未校正摄像机的非完整机器人的自适应镇定

针对基于未校准顶置摄像机的非完整移动机器人视觉伺服镇定问题,首先从标准机器人运动学模型以及视觉空间与工作空间的转换得到视觉平面上的机器人运动学模型,而后根据视觉空间的运动学的速度误差以及视觉空间的机器人的动力学模型设计了一个自适应控制器,而且控制器具有鲁棒性,控制器中的鲁棒项函数用以抑制动力学的扰动,摄像机估计值用以估计未知的摄像机参数,动力学的惯性参数估计值用以消除动力学参数的不确定性.控制系统的稳定性以及参数估计值的有界性由李雅普诺夫定理证明.仿真结果用于说明控制律的有效性.     

6自由度串联机器人D-H模型参数辨识及标定

为了提高串联机器人的末端绝对精度,本文首先采用轴线测量法识别机器人D-H参数模型,继而将D-H参数转化为最小完整连续运动学(CMMK)模型参数,并进行非线性优化,以解决D-H参数模型奇异和冗余带来的非线性优化不易收敛问题,最后,将优化后的CMMK模型参数转化为工业标准的D-H模型参数,再经过补偿后将其作为设计模型以获得更高精度的定位.通过在MOTOMAN-MH80机器人上进行试验,该方法能确实有效地识别机器人的杆件参数,未标定前机器人的位置误差只能达到2 mm左右,而标定后降至0.7 mm左右,精度提高了将近70%.本文方法通过轴线测量获取机器人模型参数初值,避免了对机器人进行理论建模的过程,与运动学回路法相比,具有较高的通用性;采用最小完整连续运动学模型进行标定,能有效解决D-H模型奇异性、非连续、不易收敛到正确值的问题.     

考虑结构变形的机器人运动学标定及补偿

针对一种3P3R型串联机器人,建立了参考零位模型与DH(Denavit-Hartenberg)模型的混合运动学模型,将直线运动部分与旋转运动部分分开建模,能够更好地描述机器人不同机械结构的几何关系,在此基础上提出了结合几何辨识和参数辨识的两步标定方法.然后,结合机器人的机械结构特点,分析了机器人在操作大型零件过程中的结构变形,并提出了考虑结构变形的运动学补偿模型.最后,使用激光跟踪仪完成了机器人标定实验,通过对比空载和加载情况下的定位误差,验证了运动学标定和补偿的效果.结果表明,混合运动模型采用两步参数辨识能够在空载情况下取得较高的标定精度,而运动学补偿模型则能够在加载情况下对运动学进行较好的变形误差补偿.     

医用机器人运动学参数最优化设计

提出了一种实用的医用机器人运动学参数误差的优化补偿方法．采用D-H方法建立起机器人连杆坐标系．在运动学分析和模型变换的基础上，运用数值优化技术建立了机器人运动学参数的误差方程，实现了运动学参数的优化设计，有效提高了机器人的重复定位精度．以仿真和实验验证的方式对优化结果进行了分析．     

非线性回归机器人运动参数估计方法

针对机器人运动参数估计精度、实时性等问题,提出一种非线性回归的机器人运动参数估计方法。建立了机器人的非线性回归运动参数模型,并利用微分方法对运动参数方程进行转化,通过递推最小二乘算法与卡尔曼滤波器估计出机器人运动参数与消除误差。讨论了机器人沿墙壁、向墙壁、原地圆周运动三种特殊情况的运动参数估计。实验验证了本文方法的可行性与有效性,并与迭代最近点(ICP)算法进行了对比分析。     

工业码垛机器人运动学仿真

码垛机器人的运动学分析是实现码垛控制的前提和基础.针对码垛机器人的结构特点,运用空间几何方法对码垛机器人的运动学特性进行理论分析,建立驱动关节与末端执行器的映射关系.利用INVENTOR和ADAMS软件建立了码垛机器人参数化虚拟样机模型,进行运动学仿真.同时,对码垛机器人码垛轨迹仿真优化.实验结果证明,新型工业码垛机器人具有良好的运动学特性,符合现代码垛作业的要求.新提出的机器人仿真分析方法,对进一步提高码垛机器人技术的设计水平,具有促进和借鉴作用.     

基于工作空间的PUMA机器人臂长参数优化

本文以PUMA机器人为例,提出一种基于工作空间的机器人臂长优化方法.首先分析PUMA机器人的运动学,基于运动学通过数值法和图解法相结合的方法得到工作空间的包络曲线,然后采用解析法建立基于工作空间的臂长优化模型,最后采用遗传算法求解.结果表明:上述方法能够实现基于工作空间的机器人臂长参数优化,比试凑法和影响系数法得到更优的结果.     

