一种新型机器人自标定装置及其算法

针对传统基于几何约束的机器人自标定装置仅能对局部工作空间内的机器人位型进行标定测量的问题,提出了一种由安装于机器人末端的球心位置测量装置和可移动球杆组成的新型便携式机器人自标定装置,通过利用球面约束和距离约束,可在较大工作空间内对机器人进行标定测量,从而提高标定结果的可靠性．根据可移动球杆的单、双球布置方式,分别建立了基于向量差和距离差的2种机器人自标定模型及其算法．通过采用局部指数积公式并引入位置伴随变换矩阵,简化了2种自标定模型,从而降低了对运动学方程线性化的计算量．最后,对一种6自由度串联机器人进行了仿真实验,实验结果表明2种自标定算法均能够快速收敛,验证了2种算法的有效性和鲁棒性．     

图像雅可比矩阵伪逆估计在视觉伺服中的应用

在一般无标定视觉伺服算法的基础上，提出了一种基于递推最小二乘法估计图像雅可比矩阵伪逆的无标定视觉伺服算法．这种方法无需计算雅可比矩阵伪逆．针对固定目标，文中给出了比例控制算法的稳定性条件．针对运动目标，推导了基于图像雅可比矩阵伪逆估计的伪高斯—牛顿视觉伺服算法和信任区视觉伺服算法．最后,仿真验证了三种视觉伺服算法的有效性．     

基于雅可比预测的机器人无模型视觉伺服定位控制

针对未知相机标定及目标3D几何模型,研究机器人无模型视觉伺服定位方法.引入状态空间,建立机器人“视觉空间-运动空间”雅可比非线性映射的状态方程和观测方程,提出神经网络联合卡尔曼滤波雅可比预测算法,网络在线动态补偿系统近似误差与参数估计误差,实现最小均方差条件下的雅可比预测;以李雅普诺夫稳定性准则构建雅可比预测的无模型图像视觉伺服控制方案,避免了相机标定和目标建模.“眼在手”六自由度机器人定位比较实验表明,图像空间特征轨迹平滑稳定在相机视场中,笛卡尔空间机器人末端运动平稳,无震荡回退,定位精度在10个像素范围内.     

一种新的双环结构机器人无标定自抗扰视觉伺服控制方法

在研究基于自抗扰控制器的机器人无标定视觉伺服方法的基础上，提出了一种新的双环结构机器人无标定自抗扰视觉伺服控制方法．内环采用Kalman滤波算法进行图像雅可比矩阵的在线辨识，可较好地逼近真实模型；外环采用自抗扰控制器，利用非线性观测器实时估计系统相对于当前估计模型的总扰动，并在控制中加以动态补偿．针对六自由度工业机器人进行了二维运动目标的跟踪实验，实验结果表明了该方法的可行性和有效性．     

空间机器人连续运动轨迹控制建模和仿真研究

基于地面机器人常用的D-H 建模方法和空间机器人（SR）满足的动量守恒和角动量守恒原理,提出 了SR 连续运动轨迹控制建模算法．首先基于地面机器人雅可比矩阵的思想和D-H 法,建立了适用于空间机器人 的运动学模型;其次研究了空间机器人的广义雅可比矩阵计算及其连续运动轨迹控制的有效算法;最后通过计算 机仿真验证了所提出算法的有效性．本文基于D-H 方法的SR 概念模型和运动学模型可推广到包含旋转关节和平 移关节的树型结构链杆的任何SR．     

基于距离精度的机器人5参数位置误差模型

随着机器人离线编程技术的应用，对机器人的位置误差的要求提高了，但在应用传统的方法进行位置误差的标定和补偿时，要涉及到测量系统坐标系与机器人基础坐标系间的变换。由于这一过程很难精确完成，容易引入误差，本文利用距离精度的定义，建立了机器人的距离误差模型，该模型可以避免坐标转换带来的误差，此外，由于精确的几何模型对于机器人精度标定的提高有很大的影响，所以本文将针对传统DH参数的不足之处，采用修正的5参数的MDH模型作为机器人的运动学模型，最后本文对位置误差进行了补偿。     

