Unifying Kinematic Modeling, Identification, and Control of a Gough–Stewart Parallel Robot Into a Vision-Based Framework

In this paper, it is shown that computer vision, used as an exteroceptive redundant metrology mean, simplifies the control of a Gough-Stewart parallel robot. Indeed, contrary to the usual methodology, where the robot is modeled independently from the control law which will be implemented, we take into account that vision will be used for control, from the early modeling stage. Hence, kinematic modeling and projective geometry are fused into a control-devoted projective kinematic model. Thus, a novel vision-based kinematic modeling of such a robot is proposed through the observation of its legs. Inspired by the geometry of lines, this model unifies and simplifies both identification and control. Indeed, it has a reduced parameter set, and allows us to propose a linear solution to its calibration. Using the same model, a visual servoing scheme is presented, where the attitudes of the nonrigidly linked legs are servoed, rather than the end-effector pose. Finally, theoretical results concerning the stability of this control law are provided     

Distributed Geodesic Control Laws for Flocking of Nonholonomic Agents

We study the problem of flocking and velocity alignment in a group of kinematic nonholonomic agents in 2 and 3 dimensions. By analyzing the velocity vectors of agents on a circle (for planar motion) or sphere (for 3-D motion), we develop a geodesic control law that minimizes a misalignment potential and results in velocity alignment and flocking. The proposed control laws are distributed and will provably result in flocking when the underlying proximity graph which represents the neighborhood relation among agents is connected. We further show that flocking is possible even when the topology of the proximity graph changes over time, so long as a weaker notion of joint connectivity is preserved     

Visual object recognition with supervised learning

A component-based approach to visual object recognition rooted in supervised learning allows for a vision system that is more robust against changes in an object's pose or illumination. Learning figures prominently in the study of visual systems from the viewpoints of visual neuroscience and computer vision. Whereas visual neuroscience concentrates on mechanisms that let the cortex adapt its circuitry and learn a new task, computer vision aims at devising effectively trainable systems. Vision systems that learn and adapt are one of the most important trends in computer vision research. They might offer the only solution to developing robust, reusable vision systems.     

A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications

Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.

     

A Novel In situ Recognition of Misalignment between Mating Parts in Robotic Assembly Processes

A visual sensing system is utilized mainly to estimate the misalignment between mating parts, the recognition of which is the integral part of any assembly process. The recognition, however, requires the information on the state of the misalignment that includes the shapes of parts in mating motion and instantaneous relative position and angular orientation between mating parts. Normally, this information has been given in advance by an operator to facilitate assembly action. Therefore, in order to recognize the assembly state in sequence without intervention of an operator, it requires an effective sensing system and algorithm capable of working well even without a priori information on part shape and location. In this paper, we propose a novel system that can assemble parts under such uncertain environments. The system, composed of an omnidirectional sensing module and a recognition module, is capable of acquiring information on the sequential state of parts assembly motion from which instantaneous, relative location and orientation between the mating parts can be determined. Since the system does not utilize a priori knowledge on the shape of mating parts, it greatly reduces the degree of human intervention, thus increasing autonomy and flexibility. To evaluate the performance of the proposed system, a series of assembly experiments are performed. The results show that the proposed system, indeed, demonstrates effectiveness of vision guided assembly action. © 2002 Wiley Periodicals, Inc.     

四自由度折叠式机械手的控制系统

四自由度折叠式机械手是一台ＳＴＤ戒严控制机管理和控制，通过视觉图象和接近觉传感器操纵机械手在移动空间中完成联送机器人任务，文中推导了机械手运动学方程，给出了运动学正逆解，详细介绍了可编位置控制系统和操纵系统。     

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．     

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.     

Toward a practical face recognition system: robust alignment and illumination by sparse representation

Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they are used in a real recognition system. This is mostly due to the difficulty of simultaneously handling variations in illumination, image misalignment, and occlusion in the test image. We consider a scenario where the training images are well controlled and test images are only loosely controlled. We propose a conceptually simple face recognition system that achieves a high degree of robustness and stability to illumination variation, image misalignment, and partial occlusion. The system uses tools from sparse representation to align a test face image to a set of frontal training images. The region of attraction of our alignment algorithm is computed empirically for public face data sets such as Multi-PIE. We demonstrate how to capture a set of training images with enough illumination variation that they span test images taken under uncontrolled illumination. In order to evaluate how our algorithms work under practical testing conditions, we have implemented a complete face recognition system, including a projector-based training acquisition system. Our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.     