新型蛇形机器人蜿蜒运动的动力学分析

为提高蛇形机器人执行各种运动的能力,研制了新型蛇形机器人系统．重点研究了该蛇形机器人的动力学．建立了机器人的运动学模型,并根据运动学模型提出了控制蛇形机器人蜿蜒运动的复合运动控制方法．用拉格朗日方法建立动力学模型,对不同参数下蛇形机器人的关节力矩特性和摩擦力特性进行了分析比较,为蛇形机器人的有效运动提供了理论依据．     

基于局部指数积的模块化机器人运动学参数标定

针对模块化可重构机器人系统展开基于局部指数积法的运动学参数标定研究,提出一种基于子装配体的模块化机器人标定方法.首先,采用旋量的指数形式对子装配体进行数学描述,建立子装配体的运动学模型.然后,采用局部指数积方法,建立基于子装配体、包含关节约束条件的模块化机器人实际运动学模型.通过对运动学模型取微分,根据指数映射微分公式的显式表达式,给出模块化机器人末端位置误差与子装配体的关节旋量误差、末端子装配体的局部位置误差之间映射关系的显式表达.最后,以一套模块化可重构机械臂系统为试验平台,采用激光跟踪仪为测量设备进行试验.试验结果表明标定过程能够收敛到稳定值,经参数标定后用于试验的6自由度模块化机械臂定位误差模的平均值降低了近95％,最大值降低了近92％.     

连续型同心管机器人正运动学快速求解方法

针对可在受限环境中灵活运动的连续型同心管机器人传统正运动学方法计算时间较长,不利于机器人实时运行的问题,本文提出了基于机器人几何学的同心管机器人正运动学快速求解方法,能够在精度损失有限的情况下,提高正运动学模型的计算效率.先根据Cosserat杆模型对同心管机器人进行建模,再利用李代数理论建立了机器人空间位置和曲率的关系式,并结合提出的正运动学方法,对机器人进行了基于逆运动学的开环控制实验.最后通过3管机器人的仿真和实物实验验证了本文所提方法的快速性和有效性.     

移动机器人滑动参数定界及鲁棒镇定控制

本文针对带运动学参数不确定性的野外轮式移动机器人模型的在线辨识、定界和点镇定控制问题展开了研究.考虑了移动机器人二维平面运动过程中所存在的滑动效应和自身几何参数未知等不确定性,并将其建模为运动学模型中所包含的未知时变参数.通过引入基于有界误差假设的非线性集员滤波方法,对移动机器人运动学模型中存在的不确定性参数进行了辨识和定界.在此基础上结合backstepping控制思想和Lyapunov分析方法解决了移动机器人的鲁棒镇定问题,在存在滑动参数干扰的情况下实现了移动机器人的全局指数收敛点镇定控制,提高了整体控制系统的稳定性和鲁棒性.仿真结果证明了本方法的有效性和鲁棒性.     

Estimation model for kinematic calibration of manipulators with a parallel structure

This article provides an estimation model for calibrating the kinematics of manipulators with a parallel geometrical structure. Parameter estimation for serial link manipulators is well developed, but fail for most structures with parallel actuators, because the forward kinematics is usually not analytically available for these. We extend parameter estimation to such parallel structures by developing an estimation method where errors in kinematical parameters are linearly related to errors in the tool pose, expressed through the inverse kinematics, which is usually well known. The method is based on the work done to calibrate the MultiCraft robot. This robot has five linear actuators built in parallel around a passive serial arm, thus making up a two-layered parallel-serial manipulator, and the unique MultiCraft construction is reviewed. Due to the passive serial arm, for this robot conventional serial calibration must be combined with estimation of the parameters in the parallel actuator structure. The developed kinematic calibration method is verified through simulations with realistic data and real robot kinematics, taking the MultiCraft manipulator as the case. © 1994 John Wiley & Sons, Inc.     

基于神经网络的不确定移动机器人鲁棒自适应跟踪控制

针对含运动学未知参数以及动力学模型不确定的非完整轮式移动机器人轨迹跟踪问题,基于Radical Basis Function(径向基函数)神经网络,提出了一种鲁棒自适应控制器.首先,考虑移动机器人运动学参数未知的情况,提出了一种含自适应参数的运动学控制器,用以补偿参数不确定性导致的系统误差;其次,利用神经网络控制技术,对于机器人在移动中动力学模型不确定问题,提出了一种具有鲁棒性的动力学控制器,使得移动机器人可以在不知道具体动力学模型的情况下跟踪到目标轨迹;最后利用Lyapunov稳定性理论证明了整个系统的稳定性.通过数值仿真验证了所设计的控制器的可行性.     

