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.     

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.     

Visual Control of Robots with Delayed Images

This research develops a control scheme for visual servoing that explicitly takes into account the delay introduced by image acquisition and processing. For this purpose, a predictor block, i.e., an estimator that predicts several samples ahead of time, is properly included in the scheme. The proposed approach is analytically analyzed in terms of dynamics and steady-state errors, and compared to previous approaches. Furthermore, several simulations are comparatively shown in order to illustrate the benefits and limitations of the proposed control scheme. Finally, some experimental results using a turntable and a 3-d.o.f. Cartesian robot are provided in order to validate the analytical and simulation results.     

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.     

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.     

On intensity-based 3D visual servoing

This article investigates the problem of pose-based visual servoing whose equilibrium state is defined via a reference image. Differently from most solutions, this work directly exploits the pixel intensities without any feature extraction or matching. Intensity-based methods provide for higher accuracy and versatility. Another central idea of this work concerns the exploitation of the observability issue associated to monocular systems, which always occurs around the equilibrium. This overall framework allows for developing a family of new 3D visual servoing techniques with varying degrees of computational complexity and of prior knowledge. Importantly, they are arranged hierarchically from the simplest to the optimal ones, all in a unified scheme. Three new methods are then presented, and their closed-loop performances are experimentally assessed using a six degree-of-freedom robotic arm. As an additional contribution, these results refute the common belief that correct camera calibration and pose recovery are crucial to the accuracy of 3D visual servoing techniques.     

移动机器人视觉伺服镇定quasi-min-max预测控制

针对受限移动机器人视觉伺服系统,提出一种移动机器人视觉伺服镇定准最小最大模型预测控制策略. 基于移动机器人视觉伺服镇定误差模型,建立移动机器人视觉伺服线性参数时变预测模型,进而引入准最小最大策略,设计移动机器人视觉伺服镇定模型预测控制器.与传统视觉伺服预测控制器相比,所提控制器只需求解线性矩阵不等式表示的凸优化问题,降低了视觉伺服预测控制器的计算耗时,同时保证了闭环视觉伺服系统的渐近稳定性.仿真结果验证了所提出策略的有效性和在计算效率上的优越性.     

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.     

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.     

Statistically robust 2-D visual servoing

A fundamental step toward broadening the use of real-world image-based visual servoing is to deal with the important issue of reliability and robustness. In order to address this issue, a closed-loop control law is proposed that simultaneously accomplishes a visual servoing task and is robust to a general class of image processing errors. This is achieved with the application of widely accepted statistical techniques such as robust M-estimation and LMedS. Experimental results are presented which demonstrate visual servoing tasks that resist severe outlier contamination.     

Autonomous robotic capture of non-cooperative target using visual servoing and motion predictive control

This paper presents a framework for autonomous capture operation of a non-cooperative mobile target in a 3-dimensional workspace using a robotic manipulator with visual servoing. The visual servoing with an eye-in-hand configuration is based on motion predictive control using Kalman filter for the on-line state and parameter estimation of the target. A transitional decision making process is developed to guide the robotic manipulator between the different phases of the capture operation by employing a custom metric that translates visual misalignments between the end-effector and the target into a guidance measurement. These phases include the target acquisition and approach stage and the alignment and capture phase. Experiments have been carried out on a custom designed and built robotic manipulator with 6 degrees of freedom. The objective is to evaluate the performance of the proposed motion predictive control scheme for the autonomous capturing task and to demonstrate the robustness of the proposed control scheme in the presence of noise and unexpected disturbances in vision system, sensory-motor coordination and constraints for the execution in real environments. Experimental results of the visual servoing control scheme integrated with the motion predictive Kalman filter indicate the feasibility and applicability of the proposed control scheme. It shows that when the target motion is properly predicted, the tracking and capture performance has been improved significantly.     

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...     

