performance of the controller.     

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.     

Dynamic Visual Tracking for Manipulators Using an Uncalibrated Fixed Camera

This paper presents a new controller for controlling a number of feature points on a robot manipulator to trace desired trajectories specified on the image plane of a fixed camera. It is assumed that the intrinsic and extrinsic parameters of the camera are not calibrated. A new adaptive algorithm is developed to estimate the unknown parameters online, based on three original ideas. First, we use the pseudoinverse of the depth-independent interaction matrix to map the image errors onto the joint space of the manipulator. By eliminating the depths in the interaction matrix, we can linearly parameterize the closed-loop dynamics of the manipulator. Second, to guarantee the existence of the pseudoinverse, the adaptive algorithm introduces a potential force to drive the estimated parameters away from the values that result in a singular Jacobian matrix. Third, to ensure that the estimated parameters are convergent to their true values up to a scale, we combine the Slotine-Li method with an online algorithm for minimizing the error between the estimated projections and real image coordinates of the feature points. We have proved asymptotic convergence of the image errors to zero by the Lyapunov theory based on the nonlinear robot dynamics. Experiments have been carried out to verify the performance of the proposed controller.     

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.     

Homography-based visual servo tracking control of a wheeled mobile robot

A visual servo tracking controller is developed in this paper for a monocular camera system mounted on an underactuated wheeled mobile robot (WMR) subject to nonholonomic motion constraints (i.e., the camera-in-hand problem). A prerecorded image sequence (e.g., a video) of three target points is used to define a desired trajectory for the WMR. By comparing the target points from a stationary reference image with the corresponding target points in the live image and the prerecorded sequence of images, projective geometric relationships are exploited to construct Euclidean homographies. The information obtained by decomposing the Euclidean homography is used to develop a kinematic controller. A Lyapunov-based analysis is used to develop an adaptive update law to actively compensate for the lack of depth information required for the translation error system. Experimental results are provided to demonstrate the control design.     

Uncalibrated visual servoing of robots using a depth-independent interaction matrix

This paper presents a new adaptive controller for image-based dynamic control of a robot manipulator using a fixed camera whose intrinsic and extrinsic parameters are not known. To map the visual signals onto the joints of the robot manipulator, this paper proposes a depth-independent interaction matrix, which differs from the traditional interaction matrix in that it does not depend on the depths of the feature points. Using the depth-independent interaction matrix makes the unknown camera parameters appear linearly in the closed-loop dynamics so that a new algorithm is developed to estimate their values on-line. This adaptive algorithm combines the Slotine-Li method with on-line minimization of the errors between the real and estimated projections of the feature points on the image plane. Based on the nonlinear robot dynamics, we prove asymptotic convergence of the image errors to zero by the Lyapunov theory. Experiments have been conducted to verify the performance of the proposed controller. The results demonstrated good convergence of the image errors.     

Visual tracking of a moving target using active contour based SSD algorithm

This paper presents a new image based visual tracking scheme for a mobile robot to trace a moving target using a single camera mounted on the mobile robot. To accurately estimate the position of the target in the next image, it decomposes the effect of the camera motion on the velocity vector of the target in the image frame. Based on the estimated velocity of the target and the image Jacobian, the control inputs of the mobile robot are determined in such a way that the target may appear inside the central area of the image frame. Since the shape of the target in the image frame varies due to rotation and translation of the target, a new shape adaptive Sum-of-Squared Difference (SSD) algorithm is proposed which uses the extended snake algorithm to extract the contour of the target and updates the template in every step of the matching process. The proposed scheme has been implemented using a Nomad Scout Robot II. The experimental results have shown that the proposed scheme follows the target within a negligible error range even when the target is temporarily lost due to various reasons.     

基于神经网络的不确定移动机器人鲁棒自适应跟踪控制

针对含运动学未知参数以及动力学模型不确定的非完整轮式移动机器人轨迹跟踪问题,基于Radical Basis Function（径向基函数）神经网络,提出了一种鲁棒自适应控制器.首先,考虑移动机器人运动学参数未知的情况,提出了一种含自适应参数的运动学控制器,用以补偿参数不确定性导致的系统误差;其次,利用神经网络控制技术,对于机器人在移动中动力学模型不确定问题,提出了一种具有鲁棒性的动力学控制器,使得移动机器人可以在不知道具体动力学模型的情况下跟踪到目标轨迹;最后利用Lyapunov稳定性理论证明了整个系统的稳定性.通过数值仿真验证了所设计的控制器的可行性.     

Navigation function-based visual servo control

In this paper, the mapping between the desired camera feature vector and the desired camera pose (i.e., the position and orientation) is investigated to develop a measurable image Jacobian-like matrix. An image-space path planner is then proposed to generate a desired image trajectory based on this measurable image Jacobian-like matrix and an image-space navigation function (NF) (i.e., a special potential field function) while satisfying rigid body constraints. An adaptive, homography-based visual servo tracking controller is then developed to navigate the position and orientation of a camera held by the end-effector of a robot manipulator to a goal position and orientation along the desired image-space trajectory while ensuring the target points remain visible (i.e., the target points avoid self-occlusion and remain in the field-of-view (FOV)) under certain technical restrictions. Due to the inherent nonlinear nature of the problem and the lack of depth information from a monocular system, a Lyapunov-based analysis is used to analyze the path planner and the adaptive controller. Simulation results are provided to illustrate the performance of the proposed approach.