Robust Jacobian matrix estimation for image-based visual servoing

A method for local estimation through training of the feature Jacobian for uncalibrated closed-loop robot manipulator control is presented, which can handle non-Gaussian outliers due to illumination changes. This is achieved through the employment of a robust estimator. The method is experimentally validated through a sunroof fitting robot in a closed-loop control scheme.     

带有时延补偿的图像雅可比矩阵估计方法

提出一种新的带有时延补偿的图像雅可比矩阵在线估计方法, 用于存在时延的无标定视觉伺服系统.传统的图像雅可比矩阵估计方法没有考虑时延影响, 从而产生较大的估计误差.为了补偿时延, 本文采用局部拟合方法估计图像雅可比矩阵, 以获得当前时刻更准确的图像雅可比矩阵估值, 并可对图像预补偿. 本文以无标定的移动机器人和视觉传感器为实验对象,仿真和实验表明该方法改善了系统的动态性能, 减小了稳态误差,从而验证带有时延补偿的图像雅可比矩阵估计方法的可行性和有效性.     

Feedback control for robotic manipulator with an uncertain Jacobian matrix

In most applications of robots, a desired path for the end‐effector is usually specified in task space such as Cartesian space. One way to move the robot along this path is to solve the inverse kinematics problem to generate the desired angles in joint space. However, it is a very time consuming task to solve the inverse kinematics problem. Furthermore, in the presence of uncertainty in kinematics, it is impossible to derive the desired joint angle from the desired end‐effector path and the Jacobian matrix of the mapping from joint space to task space. In this article, a feedback control law using an uncertain Jacobian matrix is proposed for setpoint control of robots. Sufficient conditions for the bound of the estimated Jacobian matrix and stability conditions for the feedback gains are presented to guarantee the stability and passivity of the robots. A gravity regressor with an uncertain Jacobian matrix is also proposed for gravitational force compensation when the gravitational force is uncertain. Simulation results are presented to illustrate the performance of the proposed controllers. ©1999 John Wiley & Sons, Inc.     

基于分层运动分解的飞行机械臂视觉伺服控制

飞行机械臂系统具有主动作业能力, 通过搭载视觉传感器感知周围环境, 系统的自主能力将进一步提高.然而, 考虑到无人机的欠驱动和整个系统的非线性特性, 飞行机械臂系统的视觉伺服控制仍然是一项具有挑战性的工作. 本文在充分考虑机械臂对无人机的力/力矩作用后, 提出了一种基于分层运动分解的飞行机械臂视觉伺服控制方案. 首先, 对飞行机械臂系统的运动学和动力学模型进行分析. 然后, 根据所得的相机运动学模型, 通过基于图像的视觉伺服控制获得相机的期望速度, 进而制定无人机和机械臂的速度分配策略. 在考虑机械臂运动时对无人机产生的力/力矩影响, 设计了底层的飞行控制器. 最后, 在与现有方法的仿真对比中可以看出, 所提方法具有良好的控制性能, 对图像特征点位置的不确定性及图像噪声也表现了较好的鲁棒性     

带时延补偿的图像雅可比矩阵在线估计方法

传统的在线估计图像雅可比矩阵的方法没有考虑时延因素,因此具有较大的估计误差。为了补偿时间延迟,提出一种新的带时延补偿的图像雅可比矩阵估计方法。该方法利用卡尔曼滤波估计特征点在图像空间中当前时刻的位置和速度,进而计算当前时刻较为准确的图像雅可比矩阵估计值。仿真和实验结果表明,该方法显著地提高了系统的性能,从而验证了提出的带时延补偿的图像雅可比矩阵在线估计方法的可行性和优越性。     

Uncalibrated visual servoing using the fundamental matrix

This paper describes a new algorithm for visual control of an uncalibrated 3 DOF joint system, using two weakly calibrated fixed cameras. The algorithm estimates on-line the Image Jacobian, a matrix which linearly relates joint velocity and image feature velocity. In our experiments we prove that by using the fundamental matrix, robustness of the estimation in the presence of noise is significantly increased with respect to already existing algorithms in specialized literature.     

