6-6 SPS 6自由度Stewart并行机构位姿正解单解存

在获得了6-66自由度Stewart 并行机构位姿正解 的基础上,进一步提出了6-66自由度Stewart 并行机构位姿正解单解存在的条件．数字 模拟结果显示了该条件的适用性．     

基于DSP的Stewart平台控制系统研制

六自由度Stewart平台具有多通道、实时性高、运算量大等特点,为提高平台的控制精度,对六自由度平台位姿参数正解法进行改进,将平台定为动平面三点空间坐标,并将二次方程转化为线性方程,对产生的奇异解进行方程补偿,降低了运算次数并提高了效率。设计了基于DSP的控制系统。系统由DSP控制板、直流电机、Stewart平台和上位机软件构成。上位机软件由LabView开发,通过串口进行上下位机的通信。介绍了平台控制系统的原理、硬件和软件的设计与实现。实验表明,该控制系统具有良好的稳定性和较高精度的定位控制能力。     

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．     

6-DOF并联机器人位置正解的实用解法

对6-DOF并联机器人的位置正解进行了研究和分析,通过位置反解的求解思路来解位置正解的问题。将上下平台统一在一个坐标系下。按照空间两点间距离计算公式,以6个杆的伸长值为已知量,位姿参数为未知量,建立关于6个杆的参数方程。通过迭代法求得位姿参数。特点之一是未知量个数少,计算精度高;另一特点是从实现的角度来阐述,实用性强。通过实验验证该思路满足即时控制的要求。     

并联机器人位姿正解优化算法及其仿真

选取3-6结构并联机器人为研究模型,根据构型间的约束关系,建立机构的位姿正解的求解模型,并采用改进粒子群算法进行求解,将复杂的位姿正解问题转化为多元非线性方程的寻优过程。为提高求解精度,利用混沌序列的不可预测性与无序性以及在一定范围内不重复遍历所有状态的特性,提出一种基于混沌序列调整惯性权重的改进粒子群算法,将其用于求解位姿正解的计算。计算实例表明,该算法能求解出全部的位姿正解,且相较于标准粒子群算法能达到更高的收敛精度。最后采用Solid Works和Adams进行联合仿真,验证了这种优化算法的可行性。     

基于神经网络的并联3自由度机器人位置正解

并联机器人位置正解是机器人运动学中难点问题之一，常规求解方法比较复杂且难度较大，通常需要对大量的非线性方程组进行推导计算且得到的解不唯一。该文提出了一种将人工神经网络用于并联机器人位置正解求解的通用方法，并结合实际机构对并联3自由度机器人进行了具体求解。通过对神经网络拓扑结构的设计以及选取有效的学习算法并用大量的位置反解数据对神经网络进行训练，获得了用于求解位置正解的神经网络模型，该网络可以实现位置正解问题的求解计算，从而避免了复杂的推导和演算。计算机仿真与实验结果表明了该方法的有效性与可行性。     

一种TECA转运车的并联举升机构

为实现ITER(国际热核聚变实验堆计划)部件转运车TECA(Transfer Equipment CAsk)与托卡马克真空室或热室窗口的自动对接,提出了基于并联举升机构实现TECA的位姿调整的方法.设计了一种用于核环境下TECA转运车位姿调整的Stewart型并联举升机构,分析了该举升机构及其运动特征,建立了该机构的运动学模型,给出了逆运动学解析过程,并通过牛顿迭代法得到正运动学数值解.最后,采取双闭环的控制方式,以提高对接精度和速度.现场实验中该并联举升机构能够在大尺度范围下完成4.25 m×1.3 m×1.75 m的TECA转运车与真空室窗口的精确对接任务,并且能够达到0.88 mm以内的重复定位精度.结果表明该方法在求解平台位姿参数过程中具有速度快、精度高等优点.     

并联机器人视觉盲区末端位姿检测方法

为解决并联机器人末端执行器受机构支路遮挡造成的双目视觉盲区末端位姿错误检测问题,提出一种运动学正解结合混合优化RBF神经网络(RBFNN)误差补偿的视觉盲区末端位姿检测方法。首先在非视觉盲区采集RBFNN训练样本,其中运动学正解为输入样本,运动学正解和视觉检测位姿的差值为输出样本;然后进行训练,并采用GWO(Grey Wolf Optimization)算法和LM(Levenberg-Marquardt)算法混合优化权值;最后将训练好的网络用于视觉盲区,通过对运动学正解进行误差补偿以提高末端位姿检测精度。实验结果表明,与未补偿的检测方法相比,混合优化RBFNN补偿后的末端位姿检测方法,其末端位姿分量x,y,z,γ的误差平均绝对值分别降低了54.4%、67.7%、54.7%和52.9%,误差标准差分别降低了52.9%、62.8%、51.9%和58.8%,验证了所提方法的有效性。     

基于蚁群算法的并联机器人误差补偿方法

提出一种利用蚊群算法补偿Stewart并联机器人位姿误差的方法.基于闭环矢量法建立Stewart并联机器人位姿误差模趔,通过6个驱动杆的长度误差和铰链误差得到并联机器人的位姿误差.在位姿误差模型的基础上,利用基于网格划分策略的连续蚁群算法,通过信息素更新指导蚂蚁反复搜索,对驱动杆杆长误差进行寻优,最终补偿Stewart...     