Visual servoing of redundant manipulator with Jacobian matrix estimation using self-organizing map

Vision based redundant manipulator control with a neural network based learning strategy is discussed in this paper. The manipulator is visually controlled with stereo vision in an eye-to-hand configuration. A novel Kohonen’s self-organizing map (KSOM) based visual servoing scheme has been proposed for a redundant manipulator with 7 degrees of freedom (DOF). The inverse kinematic relationship of the manipulator is learned using a Kohonen’s self-organizing map. This learned map is shown to be an approximate estimate of the inverse Jacobian, which can then be used in conjunction with the proportional controller to achieve closed loop servoing in real-time. It is shown through Lyapunov stability analysis that the proposed learning based servoing scheme ensures global stability. A generalized weight update law is proposed for KSOM based inverse kinematic control, to resolve the redundancy during the learning phase. Unlike the existing visual servoing schemes, the proposed KSOM based scheme eliminates the computation of the pseudo-inverse of the Jacobian matrix in real-time. This makes the proposed algorithm computationally more efficient. The proposed scheme has been implemented on a 7 DOF PowerCube? robot manipulator with visual feedback from two cameras.     

Sliding mode variable structure control for visual servoing system

A visual servoing tracking controller is proposed based on the sliding mode control theory in order to achieve strong robustness against parameter variations and external disturbances. A sliding plane with time delay compensation is presented by the pre-estimate of states. To reduce the chattering of the sliding mode controller, a modified exponential reaching law and hyperbolic tangent function are applied to the design of visual controller and robot joint controller. Simulation results show that the visual servoing control scheme is robust and has good tracking performance.     

带有时变计算时延的倒立摆视觉实时控制研究

针对倒立摆视觉实时控制系统中,通过每帧图像处理检测小车位移和摆杆偏角带来的时变图像处理计算时间,直接影响控制系统采样周期和系统性能的问题,不同于目前不考虑图像处理计算时间或将其视为定时滞进行倒立摆视觉实时控制的研究方法,本文首先构建了倒立摆视觉伺服控制实验平台,考虑倒立摆的小车与摆杆特征,提出了小车位移和摆杆偏角的实时计算方法;然后统计分析了图像处理计算时间的特性并设计了视觉传感事件触发采样策略,进一步刻画了时变时延与计算时间的直接关系,进而建立了带有时变计算时延的倒立摆视觉控制系统闭环模型,证明了系统稳定性并建立了反映图像处理计算时间与系统稳定性之间的关系.最后,仿真和实时控制实验验证了所提方法的可行性和有效性.     

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.     

一种基于Dyna-Q学习的旋翼无人机视觉伺服智能控制方法

基于图像的视觉伺服机器人控制方法通过机器人的视觉获取图像信息,然后形成基于图像信息的闭环反馈来控制机器人的合理运动.经典视觉伺服的伺服增益的选取在大多数条件下是人工赋值的,故存在鲁棒性差、收敛速度慢等问题.针对该问题,提出一种基于Dyna-Q的旋翼无人机视觉伺服智能控制方法调节伺服增益以提高其自适应性.首先,使用基于费尔曼链码的图像特征提取算法提取目标特征点;然后,使用基于图像的视觉伺服形成特征误差的闭环控制;其次,针对旋翼无人机强耦合欠驱动的动力学特性提出一种解耦的视觉伺服控制模型;最后,建立使用Dyna-Q学习调节伺服增益的强化学习模型,通过训练可以使得旋翼无人机自主选择伺服增益.Dyna-Q学习在经典的Q学习的基础上通过建立环境模型来存储经验,环境模型产生的虚拟样本可以作为学习样本来进行值函数的迭代.实验结果表明,所提出的方法相比于传统控制方法PID控制以及经典的基于图像视觉伺服方法具有收敛速度快、稳定性高的优势.     

Developing robust vision modules for microsystems applications

In this work, several robust vision modules are developed and implemented for fully automated micromanipulation. These are autofocusing, object and end-effector detection, real-time tracking and optical system calibration modules. An image based visual servoing architecture and a path planning algorithm are also proposed based on the developed vision modules. Experimental results are provided to assess the performance of the proposed visual servoing approach in positioning and trajectory tracking tasks. Proposed path planning algorithm in conjunction with visual servoing imply successful micromanipulation tasks.     

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.     

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.