基于无极卡尔曼滤波算法的雅可比矩阵估计

在基于图像的机器人视觉伺服中,采用在线估计图像雅可比的方法,不需事先知道系统的精确模型,可以避免复杂的系统标定过程。为了有效改善图像雅可比矩阵的在线估计精度,进而提高机器人的跟踪精度,针对机器人跟踪运动目标的应用背景,提出了利用无极卡尔曼滤波算法在线估计总雅可比矩阵。在二自由度的机器人视觉伺服仿真平台上,分别用卡尔曼滤波器（KF）、粒子滤波器（PF）和无极卡尔曼滤波器（UKF）三种算法进行总雅可比矩阵的在线估计。实验结果证明,使用UKF算法的跟踪精度优于其他两种算法,时间耗费仅次于KF算法。     

A visual-inspection system using a self-organizing map

Visual inspections by hand often cause bottlenecks in production processes in industries. Therefore, it is desirable to be mechanized and automated. In order to satisfy these requirements, we apply image recognition using a self-organizing map (SOM) to visual inspection equipment. The SOM maps high-dimensional input data onto a low-dimensional (typically two-dimensional) space. Through the mapping, the data are automatically clustered based on their similarity. Any unknown data which are input onto the self-organized map are also mapped onto it according to their similarity. The categories of the unknown data are thus recognized based on their positions on the map. The reason we use a SOM for inspections is that users can then know the similarity distribution of all data at a glance on the map, and understand the mechanism of the recognition visually. We have developed a visual inspection system using a SOM, and have evaluated it using actual product images. We have obtained high recognition accuracies of 98% and 96% for one- and two-inspection-point tests, respectively, for a real industrial product.     

图象雅可比矩阵的在线Kalman滤波估计

通过分析现有图象雅可比矩阵的在线辨识方法,提出一种新的辨识思路.将雅可比矩阵的在线估计转化为系统的状态观测,并设计了相应的Kalman-Bucy滤波估计算法.以双目立体视觉反馈下的运动目标跟踪任务为例,通过仿真和实验说明了所提出方法的有效性.     

Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system

The present research deals with the cell formation problem (CFP) of cellular manufacturing system which is a NP-hard problem thus, the development of optimum machine-part cell formation algorithms has always been the primary attraction in the design of cellular manufacturing system. In this proposed work, the self-organizing map (SOM) approach has been used which is able to project data from a high-dimensional space to a low-dimensional space so it is considered a visualized approach for explaining a complicated CFP data set. However, for a large data set with a high dimensionality, a traditional flat SOM seems difficult to further explain the concepts inside the clusters. We propose one such possible solution for a large CFP data set by using the SOM in a hierarchical manner known as growing hierarchical self-organizing map (GHSOM). In the present work, the two novel contributions using GHSOM are: the choice of optimum architecture through the minimum pattern units extracted at layer 1 for the respective threshold values and selection. Furthermore, the experimental results clearly indicated that the machine-part visual clustering using GHSOM can be successfully applied in identifying a cohesive set of part family that is processed by a machine group. Computational experience specifically with the proposed GHSOM algorithm, on a set of 15 CFP problems from the literature, has shown that it performs remarkably well. The GHSOM algorithm obtained solutions that are at least as good as the ones found the literature. For 75% of the cell formation problems, the GHSOM algorithm improved the goodness of cell formation through GTE performance measure using SOM as well as best one from the literature, in some cases by as much as more than 12.81% (GTE). Thus, comparing the results of the experiment in this paper with the SOM and GHSOM using the paired t-test it has been revealed that the GHSOM approach performed better than the SOM approach so far the group technology efficiency (GTE) measures of performance of the goodness of cell formation is concerned.     

基于扩展雅可比矩阵的冗余液压驱动四足机器人运动控制

针对冗余液压驱动四足机器人运动学逆解问题,提出一种基于扩展雅可比矩阵的冗余液压驱动四足机器人运动控制方法.该方法既能解决冗余自由度带来的逆解多解问题,还能使机器人足端入地角度满足摩擦锥要求避免足端滑动.首先,规划机器人足端轨迹得到机器人足端速度,在分析机器人足端入地角度对机器人运动性能影响的基础上,结合机器人腿部结构几...     

Dimensional synthesis of a 3-DOF parallel manipulator based on dimensionally homogeneous Jacobian matrix

A study of dimensional synthesis of a 3-DOF parallel manipulator with coupling of translation and rotation is carried out.The architecture of the manipulator is composed of a moving platform attached to a fixed base through three identical PRS(prismatic-revolute-spherical) serial limbs,whose unique topology leads to the physical unit inconsistency of the conventional Jacobian matrix and the emergence of the parasitic motion.Then this paper introduces a kinetostatic performance index of the manipulator based...     

