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.     

机器人视觉伺服研究进展:视觉系统与控制策略

视觉伺服控制是机器人系统的重要控制手段. 随着机器人应用需求的日益复杂多样,视觉伺服的研究面临着挑战. 视觉伺服系统的设计主要包括视觉系统、控制策略和实现策略三个方面. 文中对视觉伺服中存在的主要问题进行了分析,重点介绍了视觉系统中改善动态性能和处理噪声的主要技术手段,阐述了处理模型不确定性和约束的控制策略的改进方案,总结了提高视觉伺服系统的可实现性和灵活性的实现策略. 最后,基于当前的研究进展对未来的研究方向进行了展望.     

基于无模型自适应控制的视觉伺服

传统的机器人视觉伺服控制技术需要已知机器人精确的动力学和运动学模型以及机器人的手-眼参数。然而,由于机器人建模、手-眼标定等过程存在一定误差,因此很难精确获得视觉伺服控制模型,从而影响机器人视觉伺服系统的精度和收敛速度。针对这一难题,本文提出一种基于无模型自适应控制方法（MFAC）的机器人视觉伺服技术。利用视觉伺服系统的输入与输出数据,实现自适应视觉伺服控制,即通过MFAC在线估计机器人伺服控制器中的雅各比矩阵,并结合滑模控制器,实现机器人对目标的快速精确跟踪。实验结果表明,本文提出的方法在系统参数变化引起的未知扰动情况下仍能保证伺服控制器平稳收敛,并且能够减小视觉跟踪误差。     

单目视觉伺服研究综述

视觉伺服是机器人视觉领域的研究热点之一，具有十分广泛的应用前景.本文针对单目视觉系统，从视觉伺服的运动映射关系、误差表征、控制律设计、关键影响因素等多个层面，对视觉伺服的研究现状进行了论述，并分析了不同视觉伺服方法的特点，给出了视觉伺服在不同领域的典型应用.最后，指出了视觉伺服未来的主要发展方向.     

微操作机器人的视觉伺服控制

视觉伺服控制是微操作机器人实现精确运动,完成自动操作的必要手段．本文介绍 了实现微操作机器人视觉伺服控制的方法．首先论述了微操作机器人的视觉伺服结构,并以 建立的面向生物工程的双手微操作机器人系统为例,介绍了基于二维显微视觉信息的三自由 度柔性铰链微操作机器人的运动学建模方法,针对压电驱动器控制器的特点提出了基本的PI D视觉伺服控制规律实现方法,并进行了点到点运动和圆轨迹跟踪实验．实验结果表明,视 觉伺服控制克服了由于标定以及环境等因素导致的运动模型不准确而引入的误差．     

基于视觉伺服的仓储物流机器人自动定位技术

针对当前仓储物流机器人自动定位技术视觉信息反馈延迟,无法确定深度信息,导致定位误差较大、延迟时间较长的问题.提出了基于视觉伺服的仓储物流机器人自动定位方法.通过几何地图描述全局环境模型,确定机器人结构,分析视觉伺服定位原理,根据雅可比矩阵,标定摄像机位置,利用Parzen窗方法预测像素空间密度,提取主成分,建立误差模型,实现仓储物流机器人自动定位.仿真实验证明,所提方法的定位误差较小,能够有效降低定位延迟时间,满足信息反馈实时性要求.     

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.     

基于视觉伺服的输电线巡检机器人抓线控制

巡检机器人在自动越障时，需要完成机器人手臂的准确抓线控制。结合输电线的几何特征和摄像机成像原理，提出了一种基于单摄像机的立体视觉方法来确定输电线的位置和姿态。结合该定位方法及视觉伺服理论建立机械手抓线伺服控制模型。在自行研制的巡检机器人进行了视觉伺服抓线实验。实验结果验证了该方法的有效性。     

