基于图像视觉伺服的二维运动优化控制

基于图像的视觉伺服方法,图像的变化直接解释为摄像机的运动,而不是直接对机械手末端实现笛卡尔速度控制,导致机械手的运动轨迹迂回,产生摄像机回退现象.针对这一问题,提出了将旋转和平移分离并先实现旋转的视觉伺服方案.该方案计算量小,系统响应时间短,解决了图像旋转和平移间的干扰,克服了传统基于图像视觉伺服产生的摄像机回退现象,实现了时间和路径的最优控制.并用传统IBVS的控制律和摄像机成像模型解释了回退现象的产生原因.二维运动仿真说明了提出方案的有效性.     

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。     

基于DDPG算法的旋翼无人机智能跟踪方法

针对旋翼无人机视觉伺服控制可视性约束差与控制效率低的问题,介绍四旋翼无人机的动力学模型与传统视觉伺服的基本原理;针对视觉伺服增益调节问题,提出了一种基于深度强化学习的旋翼无人机视觉伺服控制的马可科夫模型;并结合深度确定性策略梯度（DDPG）算法训练了模型,最终得到线速度和角速度2个解耦的伺服增益。结果表明,与传统的基于图像的视觉伺服（IBVS）方法相比,该方法能保证目标始终在视场中、飞行轨迹平滑、到达目标位置所需时间短,满足旋翼无人机视觉伺服控制可视性约束条件,同时,控制效率比传统控制方法高23.5%。实验中该方法能在复杂的非线性环境中实现精确控制,增加了视觉伺服技术的应用场景。     

基于雅可比预测的机器人无模型视觉伺服定位控制

针对未知相机标定及目标3D几何模型,研究机器人无模型视觉伺服定位方法.引入状态空间,建立机器人“视觉空间-运动空间”雅可比非线性映射的状态方程和观测方程,提出神经网络联合卡尔曼滤波雅可比预测算法,网络在线动态补偿系统近似误差与参数估计误差,实现最小均方差条件下的雅可比预测;以李雅普诺夫稳定性准则构建雅可比预测的无模型图像视觉伺服控制方案,避免了相机标定和目标建模.“眼在手”六自由度机器人定位比较实验表明,图像空间特征轨迹平滑稳定在相机视场中,笛卡尔空间机器人末端运动平稳,无震荡回退,定位精度在10个像素范围内.     

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.     

机器人视觉伺服研究综述

首先对于3种机器人视觉伺服策略,即基于位置的视觉伺服、基于图像的视觉伺服以及2.5维视觉伺服进行了讨论.然后,对于视觉伺服的研究方向和面临的主要问题,如机器人位姿提取、视觉伺服系统的不确定性研究、图像空间的路径规划、智能视觉伺服等进行了分析和讨论.在此基础上,对于机器人视觉伺服领域的未来研究重点,包括如何使参考点位于视场之内,高速伺服策略以及鲁棒视觉伺服技术进行了分析和展望.     

微小无人直升机自适应视觉伺服

针对摄像机未标定和特征点坐标未知的情况, 本文提出一种新颖的基于图像的无人直升机自适应视觉伺服方法. 控制器是基于反推法设计的, 但是和已有的基于反推法的视觉伺服不同的是, 它利用与深度无关矩阵将图像误差映射到执行器空间, 从而可以避免估计特征点的深度. 这种设计方法可以线性化未知的摄像机参数和特征点坐标, 所以能方便地设计自适应算法来在线估计这些未知参数, 同时为了保证图像误差收敛和避免估计参数收敛至零解而引入了两个势函数. 利用Lyapunov方法证明了基于非线性动力学的控制器的稳定性, 并给出了仿真验证.     

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.     

基于虚拟势场法的全局收敛视觉路径规划

作为一种局部路径规划策略, 虚拟势场法由于其简单易用, 效果良好而在机器人领域得到了广泛的应用. 但是这种方法的缺点是在路径规划的过程中, 机器人经常会陷入局部极小. 本文提出了一种基于虚拟势场法的视觉路径规划策略, 可以保证图像中的特征点在伺服过程中始终保持在摄像机的视野之内, 并且通过稳定性分析证明了该策略具有全局收敛性. 另外, 本文进一步探讨了视觉伺服中另一个常见问题: 如何在规划过程中得到较好的三维运动轨迹. 最后用仿真结果验证了本文所提出的路径规划策略具有良好的性能.     

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.     

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.     

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.     

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.     

Reference Trajectory-Based Visual Predictive Control

Abstract

In this paper we present an image predictive controller for an eye-in-hand-type servoing architecture, composed of a 6-d.o.f. robot and a camera mounted on the gripper. A novel architecture for integrating reference trajectory and image prediction is proposed for use in predictive control of visual servoing systems. In the proposed method, a new predictor is developed based on the relation between the camera velocity and the time variation of the visual features given by the interaction matrix. The image-based predictor generates the future trajectories of a visual feature ensemble when past and future camera velocities are known. In addition, a reference trajectory is introduced to define the way how to reach the desired features over the prediction horizon starting from the current features. The advantages of the new architecture are the reference trajectory used for the first time in the sense of the predictive control and the predictor based on a local model. Simulations reveal the efficiency of the proposed architecture to control a 6-d.o.f. robot manipulator.     

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.     

Vision Based Output Feedback Control of Micro Aerial Vehicles in Indoor Environments

We present a new image based visual servoing (IBVS) approach for control of micro aerial vehicles (MAVs) in indoor environments. Specifically, we show how a MAV can be stabilized and guided using only corridor lines viewed on a front facing camera and angular velocity measurements. Since the suggested controller does not include explicit attitude feedback it does not require the use of accelerometers which are susceptible to vibrations, nor complex attitude estimation algorithms. The controller also does not require direct velocity measurements which are difficult to obtain in indoor environments. The paper presents the new method, stability analysis, simulations and experiments.