三分支机器人协调操作及关节力矩优化

针对三分支机器人协调运动，采用分离影响系数法分离各个分支的雅可比矩阵和惯性矩阵，再重新组合成整个系统的雅可比矩阵和惯性矩阵，建立三分支机器人运动学和动力学方程．应用乘子罚函数方法，对三分支机器人基于最小关节驱动力矩优化设计,避免矩阵的奇异值分解，提高计算的稳定性，应用迭代方法，简化了问题的求解．     

摄像机参数标定实验的设计与实现

在基于位置的视觉伺服中，需要建立将二维图像信息转换为三维坐标信息的图像雅可比矩阵。为了获取高精度的雅可比矩阵本文设计了CCD摄像机参数标定的实验。首先介绍了摄像机标定原理，然后设计了标定实验，最后给出了标定的具体步骤以及实验结果。     

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。     

基于神经网络的冗余度TT-VGT机器人的运动学求解

应用BP神经网络对冗余度TT-VGT机器人的位姿正解进行训练学习,进而求解机器人 的位姿反解问题．根据网络模型求得机器人的一、二阶影响系数,应用神经网络求解雅可比 矩阵的伪逆．并对七重四面体的变几何桁架机器人进行了仿真计算．     

Kinematic calibration for collaborative robots on a mobile platform using motion capture system

For modern robotic applications that go beyond the typical industrial environment, absolute accuracy is one of the key properties that make this possible. There are several approaches in the literature to improve robot accuracy for a typical industrial robot mounted on a fixed frame. In contrast, there is no method to improve robot accuracy when the robot is mounted on a mobile base, which is typical for collaborative robots. Therefore, in this work, we proposed and analyzed two approaches to improve the absolute accuracy of the robot mounted on a mobile platform using an optical measurement system. The first approach is based on geometric operations used to calculate the rotation axes of each joint. This approach identifies all rotational axes, which allows the calculation of the Denavit–Hartenberg (DH) parameters and thus the complete kinematic model, including the position and orientation errors of the robot end-effector and the robot base. The second approach to parameter estimation is based on optimization using a set of joint positions and end-effector poses to find the optimal DH parameters. Since the robot is mounted on a mobile base that is not fixed, an optical measurement system was used to dynamically and simultaneously measure the position of the robot base and the end-effector. The performance of the two proposed methods was analyzed and validated on a 7-DoF Franka Emika Panda robot mounted on a mobile platform PAL Tiago-base. The results show a significant improvement in absolute accuracy for both proposed approaches. By using the proposed approach with the optical measurement system, we can easily automate the estimation of robot kinematic parameters with the aim of improving absolute accuracy, especially in applications that require high positioning accuracy.     

Combination of geometric and parametric approaches for kinematic identification of an industrial robot

Combination of geometric and parametric approaches of kinematic identification is proposed in this article. The experimental strategy is similar to that used in geometric approach wherein each axis of the robot is actuated one after the other. This adds clarity to experimental strategy, which becomes ambiguous while solely using a conventional parametric approach. Therefore it is easier to conduct experiments even if there are restrictions in workspace. The estimation was done using a parametric technique, but in a stage wise manner using a divide and conquer strategy. This allowed measurement of the robot accuracy after removing the errors arising due to the definition of base and end-effector frames. Additionally it is possible to visualize the reduction in errors during the estimation process. In addition to this, the Jacobian matrix that relates the pose errors to the correction in parameters is adapted during estimation using a damped least squares method depending upon the convergence of the parameters. Results were obtained after extensive experiments on industrial robots using three different measurement instruments namely laser tracker, monocular camera and multi-camera system. The proposed method performs better than the conventional approach which uses only geometric approach. Finally thanks to the new approach, it was possible to conduct experiments after dividing the workspace region into those with high and low levels of observability; which is not easy while using conventional approaches. It was also possible to perform identification in regions closer and farther away from the robot where there is deterioration of observability. The results show that the proposed method could reduce the errors in pose in a consistent manner, even when different measurement instruments were used, or when there was a deterioration in observability due to the choice of poses during identification.     