Elman神经网络的网络流量预测

研究网络流量准确预测问题,由于网络流量变化具有非线性、突变性,传统网络流量预测是建立在线性模型的基础上,无法准确描述网络流量变化规律,导致预测精度低.为了提高网络流量的预测精度,提出一种Elman神经网络的网络流量预测模型.根据Elman神经网络良好的时变性捕捉能力和非线性预测能力对网络流量变化规律进行建模和预测.实验结果表明,模型具有良好的预测效果,相对于传统ARIMA模型、BP神经网络模型,Elman神经网络模型预测精度更高,误差更小,说明了改进的优化方法对网络流量预测是有效和可行的.     

Implementing the homotopy continuation method in a hybrid approach to solve the kinematics problem of spatial parallel robots

In this paper, first the application of homotopy continuation method (HCM) in numerically solving kinematics problem of spatial parallel manipulators is investigated. Using the HCM the forward kinematics problem (F-Kin) of a six degrees of freedom (DOFs) 6–3 Stewart platform and the inverse kinematics problem (I-Kin) of a 3-DOF 3-PSP robot are solved. The governing equations of the kinematics problems of the robots are developed and embedded in the homotopy continuation function. The HCM is utilized in order to solve the nonlinear system of equations derived from the kinematics analysis of the robots. Then, to represent the real case application an initial guess far from the correct answer is selected. It is shown that, comparing with the Newton–Raphson method (NRM), the F-Kin calculation time for the Stewart robot is decreased by 43%. Therefore, using the HCM a hybrid method is suggested to solve the F-Kin of the Stewart robot. Furthermore, the HCM, as an innovative method, relieves other downsides of the conventional numerical methods, including a proper initial guess requirement as well as the problems of convergence.     

Spherically actuated platform manipulator

This article presents the inverse and forward pose and rate kinematics solutions for a novel 6‐DOF platform manipulator, actuated by two base‐mounted spherical actuators. The moving platform is connected to the fixed base by two identical spherical‐prismatic‐universal serial chain legs. The S‐joint is active, and the remaining two joints in each chain are passive. An analytical solution is presented for the inverse pose problems, a semi‐analytical solution is presented for the rate problems, and the numerical Newton–Raphson technique is employed to solve the forward pose problem. Unfortunately, the passive joint variables cannot be ignored in the kinematics solutions as they can for the Gough–Stewart platform. Examples are presented and hardware has been built, using two Rosheim Omni‐Wrists on loan from NASA as the spherical actuators. © 2001 John Wiley & Sons, Inc.     

A study of neural network based inverse kinematics solution for a three-joint robot

A neural network based inverse kinematics solution of a robotic manipulator is presented in this paper. Inverse kinematics problem is generally more complex for robotic manipulators. Many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. In this study, a three-joint robotic manipulator simulation software, developed in our previous studies, is used. Firstly, we have generated many initial and final points in the work volume of the robotic manipulator by using cubic trajectory planning. Then, all of the angles according to the real-world coordinates (x, y, z) are recorded in a file named as training set of neural network. Lastly, we have used a designed neural network to solve the inverse kinematics problem. The designed neural network has given the correct angles according to the given (x, y, z) cartesian coordinates. The online working feature of neural network makes it very successful and popular in this solution.     

On the dynamic model and kinematic analysis of a class of Stewart platforms

In this paper, a dynamic model for a class of Stewart platform (six degrees of freedom parallel link robotic manipulators) is derived by using tensor representation. A set of six Lagrange's equations are obtained. The kinematics analysis for a class of Stewart platform is conducted and a sixteenth order polynomial equation corresponding to the forward kinematic solution of the Stewart platform is obtained, which gives all possible global solutions of a manipulator configuration for a given set of six leg lengths.     

