基于点线特征的解耦视觉伺服控制方法

针对机器人的自动对准问题,提出一种基于点线特征的解耦视觉伺服控制方法。所提方法以点和直线作为图像特征,并利用图像特征的交互矩阵解耦姿态控制和位置控制,实现六自由度对准。首先利用直线及其交互矩阵设计姿态控制律,以消除旋转偏差;然后利用点及其交互矩阵设计位置控制律,以消除位置偏差;最后实现机器人末端目标的自动对准。在对准控制过程中,基于执行的相机运动量以及相机运动前后特征的变化量,可实现对深度的在线估计。另外,还设计了监督器对相机的运动速度进行调节,从而确保特征一直处于相机视野当中。在Eye-in-Hand机器人平台上,分别用所提方法和传统的基于图像的视觉伺服方法实现了机器人的六自由度对准。所提方法经过16步实现了机器人的自动对准,对准结束时机器人末端位姿的最大平移误差为3.26 mm,最大旋转误差为0.72°。相较于对比方法,该方法的控制过程更加高效,控制误差收敛更快,对准误差更小。实验结果表明,所提方法可以实现快速高精度的自动对准,能够提高机器人操作的自主性和智能化水平,有望应用于目标跟踪、拾取和定位、自动化装配、焊接、服务机器人等领域。     

乒乓球机器人的视觉伺服系统

视觉伺服的乒乓球机器人系统作为典型的"手眼系统",是研究高速视觉感知和快速伺服运动的理想平台,其涉及的高速物体识别跟踪、快速精确轨迹预测及机械臂伺服准确回球等关键技术在工业、军事等领域有广泛的应用前景.本文提出了乒乓球机器人的高速视觉伺服系统实现方法,包括基于特征直方图统计和快速轮廓搜索的目标识别算法,基于模型参数学习和自适应模型调整的物体运动状态估计和轨迹预测算法,及基于轨迹预测的灵巧臂回球规划算法.通过实验验证了各算法的实时性和高效性,并在165cm高的仿人机器人"悟"和"空"上成功实现了双机器人对打和与人对打任务.     

A review on vision-based control of flexible manipulators

In this review, recent developments in the field of flexible robot arm control using visual servoing are reviewed. In comparison to rigid robots, the end-effector position of flexible links cannot be obtained precisely enough with respect to position control using kinematic information and joint variables. To solve the task here the use of a vision sensor (camera) system, visual servoing is proposed to realize the task of control flexible manipulators with improved quality requirements. The paper is organized as follows: the visual servoing architectures will be reviewed for rigid robots first. The advantages, disadvantages, and comparisons between different approaches of visual servoing are carried out. The using of visual servoing to control flexible robot is addressed next. Open problems such as state variables estimation as well as the combination of different sensor properties as well as some application-oriented points related to flexible robot are discussed in detail.     

Robust place recognition based on omnidirectional vision and real-time local visual features for mobile robots

Topological localization is especially suitable for human–robot interaction and robot’s high level planning, and it can be realized by visual place recognition. In this paper, bag-of-features, a popular and successful approach in pattern recognition community, is introduced to realize robot topological localization. By combining the real-time local visual features proposed by ourselves for omnidirectional vision and support vector machines, a robust and real-time visual place recognition algorithm based on omnidirectional vision is proposed. The panoramic images from the COLD database were used to perform experiments to determine the best algorithm parameters and the best training condition. The experimental results show that the robot can achieve robust topological localization with high successful rate in real time by using our algorithm.     

遥操作机器人的新型力反馈算法*

在采用液压挖掘机改造的遥操作机器人双向伺服控制系统中,针对大臂和前臂两个自由度构建力反馈控制算法。以准确地获取从端机器人与环境的作用力,使反馈力能够更好地反映从端工作状况为目的,采用构建干扰力补偿项的方法消除干扰力对反馈力的影响;以机器人转角为输入,以空载时检测到的液压缸作用力为输出,通过径向基函数构建干扰力补偿项,此补偿项可对多种因机器人的机械本体动力学特性产生的干扰力之合力进行补偿。实验证明,在以液压机构为从手的双向力反馈系统中,通过构建干扰力补偿项的方法提高力反馈效果的方法是可行的,采用的带有干扰力     

基于分解动量的仿人机器人手臂高速运动实时平衡控制

本文面向仿人机器人7自由度手臂高速动态作业运动需求,提出了基于分解动量的仿人机器人实时平衡控制方法,给出了作业臂运动用末端执行器速度向量表示下的机器人总动量计算公式,分析了动量控制维度的择优选取原则,给出了基于分解动量控制的辅助臂关节速度实时计算优化方法.仿真实验表明,经过改进优化的分解动量控制方法对于仿人机器人手臂高速作业运动下的平衡控制具有较高的可行性和实时性,不仅所控制维度的分解动量完全达到了预期的效果,而且其他维度的分解动量也有所降低或基本维持不变,所生成辅助臂运动平滑且在关节运动性能上有较大裕度,机器人总体具有优秀的零力矩点(zero-moment point,ZMP)稳定性.     