MOTOMAN-SV3XL机器人手眼视觉立体定位系统

分析了基于恒定旋转矩阵的机器人视觉立体定位方法原理,提出利用机器人控制命令参数直接获取恒定旋矩阵的方法,该方法大大简化了摄像机内外参数的标定过程。设计了MOTOMAN-SV3XL机器人手眼视觉控制系统,制了相应的控制软件,进行了大量的实验研究,并取得了良好的效果。     

A design of digital adaptive control systems for space robot manipulators using a transpose of the generalized Jacobian matrix

We propose a digital control method for space robot manipulators using the transpose of the generalized Jacobian matrix. The method, however, is developed on the supposition that all the physical parameters of the robot manipulator are known. Therefore, if the end-effector of the manipulator captures an object whose mass is unknown, the stability of the control system cannot be maintained because the physical parameters are changed. This article presents the adaptive control version.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

Indirect iterative learning control for a discrete visual servo without a camera-robot model.

This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.     

A direct algorithm for continuous path control of manipulators

This paper presents an algorithm for positioning and orientation of the hand for a redundant or non-redundant manipulator along a continuous path in space. This algorithm minimizes the distance between the actual position of the tip of the end-effector and the desired path. The algorithm does not use the Jacobian matrix for the inverse kinematics of the robot. It takes full advantage of the resolution of the joint drives, avoids singularity problems, and can be used for both redundant manipulators. The algorithm can be used in any situation where continuus motion of the end-effector is required in an open loop mode.     

Prediction Error Based Adaptive Jacobian Tracking of Robots With Uncertain Kinematics and Dynamics

In this note, we have proposed a prediction error based adaptive Jacobian controller to solve the problem of concurrent adaptation to both uncertain kinematics and dynamics. This controller is composed of a modified computed torque controller and two cushion floor least-square estimators, and in ideal case of perfect knowledge of the robot parameters it leads to linear and decoupled error dynamics. The kinematic and dynamic parameters adaptations are driven by prediction errors. Using input-output stability analysis, we show that the end-effector motion tracking errors converge asymptotically. We have also derived an alternative adaptive Jacobian controller that does not require the invertibility of the estimated inertia matrix. Simulation results are presented to show the performance of the proposed controllers.      

Singularity-robust decoupled control of dual-elbow manipulators

The ability of a robot manipulator to move inside its workspace is inhibited by the presence of joint limits and obstacles and by the existence of singular positions in the configuration space of the manipulator. Several kinematic control strategies have been proposed to ameliorate these problems and to control the motion of the manipulator inside its workspace. The common base of these strategies is the manipulability measure which has been used to: (i) avoid singularities at the task-planning level; and (ii) to develop a singularity-robust inverse Jacobian matrix for continuous kinematic control. In this paper, a singularity-robust resolved-rate control strategy is presented for decoupled robot geometries and implemented for the dual-elbow manipulator. The proposed approach exploits the decoupled geometry of the dual-elbow manipulator to control independently the shoulder and the arm subsystems, for any desired end-effector motion, thus incurring a significantly lower computational cost compared to existing schemes.     

Finding Unmanipulable Singularities in Parallel Mechanisms Using Jacobian Decomposition

A geometric method is presented to determine the unmanipulable singular configurations of a general class of parallel mechanisms. In unmanipulable singular configurations, the composite Jacobian matrix that maps active joints velocities to end-effector velocity loses rank, indicating the loss of a task degree-of-freedom. Finding unmanipulable configurations is difficult due to the complexity of the Jacobian matrix. The problem is greatly simplified by a novel decomposition of the matrix presented in this paper. The method is used to find singularities in several example parallel machines.