基于神经网络的机器人位姿逆解

本文运用神经网络求解机器人运动学位姿逆解，突破了文献局限于研究位置逆解的状况，首次实现自组织神经网络求解机器人姿态逆解。     

机器人逆标定方法研究

在分析传统机器人位姿标定方法的基础上，提出了一种新的机器人标定方法：基于神经网络的逆标定方法。这种标定方法把机器人实际位姿和相应的关节角误差分别作为前馈神经网络的输入和输出来训练网络，从而获得机器人任意位姿时的关节角误差值，通过修改关节值来提高机器人的位姿精度。这种标定方法把所有因素引起的误差均归结为关节角误差，无须求解机器人逆运动学方程，实现了误差的在线补偿。把标定结果与基于运动学模型的参数法的标定结果进行了比较分析。仿真和试验结果均证明了这种方法比传统方法标定效果更好，且更方便简单，避免了其他传统标定方法繁琐的建模及参数辨识过程。     

CMAC在仿人机器人逆运动学计算中的应用

本文采用关节角位移和末端位姿误差作为小脑模型神经网络(CMAC)的输入,根据仿人机器人的正运动学模型来调整CMAC的权值,使网络最终逼近仿人机器人的逆模型,从而得到末端位姿到各个关节角的映射关系,避免了传统解析方法面临的计算量大、解不唯一的问题。MATLAB仿真结果表明,利用CMAC对仿人机器人的逆运动学问题求解,可以保证机器人位姿较好地跟踪给定的参考轨迹,说明CMAC能够逼近仿人机器人的逆运动学模型。     

FGA优化RBF网络的机器人逆运动学求解研究

针对如何提高六自由度机器人逆运动学的求解精度问题,采用FGA对RBF神经网络的节点中心向量、基宽向量以及网络隐含层到输出层的权向量进行优化,并将其应用于六自由度机器人的逆运动学求解。以机器人工作空间的位姿矩阵作为预测网络的输入变量,以关节空间中的关节角度作为输出变量,构建机器人逆解RBF预测网络,然后选取样本对网络进行训练。最后对网络进行测试,仿真结果显示,优化后的网络预测精度高,泛化能力强。     

机器人动态位姿误差在工作空间的补偿算法

本文从机器人末端位姿的理论模型出发,结合实际得到的机器人末端位姿,应用最小二乘法理论,建立了在工作空间对机器人动态位姿误差进行补偿的有效方法.     

面向铣削任务的工业机器人刚度位姿优化

为提高机器人刚度性能,减小铣削加工误差,对搭载铣削执行器的6自由度机器人进行刚度优化.首先,运用虚功原理建立机器人刚度映射模型;其次,设计辨识实验获取关节刚度;再次,以铣削力椭圆平面的各向同性度为优化指标,运用遗传算法对机器人优化位姿进行求解;最后,对比分析机器人位姿优化前后的整体刚度,并进行机器人铣削试验验证位姿优化的有效性,铣削平面度可提升45％.该优化方法可指导串联型工业机器人对大型航天器舱体的铣削加工任务,提高加工质量.     

基于CMAC的双足步行机器人逆运动学控制

针对双足步行机器人(Biped Walking Robot)腿部逆运动学模型求解问题,采用一种基于CMAC神经网络的机器人逆运动学控制方法,设计CMAC神经网络控制系统.控制系统采用2个CMAC神经网络控制器分别用来逼近步行机器人支撑腿与摆动腿的逆模型,跟踪通过三维线性倒立摆模型生成的给定腰部轨迹.建立步行机器人正运动学模型来调整CMAC神经网络权值,实现了步行器人腿部逆运动学映射.仿真结果表明,CMAC神经网络控制系统可以在保证机器人位姿良好的情况下跟踪给定的参考轨迹.三维运动学仿真结果进一步验证了控制算法的有效性.     