基于自抗扰控制技术的捷联罗经对准算法

在大失准角条件下,研究了自抗扰捷联罗经对准算法．基于欧拉平台误差角概念建立了适用于自抗扰控制的二阶水平通道状态空间模型．以水平速度误差作为量测实现水平姿态对准,从稳定的水平通道指令角速度中提取方位失准角信息,完成方位自对准．仿真结果表明,该对准算法可较快地实现大失准角的自对准且对准精度与经典罗经对准法相当．     

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.     

Development of a vision-based ground target detection and tracking system for a small unmanned helicopter

It is undoubted that the latest trend in the unmanned aerial vehicles (UAVs) community is towards visionbased unmanned small-scale helicopter, utilizing the maneuvering capabilities of the helicopter and the rich information of visual sensors, in order to arrive at a versatile platform for a variety of applications such as navigation, surveillance, tracking, etc. In this paper, we present the development of a visionbased ground target detection and tracking system for a small UAV helicopter. More specifically, we propose a real-time vision algorithm, based on moment invariants and two-stage pattern recognition, to achieve automatic ground target detection. In the proposed algorithm, the key geometry features of the target are extracted to detect and identify the target. Simultaneously, a Kalman filter is used to estimate and predict the position of the target, referred to as dynamic features, based on its motion model. These dynamic features are then combined with geometry features to identify the target in the second-stage of pattern recognition, when geometry features of the target change significantly due to noise and disturbance in the environment. Once the target is identified, an automatic control scheme is utilized to control the pan/tilt visual mechanism mounted on the helicopter such that the identified target is to be tracked at the center of the captured images. Experimental results based on images captured by the small-scale unmanned helicopter, SheLion, in actual flight tests demonstrate the effectiveness and robustness of the overall system.     

A low-cost calibration method for automated optical mensuration using a video camera

Automated optical mensuration gauges the acquired image of the inspected unit while assessing its actual size and shape. The mensuration requires the following preparations: (1) alignment of the video camera perpendicularly to the inspection table, and (2) calibration of the scale ratios of image acquisition, notably, the stretching ratio caused by signal conversion and the magnification ratio of optical coupling. This paper presents the unique two-stage calibration method. The first stage applies the parallelogram conservation property, a property very sensitive to misorientation, to test against the potential misalignment. Once detected, we adjust the misalignment towards orthogonal alignment using image patterns of the calibration template. Then, the second stage determines the scale ratios. The proposed calibration method is suitable for on-site applications, and its implementation cost is low. Sensitivity analysis and experimental results are reported.     

基于捷联惯导系统的杆臂效应误差分析

随着捷联惯导系统的发展,对惯性导航精度和快速性的要求越来越高。而快速性主要取决于初始对准时间。传统的初始对准方法具有一定的局限性,而传递对准是一种新型的、快速的初始对准方法。主要分析了传递对准方法中的速度匹配方法,在此基础上推导了杆臂效应的基本原理。最后通过仿真得到,存在杆臂效应时,航向失准角估计误差稳态值为-0.5°;通过实船实验得到,存在杆臂效应时,俯仰、横滚和航向角的失准角估计误差分别为-0.22°,-0.37°,-0.35°,从而验证了杆臂效应误差对失准角的影响。     

基于手势识别的视力检测系统的设计

This design realizes a vision detection system, which can detect human visual acuity through gesture recognition technology. The system includes several modules: acquisition, data storage and conversion, gesture recognition and output control. And the camera, memory, display and speaker are connected externally. The visual icon "E" is displayed on the display screen, and then people use gestures to indicate the direction of "E". After the camera captures the image, the system recognizes and judges the direction of gesture, and then compares the correct direction to detect the visual acuity of people. Because the system eliminates manual detection, it is convenient, accurate and it saves manpower. The system realizes dynamic gesture interaction, being interesting and easy to be popularized.     