自适应环境的机器人视觉伺服控制方法

机器人和机器视觉的迅速发展,使得基于视觉的智能机器人得到更加广泛的应用。机器视觉提高了机器人控制系统对环境的适应程度,但其适应程度也受到周围环境对机器视觉算法的影响。针对这一情况,本文对机器人视觉伺服控制系统中的机器视觉算法进行了改进,提出了一种基于帧间差分法的自适应环境的机器人视觉伺服控制方法,从而提高了控制系统对环境的自适应程度,提高了对机器人控制的精确度,实验和理论分析证明,该方法具有较大的应用前景和实用价值。     

具有不确定性重力补偿的无标定视觉伺服

文章首先叙述了视觉伺服的分类。给出了机器人动力学模型及其主要性质、自适应复合雅可比矩阵的具体推导过程。摄像机位于机器人末端的情况被考虑,给出了具有不确定性重力补偿的无标定PD视觉伺服控制器,而后运用李雅普诺夫方法对其进行了稳定性分析,结果证明系统能够渐近稳定。为了验证算法的有效性,针对一个二连杆eye-in-hand视觉伺服定位系统进行了仿真,仿真结果说明了算法的有效性。     

机器人视觉伺服系统的研究

机器人伺觉伺服系统及到多学科内容。针对机器人视觉伺服系统主要的三方面内容;系统的结构方式,图象处理,控制方法,介绍了该领域的研究现状及所取得的成就。最后分析了今后的发展趋势。     

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.     

Robust image-based visual servoing using invariant visual information

This paper deals with the use of invariant visual features for visual servoing. New features are proposed to control the 6 degrees of freedom of a robotic system with better linearizing properties and robustness to noise than the state of the art in image-based visual servoing. We show in this paper that by using these features the behavior of image-based visual servoing in task space can be significantly improved. Several experimental results are provided and validate our proposal.     

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.     

不需要标定系统模型的"眼在手上"视觉伺服控制技术

工业实践中不可能精确地标定摄像机和机器人模型，但当前的视觉伺服控制都需要标定系统模型．针对这一现象，提出一种新颖的、能应用于“眼在手上”视觉伺服控制结构的动态无标定的视觉伺服控制算法，无需标定摄像机和机器人运动学模型即可跟踪运动物体，通过将非线性目标函数最小化，以视觉信息跟踪动态图像．针对目前视觉伺服控制系统中“眼在手上”系统的复合雅克比矩阵随每个时间增量的变化无法计算的现象，提出了对每一时间增量时刻的图像雅克比矩阵的变化做出估计的方法，仿真实验证明了上述方法的正确性和有效性．     

机器人视觉伺服系统的标定

机器人视觉伺服系统是机器人领域一项重要的研究方向,它的研究对于开发手眼协调的机器人在工业生产、航空航天等方面的应用有着重要的意义。本文针对机器人视觉伺服系统的特点,对典型的标定算法进行了分类和比较,最后对标定方法进行了总结。     

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.     

Remote Visual Servoing of a Robot Manipulator via Internet2

Visual servoing is a powerful approach to enlarge the applications of robotic systems by incorporating visual information into the control system. On the other hand, teleoperation – the use of machines in a remote way – is increasing the number of applications in many domains. This paper presents a remote visual servoing system using only partial camera calibration and exploiting the high bandwidth of Internet2 to stream video information. The underlying control scheme is based on the image-based philosophy for direct visual servoing – computing the applied torque inputs to the robot based in error signals defined in the image plane – and evoking a velocity field strategy for guidance. The novelty of this paper is a remote visual servoing with the following features: (1) full camera calibration is unnecessary, (2) direct visual servoing does not neglect the robot nonlinear dynamics, and (3) the novel velocity field control approach is utilized. Experiments carried out between two laboratories demonstrated the effectiveness of the application. Work partially supported by CONACyT grant 45826 and CUDI.     

Simultaneously learning to recognize and control a low-cost robotic arm

In this paper, we present a visual servoing method based on a learned mapping between feature space and control space. Using a suitable recognition algorithm, we present and evaluate a complete method that simultaneously learns the appearance and control of a low-cost robotic arm. The recognition part is trained using an action precedes perception approach. The novelty of this paper, apart from the visual servoing method per se, is the combination of visual servoing with gripper recognition. We show that we can achieve high precision positioning without knowing in advance what the robotic arm looks like or how it is controlled.