结合MPGA-RBFNN的一般机器人逆运动学求解

针对一般机器人逆运动学求解过程中存在的求解速度慢、精度低的问题，将多种群遗传算法（multiple population genetic algorithm，MPGA）引入径向基函数神经网络（radial basis functions neural network，RBFNN），提出一种适用于一般机器人的高精度MPGA-RBFNN算法。该算法采用3层结构的RBFNN进行一般机器人逆运动学求解，结合一般机器人的正运动学模型，采用MPGA优化RBFNN的网络结构和连接权值的方法，同时应用混合编码和演化的方式，实现了从机器人工作空间位姿到关节角度的非线性映射，从而避免了复杂的公式推导并提高了求解速度。采用6R一般机器人作为实验平台进行实验，实验结果表明:MPGA-RBFNN算法不仅提高了一般机器人在逆运动学中的求解速度，而且MPGA-RBFNN算法的训练成功率和逆解的计算准确率也得到了提高。     

基于激光雷达的移动机器人位姿估计方法综述

位姿估计是移动机器人研究的一个核心问题．本文综述了国内外基于激光雷达的移 动机器人位姿估计的最新进展，并对各种方法进行分类、比较和分析，从中归纳出应用中值 得注意的问题和发展趋势．     

基于目标跟踪的并联机器人视觉位姿检测

建立一种基于视觉的并联机器人位姿检测系统框架,包括图像采集、图像处理、位姿检测、参数反馈4个部分。使用单目摄像头采集图像,以二自由度冗余机器人为控制对象,利用Haar特征提取对目标进行粗跟踪。进一步获得目标上特定的几个特征点,基于平行不变性原理,得到机器人末端操作器的实际位姿参数。通过求解机器人的逆运动学方程,得到电机的控制参数。实验和仿真验证了该系统的可行性。     

基于模糊神经网络的冗余度变几何桁架机器人自适应控制

本文提出了一种基于模糊神经网络（FNN）的机器人位置自适应控制方法．利用模糊 神经网络模型来辨识冗余度变几何桁架机器人的逆动力学模型,用常规反馈控制器完成外部 干扰的补偿和闭环控制．并以四重四面体变几何桁架机器人为例进行仿真计算,表明该控制 方法具有良好的轨迹跟踪精度和抗干扰能力．     

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.     

Multi-fingered robot hand optimal task force distribution: Neural inverse kinematics approach

Grasping and manipulation force distribution optimization of multi-fingered robotic hands can be formulated as a problem for minimizing an objective function subject to form-closure constraints, kinematics, and balance constraints of external force. In this paper we present a novel neural network for dexterous hand-grasping inverse kinematics mapping used in force optimization. The proposed optimization is shown to be globally convergent to the optimal grasping force. The approach followed here is to let an artificial neural network (ANN) learn the nonlinear inverse kinematics functional relating the hand joint positions and displacements to object displacement. This is done by considering the inverse hand Jacobian, in addition to the interaction between hand fingers and the object. The proposed neural-network approach has the advantages that the complexity for implementation is reduced, and the solution accuracy is increased, by avoiding the linearization of quadratic friction constraints. Simulation results show that the proposed neural network can achieve optimal grasping force.     

两变频调速电机系统的神经网络逆同步控制

针对以恒压频比工作方式的两台变频器+感应电机系统的特点,导出了两变频调速电机系统的统一数学模型,并证明该系统可逆.进一步采用静态神经网络加积分器构成的动态神经网络来构造该逆系统,并将神经网络逆系统与两变频调速电机系统相串联复合成由速度和张力子系统组成的伪线性系统,实现速度和张力的解耦.然后分别对速度和张力子系统设计线性闭环控制器从而实现对两变频调速电机系统的高性能控制.实验结果表明系统具有较好的动、静态性能和较强的抗负载扰动的能力,提出的神经网络逆同步控制方法为解决交流多电机系统解耦控制的难题提供了新思路.     

Application of artificial neural networks to solve problems of identification and determination of concentration of salts in multi-component water solutions by Raman spectra

In this paper, the results of elaboration and comparative analysis of approaches concerned with application of neural network algorithms for effective solution of problem of pattern recognition (inverse problem with discrete output) along with inverse problem with continuous output are presented. Consideration is carried out at the example of problem of identification and determination of concentrations of inorganic salts in multi-component water solutions by Raman spectra. The studied approach is concerned with solution of both problems (classification and determination of concentrations) using a single neural network trained on experimental or quasi-model data.     

