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。     

基于符号计算研究一类6-SPS并联机器人运动学正解问题

并联机器人运动学正解问题是一个重要而且难以解决的问题．本文利用符号计算工具,应用Ｄｉａｌｙｔｉｃ消元法,对一类６－ＳＰＳ并联机器人的运动学正解问题进行研究．得到一个变元多项式方程,给出了正解的解析解．在此基础上确定其工作空间,求出其解范围内全部实解．此种方法对于一般６－ＳＰＳ同样适用     

平行导路6-PSS并联机构运动学研究

文中提出了一种新型平行导路6-PSS结构并联机器人,介绍了这种机构的特点并对其运动学相关算法进行了深入研究,其中包括运动学反解、雅可比矩阵以及正解的求解方法,同时还与6-SPS结构并联机器人运动学相关的算法进行了比较.由于文中采用构造法计算雅可比矩阵,避免了以往对反解方程直接求导的计算方式,从而使雅可比矩阵的计算大为简化,而且也减少了正解的计算量、精度得到提高.实验结果证明,这些算法正确、实用.     

基于符号计算研究一类6-SPS并联机器人运动学正解问题

并联机器人运动学正解问题是一个重要而且难以解决 的问题．本文利用符号计算工具,应用Dialytic消元法,对一类6-SPS并联机器人的运动学 正 解问题进行研究．得到一个变元多项式方程,给出了正解的解析解. 在此基础上确定其工作 空间,求出其解范围内全部实解．此种方法对于一般6-SPS同样适用．     

一种基于角度传感器的6-UPS机构正向运动学分析方法

为了提高6自由度并联机构运动学正解的求解效率,增强求解方法的通用性,提出了一种6-UPS（universal-prismatic-spherical）机构的运动学正解算法．首先在6-UPS机构任意分支虎克铰处添加2个角度传感器,测量了虎克铰2个方位的旋转角度．再基于旋转矩阵构建12个方程,通过代数消元对其进行降次处理得到简易的一元二次方程组．最后,求得6-UPS机构的运动学正解．并通过具体数值算例验证所提方法,求出了确定的位置正解．该方法不仅降低了数据处理的难度,且能求得正解的唯一解,避免了并联机构正解存在多解的问题．     

3-TCT并联机器人运动学仿真

以Pro/E环境下建立的3-TCT并联机器人的模型为研究对象,在ADAMS/View模块下,对其添加约束和驱动后,进行运动学仿真.给定动甲台的位移,测量驱动杆杆长的变化曲线,利用ADAMS/Postprocessor模块对测量结果进行后处理,可得运动学逆解;以逆解得到的驱动杆杆长的变化曲线作为驱动,添加到驱动杆上可进行运动学正向求解.正逆解仿真结果对比表明,动平台正解时的运动轨迹和逆解时的运动轨迹完伞吻合.方法避免了大量的数学计算和计算机语言编程工作,通过CAE仿真软件实现了对并联机器人的运动学仿真,为并联机器人实际样机的调试和控制提供了一套有效的分析方法.     

基于旋转子矩阵正交的6R机器人运动学逆解研究

以机器人运动学方程为基础,基于变换矩阵中旋转子矩阵正交的特性,提出一种6R机器人运动学逆解算法.通过矢量运算,得到含有4个未知变量的4个常系数非线性方程,辅以其它方程,最终得到8组封闭解.通过对钱江一号焊接机器人的实例求解,验证了该算法解决逆解问题仅需0.087 ms,比传统的反变换法具有更优的实时性能;平面工况的运动仿真验证了该算法的有效性.该算法可应用于6R机器人的强实时在线控制系统.     

工业用并联机器人位置正解的精确求解

利用工业用6-DOF并联机器人位置反解易于获得这一特性,把较难的六自由度并联机器人位置正解问题转化为应用位置反解结果作为训练样本进行学习,从而实现机械手从关节变量空间到工作变量空间的非线性映射。文中以工业用6-DOF并联机器人为例,采用BP算法对其正解进行了求解,并利用Matlab进行仿真,结果表明该方法计算精度高,克服了数值解法的求解精度受初值影响较大的缺点。经过验证,BP网络的求解精度满足了控制精确的要求。     

基于运动学正解的三转动并联机构迭代补偿控制

首先建立三转动并联机构的运动学方程,研究该并联机构的运动学反解和运动学正解,然后建立该机 构的动力学方程并分析其动力学特性．基于该机构研制了能复现自行武器行进时的车体姿态变化过程的动态模拟器 试验台．该试验台利用运动学反解算法进行闭环控制,并采用基于运动学正解的开环迭代补偿控制算法修正姿态驱 动信号,使试验台的响应逐渐逼近期望的姿态指令．测试表明该系统时域波形复现精度优于95%,验证了迭代补偿 控制的有效性．     

用连续法求一类混合链机器人机构的全部位置正解

本文对一类混合链机器人机构的位置正解问题进行了研究。首先用Ｄ－Ｈ矩阵法建立机构上下平台之间的坐标转换关系；然后利用杆长约束条件得到机构的位置正解方程组；最后应用连续法直接求出其全部位置正解。本文方法形式简洁、求解方便，且具有一般性，可推广应用于其他类型混合链机器人机构及并联机器人机构的位置正解研究。     