毫米级全方位移动微型装配机器人设计、运动学分析与控制

介绍了一种用于微型工厂的毫米级移动微装配机器人,其具有独特的全方位运动结构．微机器人由4个直径3 mm的电磁微马达驱动,并装备有一对微型夹钳．通过分析运动学矩阵的秩,证明了微机器人的全方位特性,并建立了微夹钳的运动学方程．设计了基于计算机视觉的微机器人控制系统,给出了微机器人定位和驱动方法．实验证明了微机器人的负载能力、机动性以及控制系统的有效性．     

Face recognition via fast dense correspondence

Face recognition plays a significant role in computer vision. It is well know that facial images are complex stimuli signals that suffer from non-rigid deformations, including misalignment, orientation, pose changes, and variations of facial expression, etc. In order to address these variations, this paper introduces an improved sparse-representation based face recognition method, which constructs dense pixel correspondences between training and testing facial samples. Specifically, we first construct a deformable spatial pyramid graph model that simultaneously regularizes matching consistency at multiple spatial extents - ranging from an entire image, though coarse grid cells, to every single pixel. Secondly, a matching energy function is designed to perform face alignment based on dense pixel correspondence, which is very effective to address the issue of non-rigid deformations. Finally, a novel coarse-to-fine matching scheme is designed so that we are able to speed up the optimization of the matching energy function. After the training samples are aligned with respect to testing samples, an improved sparse representation model is employed to perform face recognition. The experimental results demonstrate the superiority of the proposed method over other methods on ORL, AR, and LFWCrop datasets. Especially, the proposed approach improves nearly 4.4 % in terms of recognition accuracy and runs nearly 10 times faster than previous sparse approximation methods.     

Image processing of novel vision-assisted hard disk drive flex cable-actuator manufacturing

While advancing storage capacity for magnetic disk drives into multi-terabyte domain, the corresponding form factor is being reduced due to significant application demand in various consumer electronics sectors and requirements for power reduction. These two inversely trends veer at a point that any misalignment due to mechanical tolerances or assembling inconsistency can result in excessive operating dynamics issues and drive-to-drive final performance variation. Among all mechanical factors, misalignment of flex cable assembly and the corresponding tolerance build up from adjacent parts during manufacturing and packaging into disk drives can result in deviation of real flex cable profile from engineered design case, which inevitably gives rise to issues of uncontrollable variation in mass production. Especially when (1) settling requirement is reaching to hundreds of nanometers and (2) more data sectors are packed in a circular trace than before because of advancement of perpendicular recording technology, low frequency and long settling flex cable dynamics become the most and difficult task to resolve when disk drive’s capacity is beyond a terabyte point. The work presented in this paper suggests a solution to reduce such variation and to ensure flex cable performance at mass production level. An alternative approach in assembling flexible circuitry assembly on to actuator comb and integrated lead suspensions is proposed. Apart from traditional alignment methods, robotic vision guided method and assembling system are adopted and developed. Its capability of reaching micro-scale position and alignment accuracy is demonstrated with lower drive-to-drive flex dynamic performance variation.     

具有未知视觉参数的非完整移动机器人的自适应动态反馈跟踪控制

基于视觉反馈和标准链式形式,研究了一类不确定非完整移动机器人的轨迹跟踪控制问题.首先,利用针孔摄像机模型,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,在具有不确定视觉参数的情形下,利用back-stepping技术,设计出了一种新的自适应动态反馈跟踪控制器,实现了全局渐近的轨迹跟踪,并通过李亚普诺夫方法严格证明了闭环系统的稳定性和估计参数的有界性.仿真结果证明了所提出的控制器的有效性.     

一种基于MEMS惯性传感器的手势识别方法

随着手机等移动电子设备的发展,应用于嵌入式平台的基于MEMS惯性传感器的手势识别成为一个研究热点.提出了一种简单有效的手势识别方法:通过分析手势的运动学特征,在线实时提取手势的加速度和角速度信号特征量,截取手势信号段,利用决策树分类器进行预分类,根据手势信号的变化规律实时识别具体的手势.该方法在20位实验者中获得了96％的平均准确率,手势识别时间小于0.01s.实验结果表明该算法在嵌入式平台下能快速准确地识别手势,满足了实时人机交互的要求.