Bi-clustering continuous data with self-organizing map

In this paper, we present a new SOM-based bi-clustering approach for continuous data. This approach is called Bi-SOM (for Bi-clustering based on Self-Organizing Map). The main goal of bi-clustering aims to simultaneously group the rows and columns of a given data matrix. In addition, we propose in this work to deal with some issues related to this task: (1) the topological visualization of bi-clusters with respect to their neighborhood relation, (2) the optimization of these bi-clusters in macro-blocks and (3) the dimensionality reduction by eliminating noise blocks, iteratively. Finally, experiments are given over several data sets for validating our approach in comparison with other bi-clustering methods.     

基于相机角度自适应的视觉伺服

针对视觉伺服中传统相机视野域有限和全景相机图像处理的计算量过大,提出了基于图像的相机角度自适应视觉伺服算法,根据不同相机坐标系之间的变换,计算相机旋转的变换矩阵.用极坐标系表示此旋转变换矩阵,计算出需要调整的相应角度.由于该方法极大地缩小了输入图像的尺寸,从而降低了视觉伺服中图像处理的数据量,提高了系统的实时性.实验仿真结果表明了该方法的有效性.     

采用预测控制的龙门架式冗余自由度机械臂轨迹规划算法

为解决近年来工业常用的复杂结构大型机械臂的运动控制问题,本文以龙门架式机械臂为研究对象,首先结合材料力学分析,对在双臂荷载下的横梁挠度公式进行推导,进而对机械臂系统建立精确的运动学模型.为了便于后期算法设计,又在此基础上进行了降阶、线性化等模型简化工作.针对这一具有关节限位等约束条件和冗余自由度的多输入多输出高维复杂系统,提出了一种基于预测控制的轨迹规划算法.该算法根据模型中不同关节的运动范围以及定位精度等特性,划定其不同的运动优先级,通过在优化问题中对各个关节控制量设置不同的权重,来解决冗余问题.同时通过实时更新与滚动优化,很好的保证了控制的及时陛.仿真结果表明了本文所推导的挠度公式的准确性和所提出算法的有效性,不仅很好地解决了冗余问题,而且充分利用不同关节的运动特性,针对不同类型的跟踪目标值给出相适应的轨迹规划方案.     

Extraction of rice-planted area using a self-organizing feature map

We introduce a neural network of self-organizing feature map (SOM) to classify remote-sensing data, including microwave and optical sensors, for the estimation of areas of planted rice. This method is an unsupervised neural network which has the capability of nonlinear discrimination, and the classification function is determined by learning. The satellite data are observed before and after rice planting in 1999. Three sets of RADARSAT and one set of SPOT/HRV data were used in Higashi–Hiroshima, Japan. The RADARSAT image has only one band of data and it is difficult to extract the rice-planted area. However, the SAR back-scattering intensity in a rice-planted area decreases from April to May and increases from May to June. Therefore, three RADARSAT images from April to June were used in this study. The SOM classification was applied the RADARSAT and SPOT data to evaluate the rice-planted area estimation. It is shown that the SOM is useful for the classification of satellite data.     

Load estimation for microgrid planning based on a self-organizing map methodology

This study presents a novel load estimation method for isolated communities that do not receive energy or only receive it for a limited time each day. These profiles have been used to determine the installed capacity of generating units for microgrid electrification projects. The social characteristics and lifestyles of isolated communities differ from those in urban areas; therefore, the load profiles of microgrids are sensitive to minor variations in generation and/or consumption. The proposed methodology for obtaining the residential profiles is based on clustering algorithms such as k-means, a self-organizing map (SOM) or others. In this work, SOM clustering is considered because it allows a better interpretation of results that can be contrasted with social aspects. The proposed methodology includes the following components. First, the inputs are processed based on surveys of residents that live in each socio-economic level of housing and the community. Second, family types are clustered using an SOM, from which relevant information is derived that distinguishes one family from another. Third, the load profiles of each cluster are selected from a database. Additionally, social aspects and relevant energy supply information from communities with similar characteristics are used to generate the required database. The SOM for the clustering of families of the community with available energy measurements is used as an initial guess for the clustering of the families in the community with unknown energy measurements.The methodology is applied and tested in the community of El Romeral, Chile, where a microgrid will be installed. The SOM technique compares favorably with a benchmark method that uses the average load profile of a community; furthermore, the SOM clustering algorithm for the methodology is favorably compared with the k-means algorithm because the results obtained by SOM are consistent with the social aspects.