Inverse Back Analysis Based on Evolutionary Neural Networks for Underground Engineering

In essence, back analysis is a process of system identification. Therefore, artificial neural networks represent a suitable solution methodology for this problem. To overcome the shortcomings of the neural networks and evolutionary neural networks, based on immunized evolutionary programming, a new evolutionary neural network whose architecture and connection weights simultaneously evolve is proposed. Using this new evolutionary neural network, a novel inverse back analysis for underground engineering is studied. Using a numerical example and a real engineering example, namely, an underground roadway of the Huainan coal mine in China, the accuracy of this inverse back analysis is verified. Moreover, the non-uniqueness of the solution generated by the inverse back analysis is analyzed. The results show that, using the back-calculated parameters, the computed displacements agree with the measured ones. Thus, the new inverse back analysis method is demonstrated to be a high-performance method for usage in underground engineering. Moreover, various other conclusions can be drawn: the training samples of the neural network should be collected from the results of the positive analysis by the finite element method and selected based on the orthogonal experimental design, and the precision of the back analysis using multiple parameters is worse than that using a single parameter.     

Finite-element neural networks for solving differential equations

The solution of partial differential equations (PDE) arises in a wide variety of engineering problems. Solutions to most practical problems use numerical analysis techniques such as finite-element or finite-difference methods. The drawbacks of these approaches include computational costs associated with the modeling of complex geometries. This paper proposes a finite-element neural network (FENN) obtained by embedding a finite-element model in a neural network architecture that enables fast and accurate solution of the forward problem. Results of applying the FENN to several simple electromagnetic forward and inverse problems are presented. Initial results indicate that the FENN performance as a forward model is comparable to that of the conventional finite-element method (FEM). The FENN can also be used in an iterative approach to solve inverse problems associated with the PDE. Results showing the ability of the FENN to solve the inverse problem given the measured signal are also presented. The parallel nature of the FENN also makes it an attractive solution for parallel implementation in hardware and software.     

Inverting feedforward neural networks using linear and nonlinearprogramming

The problem of inverting trained feedforward neural networks is to find the inputs which yield a given output. In general, this problem is an ill-posed problem. We present a method for dealing with the inverse problem by using mathematical programming techniques. The principal idea behind the method is to formulate the inverse problem as a nonlinear programming problem, a separable programming (SP) problem, or a linear programming problem according to the architectures of networks to be inverted or the types of network inversions to be computed. An important advantage of the method over the existing iterative inversion algorithm is that various designated network inversions of multilayer perceptrons and radial basis function neural networks can be obtained by solving the corresponding SP problems, which can be solved by a modified simplex method. We present several examples to demonstrate the proposed method and applications of network inversions to examine and improve the generalization performance of trained networks. The results show the effectiveness of the proposed method.     

微位移系统中压电陶瓷驱动器迟滞建模

针对超精密微位移系统中压电陶瓷驱动器的迟滞非线性问题,提出了一种基于遗传反向传播(BP)神经网络的压电陶瓷迟滞非线性建模方法.通过电涡流位移传感器获取压电陶瓷驱动器不同电压值下所对应的位移值;利用六次多项式拟合获得迟滞的数学模型,从而建立基于遗传BP神经网络的迟滞,模型.实验结果显示:该迟滞模型在神经网络测试下的最大误差为0.082 1 μm,平均绝对误差为0.0158 μm.表明,所建的迟滞模型能够较精确地反映出压电陶瓷驱动器的迟滞特性,同时为微位移控制系统设计提供了一定的理论基础.     

Adaptive methods for solving inverse problems in laser raman spectroscopy of multi-component solutions

This study provides comparative analysis of approaches connected with application of neural network based algorithms for efficient solution of pattern recognition problem (inverse problem with discrete output) combined with solution of inverse problem with continuous output. The analysis is performed at the example of the problem of identification and determination of concentrations of inorganic salts in multi-component aqueous solutions by Raman spectrum.