基于人机交互的机械臂位姿自动监视系统设计

为了提高机械臂位姿自动监视和控制能力,提出一种基于人机交互的机械臂位姿自动监视系统设计方法,系统设计包括机械臂的位姿自动控制算法设计和系统硬件设计两部分,采用粒子滤波位姿跟踪方法进行机械臂的动态定位位姿参数信息采集和融合处理,在动态和静态环境下,结合加速全局定位方法进行机械臂位姿自动监视的人机交互设计,构建机械臂人机交互的动态定位控制模型,采用自适应位姿跟踪方法实现对机械臂的位姿自动监视,通过捕捉未知目标的质量特性参数,实现机械臂的位姿自动监视和人机交互设计。通过DSP和嵌入式ARM实现对机械臂位姿自动监视系统的硬件设计。测试结果表明,采用该方法进行机械臂位姿自动监视的自动化水平较高,智能性较好,提高了机械臂的位姿自动监视和控制能力。     

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.     

Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.     

Assistant force compensation for hand movement of patients by using exogenous signals and/or neurophysiological signals

Because functional diseases of the brain can cause disabilities related to human movement control, a compensation method was developed for improving the performance of hand movements. The compensation for human hand movements can be carried out by adding an assistant force that is generated from artificial equipment attached to a human arm. From the experiment on visual target tracking, it was found that the tracking trajectory was adequately represented by a dynamic model of the motion of an articulated industrial robot arm, and the different abilities for movement control among healthy people and patients were classified by different model parameters as position loop gain, velocity loop gain, and response delay. Dynamic force compensation was approached by considering the different control features of the patients. The effectiveness of the proposed compensation method was verified in a simulation study on an actual industrial robot arm. A human-machine interface, e.g., a brain-computer interface (BCI), for realizing the control of artificial equipment to compensate for human hand movements is also presented and discussed.     

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。     

移动机器人轨迹跟踪快速终端滑模自抗扰控制

针对移动机器人动力学模型难以精确建立、运动过程中各种干扰对高精度轨迹跟踪造成偏航等问题,构造出一种快速终端滑模自抗扰控制器,实现了高速高精度轨迹跟踪控制目标.首先建立非完整移动机器人的干扰控制模型;然后运用扩张状态观测器实时监测系统未建模动态与各种干扰;同时将扩张状态量和系统反馈量作为快速终端滑模算法的系统变量;最后设...     

Teleoperation of robot arm with position measurement via angle-pixel characteristic and visual supporting function

In this paper, a teleoperation system of a robot arm with position measurement function and visual supporting function is developed. The working robot arm is remotely controlled by the manual operation of the human operator and the autonomous control via visual servo. The visual servo employs the template matching technique. The position measurement is realized using a stereo camera based on the angle-pixel characteristic. The visual supporting function to give the human operator useful information about the teleoperation is also provided. The usefulness of the proposed teleoperation system is confirmed through experiments using an industrial articulated robot arm.     

On-Line Robotic Interception Planning Using a Rendezvous-Guidance Technique

A novel method for online, robotic interception of moving objects using visual feedback is proposed in this paper. No prior knowledge of the motion of the object is assumed. Since such objects might depart quickly from the workspace of the robot, fast interception is a critical issue. Thus, a novel time-optimal rendezvous-guidance technique that takes the dynamic limitations of the robot into account has been developed. In the proposed methodology, first, a parallel-navigation rule, originally introduced in the missile-guidance literature, is applied to generate a set of instantaneous task-space velocity commands, which, if executed, would keep the end-effector on a collision course with the object. Subsequently, a rendezvous-guidance method is utilized to reduce the original command set to one with velocity-matching capability. Finally, the fastest velocity command in the reduced set is chosen such that the dynamic limitations of the actuators of the robot are not violated. The proposed algorithm results in a fast and robust interception as shown by several simulation examples in 2D and 3D workspace.     