Method for kinematic calibration of stewart platforms

A Stewart platform is a six degrees of freedom parallel manipulator composed of six variable-length legs connecting a fixed base to a movable plate. Like all parallel manipulators, Stewart platforms offer high force/torque capability and high structural rigidity in exchange for small workspace and reduced dexterity. Because the solution for parallel manipulators' forward kinematics is in general much harder than their inverse kinematics, a typical control strategy for such manipulators is to specify the plate's pose in world coordinates and then solve the individual leg lengths. The accuracy of the robot critically depends on accurate knowledge of the device's kinematic parameters. This article focuses on the accuracy improvement of Stewart platforms by means of calibration. Calibration of Stewart platforms consists of construction of a kinematic model, measurement of the position and orientation of the platform in a reference coordinate frame, identification of the kinematic parameters, and accuracy compensation. A measurement procedure proposed in this article allows a great simplification of the kinematic identification. The idea is to keep the length of the particular leg, whose parameters are to be identified, fixed while the other legs change their lengths during the measurement phase. By that, redundant parameters can be eliminated systematically in the identification phase. The method also shows the estimation of each leg's parameters separately because the measurement equations are fully decoupled, which results in a drastical reduction of the computational effort in the parameter identification. Simulation results assess the performance of the proposed approach. © 1993 John Wiley & Sons, Inc.     

A neural-network committee machine approach to the inverse kinematics problem solution of robotic manipulators

In robotics, inverse kinematics problem solution is a fundamental problem in robotics. Many traditional inverse kinematics problem solutions, such as the geometric, iterative, and algebraic approaches, are inadequate for redundant robots. Recently, much attention has been focused on a neural-network-based inverse kinematics problem solution in robotics. However, the result obtained from the neural network requires to be improved for some sensitive tasks. In this paper, a neural-network committee machine (NNCM) was designed to solve the inverse kinematics of a 6-DOF redundant robotic manipulator to improve the precision of the solution. Ten neural networks (NN) were designed to obtain a committee machine to solve the inverse kinematics problem using separately prepared data set since a neural network can give better result than other ones. The data sets for the neural-network training were prepared using prepared simulation software including robot kinematics model. The solution of each neural network was evaluated using direct kinematics equation of the robot to select the best one. As a result, the committee machine implementation increased the performance of the learning.     

Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach

In this paper, a fusion approach to determine inverse kinematics solutions of a six degree of freedom serial robot is proposed. The proposed approach makes use of radial basis function neural network for prediction of incremental joint angles which in turn are transformed into absolute joint angles with the assistance of forward kinematics relations. In this approach, forward kinematics relations of robot are used to obtain the data for training of neural network as well to estimate the deviation of predicted inverse kinematics solution from the desired solution. The effectiveness of the fusion process is shown by comparing the inverse kinematics solutions obtained for an end-effector of industrial robot moving along a specified path with the solutions obtained from conventional neural network approaches as well as iterative technique. The prominent features of the fusion process include the accurate prediction of inverse kinematics solutions with less computational time apart from the generation of training data for neural network with forward kinematics relations of the robot.     

A fast,robust solution to the Stewart platform forward kinematics

The Stewart platform is a six degree-of-freedom fully-in-parallel linkage well-suited to robotic tasks where structural rigidity and high small motion bandwidth are required. In this article we describe an approach for computing the forward kinematics of this device that is both fast and robust. Our solution is based on the simultaneous solution of three constraint equations using a Newton-Raphson scheme. A well-known property of Newton-Raphson is its tendency to fail when the constraint equations become poorly conditioned, and the main contribution of this article is the development of two algorithms for overcoming this limitation and hence for providing robustness. Certain other matters, such as the singular configurations of the Stewart platform and its assembly modes, are touched upon. © 1996 John Wiley & Sons, Inc.     

Neural Network Solution for Forward Kinematics Problem of Cable Robots

Forward kinematics problem of cable robots is very difficult to solve the same as that of parallel robots and in the contrary to the serial manipulators’. This problem is almost impossible to solve analytically because of the nonlinearity and complexity of the robot’s kinematic equations. Numerical methods are the most common solutions for this problem of the parallel and cable robots. But, convergency of these methods is the drawback of using them. In this paper, neural network approach is used to solve the forward kinematics problem of an exemplary 3D cable robot. This problem is solved in the typical workspace of the robot. The neural network used in this paper is of the MLP type and a back propagation procedure is utilized to train the network. A simulation study is performed and the results show the advantages of this method in enhancement of convergency together with very small modeling errors.     

Solution of forward kinematics in Stewart platform using six rotary sensors on joints of three legs

A novel method is proposed for real-time solution of direct kinematics problem of Stewart platform (SP) using six measurements on three legs’ joints consisting of the rotations of three legs in two directions. After the application of the method on a laboratory sample SP, it is observed that the method is preferable to the conventional method that uses the length measurements of all six legs, in the grounds of industrial applicability. It is due to simpler implementation, less expense, easier maintenance, and stress-free assembly. The algorithms of both forward and inverse kinematics are fully derived based on geometric relationships between the platform states and the measurement data. The sensitivity to the measurement errors is analyzed theoretically and is applied through a computer simulation to several configurations of the sample SP which are uniformly distributed in the workspace. The variances of measurement errors for those configurations are compared between the conventional and proposed methods and it is observed that: the proposed method operates more accurate in position measurement especially in lateral movements. Additionally, the proposed method is not too sensitive to direction of movement and geometry of the SP.