基于遗传算法的机器人运动学逆解

在分析以往逆解方法的基础上，提出了用遗传算法求解机器人运动学逆解的方法，给出了用于优化求解的适合度函数，并提出用二次编码法提高解的精度．计算机模拟证明：该方法能快速收敛于全局最优解，能给出机器人的可能解，并能计算冗余度机器人的逆解．     

Reliability-based approach to the inverse kinematics solution of robots using Elman's networks

The solution of inverse kinematics problem of redundant manipulators is a fundamental problem in robot control. The inverse kinematics problem in robotics is the determination of joint angles for a desired cartesian position of the end effector. For the solution of this problem, many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. Furthermore, many neural network approaches have been done to this problem. But the neural network-based solutions are not much reliable due to the error at the end of learning. Therefore, a reliability-based neural network inverse kinematics solution approach has been presented, and applied to a six-degrees of freedom (dof) robot manipulator in this paper. The structure of the proposed method is based on using three networks designed parallel to minimize the error of the whole system. Elman network, which has a profound impact on the learning capability and performance of the network, is chosen and designed according to the proposed solution method. At the end of parallel implementation, the results of each network are evaluated using direct kinematics equations to obtain the network with best result.     

Parallel approaches to the solution of the assignment problem

This paper discusses efforts to develop parallel algorithms that can be used to solve large-scale assignment problems typical of military battle management systems. Attempts to parallellze the classical Hungarian method are explored. Drawing on experimental data, a regresslon model is derived that relates the Hungarian method's run time to a cubic function of the number of parallel processors used in the solution. Four parallel heuristics for solving the assignment problem are developed and analysed. Two of them perform well in a parallel environment. The first, based on Vagel's approximation, can be used to identify a feasible, near-optimal assignment. The second algorithm partitions the assignment problem into independent subproblems across the parallel array. The resulting solution, although infeasible in the strict definition of the assignment problem, is seen to be reasonably good and can be obtained very rapidly. Either of these two heuristics are of potential use in the stringent computing environment of a real-time resource management system.     

Higher Order Real-Time Approximations in Optimal Control of Multibody-Systems for Industrial Robots

The multibody system of an industrial robot leads to a mathematical model described by ordinary differential equations. Control functions have to be determined to calculate robot trajectories which are optimal due to a given performance index subject to additional constraints. In order to solve such optimal control problems, computationally expensive methods exist. These methods have no real-time capability, since perturbations during the motion of the robot require a recalculation of the optimal trajectory within a time frame much smaller than the operating-time of the robot. Hence, a robust numerical method based on the parametric sensitivity analysis of nonlinear optimization problems is suggested. Optimal control approximations of perturbed optimal solutions can be obtained in real-time by evaluating a first order Taylor expansion of the perturbed solution. Successive improvements to the constraints in the direction of the optimal perturbed solution lead to an admissible solution with a higher order approximation of the objective. The proposed numerical method is illustrated by the optimal control of an industrial robot with three degrees of freedom subject to deviations in the payload and initial values.     

基于集群的EBE-PCG算法在物探中的应用

采用有限元法进行电法勘探时,会产生大型稀疏线性方程组,如何提高方程组的求解效率成为物探研究的关键.提出了在Beowulf集群环境下,采用粗粒度EBE-PCG算法处理物探问题.在集群环境下,该算法具有机器间相互通信少,易于并行实现等优点.实验结果表明,采用EBE-PCG算法获得了良好的并行效果.     

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.     

平面三自由度并联冗余机器人的位置分析

由于机构的结构复杂 ,对并联机器人进行位置分析 ,尤其是并联冗余机器人 ,要比串联机器人复杂得多 .本文提出一种新的平面三自由度并联冗余机器人位置分析方法 ,运用这种方法进行了位置正解和位置反解分析 .对于位置正解 ,其中方程的解最多为 4 ,说明这种平面并联机构可以有 4种不同的位姿 .对于位置反解 ,可以有16组解 .最后用数值实例进行了验证 ,给出了计算结果 .本文所提出的方法也为求解其它并联冗余机器人提供了新的途径     

Hybrid moment/position control of a parallel robot

In this paper, a hybrid moment/position controller in task space is proposed for tasks involving a contact between a robot and its environment. We consider a contour-tracking task performed by a six DOF (Degrees Of Freedom) parallel robot. The task space dynamic model of the robot in contact with its environment, seen as a black box, is estimated by a MLP-NN (MultiLayer Perceptron Neural Network). The neural network non-linearity is treated using Taylor series expansion. An adaptation algorithm of the neural parameters resulting from a closed-loop stability analysis is proposed. The performance of the proposed controller is validated on the C5 parallel robot by considering two different environments: rigid and compliant.     

基于改进模拟退火算法的搬运机器人路径规划

针对传统搬运机器人路径规划方法易陷入局部最优解,以及缺乏对环境普遍适应性的问题。应用栅格法创建搬运机器人工作环境模型,以一种建立搜索禁忌表的改进贪心算法为基础,通过加入遗传算法中“优胜劣汰”的思想,重新定义了模拟退火系数和栅格系数,提出了一种可以解决贪心算法局部收敛问题的改进模拟退火算法。最后通过仿真和具体实物实验,验证了该算法具有的可行性以及对于不同环境的适应性,能够有效地提高搬运机器人路径规划的质量。