桌面型机械臂的机器视觉应用框架研究

与传统工业机械臂相比,桌面型机械臂具有环境多变、人机协作等特点,为其增加视觉功能显得尤为重要。而目前实现机器视觉的应用框架有很多,如何根据机械臂工作环境及性质,搭建合适的视觉应用软硬件平台,以提高机器视觉识别的准确率和效率是本文研究的重点。本文通过采用TensorFlow深度学习框架,利用嵌入式系统的软硬件设计,结合OpenCV等图像处理软件,搭建适合桌面型机械臂的机器视觉二次开发框架,为进一步开发基于视觉的机械臂应用提供了基础。仿真测试及人机协作的案例应用表明该框架具有较好的适应性和高效性。     

六足机器人动态目标检测与跟踪系统研究

动态目标检测与目标跟踪是图像领域的热点研究问题,为研究其在移动机器人领域的应用价值,设计了六足机器人动态目标检测与跟踪系统。针对非刚体运动目标容易被检测为多个分散区域的问题提出区域合并算法,并通过对称匹配、自适应外点滤除对运动背景进行精确补偿,最终基于背景补偿法实现对运动目标的精确检测。研究了基于KCF（Kernel Correlation Filter）的目标跟踪算法在六足机器人平台上的应用,设计了自适应跟踪算法实现六足机器人对运动目标的角度跟踪。将运动目标检测及跟踪算法应用于六足机器人系统。实验表明,在六足机器人移动过程中,系统可对运动目标进行精确检测与跟踪。     

DCT图像压缩方法的改进及其应用

利用基于二维离散余弦变换（DCT）的方法进行图像压缩,通过设置自适应量化截断阈值来控制图像压缩数组的大小。为了加快运算速度,在图像压缩部分利用快速DCT算法;并直接对量化后的非零系数进行存储,而不采用熵编码的方式。在解码端,针对量化产生的块效应,利用基于自适应邻域的思想进行后处理。所提方法进一步提高了图像的峰值信噪比和主观视觉质量,并且达到了实时在线处理的要求。文中所提基于DCT的图像压缩方法在一个基于视觉位置反馈的二自由度机械臂远程控制系统中得到实际应用。     

A flexible-arm as manipulator position and force detection unit

This paper presents a self-sensory robot arm to two basic sensing problems in control of flexible manipulators: detection of the position and orientation variations due to structural deformation and detection of the contact force of end-effector when manipulator interacts with its environment. Taking advantage of structural flexibility, a flexible robot arm with strain gauges distributed on it acts as a sensing unit. The position and orientation of flexible arm are expressed as a function of curvature of the arm. An interpolation technique gives this continuous curvature function from a finite set of measurements made with strain gauges. A relation between strain measurements and endpoint force of flexible arm is developed and the contact force of end-effector is then determined using a force propagation algorithm. The proposed technique and algorithm were implemented and evaluated in a laboratorial flexible arm. Experimental validations using a vision system and two force sensors have shown that the self-sensory flexible arm can provide accurate endpoint position and force in both static and dynamic loading situations.     

The path precompensation method for flexible arm robot

This paper constructed a closed loop path precompensation method for a flexible arm robot. A torque computation method taking care of the elastic arm deformation was first proposed and discussed. A concept of partial deformation compensation was subsequently proposed to improve the torque profiles and the trajectory fidelity. The advantage of this concept was first shown by examples of planar trajectory. After the construction of the closed-loop path precompensation method for a flexible arm, the torque method and partial deformation compensation were incorporated to track the spatial trajectory. Numerical simulations were given to show the usefulness of the proposed concept and method.     

基于预测控制的非完整移动机器人视觉伺服镇定

非完整移动机器人视觉伺服镇定越来越受到人们的广泛关注. 目前研究人员在解决该问题时未同时考虑摄像机的可见性约束和机器人系统的控制约束, 所设计的控制器在实际应用中很难实现满意的控制. 针对此问题, 本文设计一种预测控制器来解决移动机器人视觉伺服镇定问题. 首先设计运动学预测镇定控制器来产生参考速度指令; 然后设计动力学预测控制器使移动机器人实际速度渐近逼近期望值; 所设计的预测控制器能够容易处理系统中存在的可见性约束和控制约束; 最后对所提出的视觉伺服镇定方法进行仿真验证, 结果表明所设计的控制器能有效解决移动机器人视觉伺服镇定问题.