A novel kinematic parameters calibration method for industrial robot based on Levenberg-Marquardt and Differential Evolution hybrid algorithm

The poor absolute positioning accuracy of industrial robots is the main obstacle for its further application in precision grinding of complex surfaces, such as blisk, blade, etc. Based on the established kinematic error model of a typical industrial robot FANUC M710ic/50, a novel kinematic parameters calibration method is proposed in this paper to improve the absolute positioning accuracy of robot. The pre-identification of the kinematic parameter deviations of robot was achieved by using the Levenberg-Marquardt algorithm. Subsequently, these identified suboptimal values of parameter deviations were defined as central values of the components of initial individuals to complete accurate identification by using Differential Evolution algorithm. The above two steps, which were regarded as the core of this Levenberg-Marquardt and Differential Evolution hybrid algorithm, were used to obtain the preferable values for kinematic parameters of the robot. On this basis, the experimental investigations of kinematic parameters calibration were conducted by using a laser tracker and numerical simulation method. The results revealed that the robot positioning error decreased from 0.994 mm, initial positioning error measured by laser tracker, to 0.262 mm after calibration with this proposed hybrid algorithm. The absolute positioning accuracy has increased by 40.86% than that of the Levenberg-Marquardt algorithm, increased by 40.31% than that of the Differential Evolution algorithm, and increased by 25.14% than that of the Simulated Annealing algorithm. This work shows that the proposed kinematic parameters calibration method has a significant improvement on the absolute positioning accuracy of industrial robot.     

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.     

Kinematic analysis and error modeling of TAU parallel robot

The TAU robot presents a new configuration of parallel robots with three degrees of freedom. This robotic configuration is well adapted to perform with a high precision and high stiffness within a large working range compared with a serial robot. It has the advantages of both parallel robots and serial robots. In this paper, the kinematic modeling and error modeling are established with all errors considered using Jacobian matrix method for the robot. Meanwhile, a very effective Jacobian approximation method is introduced to calculate the forward kinematic problem instead of Newton–Raphson method. It denotes that a closed form solution can be obtained instead of a numerical solution. A full size Jacobian matrix is used in carrying out error analysis, error budget, and model parameter estimation and identification. Simulation results indicate that both Jacobian matrix and Jacobian approximation method are correct and with a level of accuracy of micron meters. ADAMS's simulation results are used in verifying the established models.     

Visual servoing of mobile robots for posture stabilization: from theory to experiments

On the basis of the kinematic model of a unicycle mobile robot in polar coordinates, an adaptive visual servoing strategy is proposed to regulate the mobile robot to its desired pose. By regarding the unknown depth as model uncertainty, the system error vector can be chosen as measurable signals that are reconstructed by a motion estimation technique. Then, an adaptive controller is carefully designed along with a parameter updating mechanism to compensate for the unknown depth information online. On the basis of Lyapunov techniques and LaSalle's invariance principle, rigorous stability analysis is conducted. Because the control law is elegantly designed on the basis of the polar‐coordinate‐based representation of error dynamics, the consequent maneuver behavior is natural, and the resulting path is short. Experimental results are provided to verify the performance of the proposed approach. Copyright © 2013 John Wiley & Sons, Ltd.     

带有未知参数和有界干扰的移动机器人轨迹跟踪控

针对模型参数未知和存在有界干扰的非完整移动机器人的轨迹跟踪控制问题,本文提出了一种鲁棒自适应轨迹跟踪控制器方法.非完整移动机器人的控制难点在于它的运动学系统是欠驱动的.针对这一难点,本文利用横截函数的思想,引入新的辅助控制器,使得非完整移动机器人系统不再是一个欠驱动系统,缩减了控制器设计的难度,进而利用非线性自适应算法和参数映射方法构造李雅谱诺夫函数.通过李雅普诺夫方法设计控制器和参数自适应器,从而使得非完整移动机器人的跟随误差任意小,即可以任意小的误差来跟随任意给定的参考轨迹.仿真结果证明了方法的有效性.     

模块化机器人的惯性参数辨识研究

提出了一种基于六维力／力矩传感器的模块化机器人惯性参数辨识的方法。首先，通过Newton-Euler方程建立模块化机器人的动力学方程,然后利用基座力旋量平衡原理建立辨识模型对动力学方程中的未知参数进行辨识，最后以德国AMTEC公司生产的PowerCube模块化机器人实体对这种方法进行了实验验证。     

模块化机器人的惯性参数辨识研究

提出了一种基于六维力/力矩传感器的模块化机器人惯性参数辨识的方法。首先,通过Newton-Euler方程建立模块化机器人的动力学方程,然后利用基座力旋量平衡原理建立辨识模型对动力学方程中的未知参数进行辨识,最后以德国AMTEC公司生产的PowerCube模块化机器人实体对这种方法进行了实验验证。