六轴工业机器人在线误差补偿方法的研究

提出一种机器人动态误差在线补偿方法,用以提高机器人的轨迹精度。在机器人参数误差得到离线补偿之后,基于ANFIS构建自适应模糊神经网络轨迹控制器,建立在线补偿系统控制模型,通过ADAMS与Matlab的协同仿真对系统仿真分析;仿真结果验证了所提出方法的可行性。在自主研发制造的六轴工业机器人的平台上,借助于API激光跟踪仪对机器人末端跟踪测量,进行实验分析;实验结果验证了该方法的有效性。     

提高机器人扫描加工系统加工精度的方法

将机器人三维扫描系统应用到工业加工中,建立了机器人在线测量加工系统.利用已知半径的标准球体作为参照工具,提出一种基于几何约束的非线性优化方法.在线精确地标定了便携式三维扫描系统和机器人的位姿关系,提高了测量精度.同时,提出一种使用虚拟刀具工具中心点和预补偿机器人系统误差的方法,提高了机器人的加工精度.对吉他的边缘进行扫描和加工的实验结果表明:该系统具有稳定、高精度、易于自动化等优点.     

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。     

双足机器人预观控制的ZMP补偿步行模式研究

针对双足机器人的稳定行走,提出了一种预观控制的零力距点（ZMP）补偿步行模式在线生成方法。利用实际ZMP与目标ZMP之间的未来误差信息,基于预观控制计算机器人行走过程中质心的补偿量,事先调整质心轨迹来改变步态。最终使实际ZMP更好地跟踪目标值。12自由度的双足机器人动力学仿真验证了所提出方法的有效性,而且机器人能在一定程度不平整地面上实现稳定行走。     

管道弯制CAD/CAM系统的设计与实现

对管道弯制涉及的问题进行了分析,描述了管道弯制CAD/CAM系统整体框架,提出系统模块划分方案;给出了由管道XYZ坐标到YBC坐标的转换算法,结合管道回弹和延伸特性,建立了回弹延伸补偿算法,对弯管数据进行了修正补偿,并生成精确下料尺寸;根据工艺条件和碰撞因素,分别从可弯制性初步分析和仿真分析两个层次对管道的可弯制性进行了分析;利用Visual C 编程工具和OpenGL图形库对系统进行了实现.目前系统已成功投入运行.     

一种基于改进傅里叶变换的回弹补偿算法研究*

针对回弹规律比较复杂的冲压件中高精度的要求,提出了一种基于改进傅里叶变换的回弹闭环控制系统模型。首先,从产品模型出发利用有限元分析、成形工艺参数优化等方法进行初始模具设计;然后对模具及冲压产品进行测量,评价回弹误差;最后通过两个迭代循环来基本消除回弹误差,完成最后模具修正补偿。实验证明,利用该模型可以有效地完成对模具形状修正,实现了复杂冲压件的回弹补偿。     

基于双目视觉动态跟踪的机器人标定

采用双目视觉动态跟踪技术对自主研发的工业机器人进行运动学标定。区别于以往机器人运动学标定中复杂模型计算,在此利用双目视觉动态跟踪系统的静态测量和动态跟踪等优势特性来跟踪测量机器人的连杆参数误差,结合机器人控制系统开放性特点,运用提出的动态标定原理对机器人实施连杆参数测量、辨识、修正及补偿。实验表明,通过参数反馈补偿,自主研发的机器人的定位误差明显降低,且该方法易于实现,为机器人精度研究提供了可靠依据。     

NURBS曲面重构方法在板料模面修型中的应用

给出汽车覆盖件成形过程中处理回弹影响的NURBS曲面几何补偿算法。该算法基于NURBS曲面重构技术,依据有限元网格模型的回弹数值仿真结果对原本用NURBS曲面表达的模具型面进行反向位移补偿修整,使修整后的模具型面仍然保持NURBS曲面表达。并且这些曲面在光滑性、精度等方面都满足工程实际要求的品质。     

径向基函数网络在变压边力反演中的应用

变压边力策略是在冲压件成型中控制回弹的一种有效措施。综合运用径向基函数（RBF）神经网络和三维回弹模拟技术开展了由冲压件成形质量反求变压边力的研究, 同时研究了径向基函数神经网络的动态结构设计问题,提出一种基于泛化的径向基函数神经网络的动态结构设计方法DYNSDRBF,编制了相应的计算程序。DYNSDRBF方法在变压边力反演神经网络设计中的应用结果表明,运用该方法设计的神经网络具有较好的计算精度,可有效地提高冲压件的成形质量。     

基于激光干涉的大角度高精度在线测角方法研究

由于红外探测器尺寸限制,大多数红外遥感相机仍采用扫描系统来扩大探测视场。随着探测精度要求的提高,对扫描系统的测角精度要求也越来越高,因此,高精度扫描系统测角精度决定了遥感器的性能。基于某红外遥感相机扫描系统测角技术要求,对扫描系统测角精度的在线检测方法进行研究。扫描系统最大扫描角度为±5.8°,测角精度1"的。根据对国内外高精度测角设备的调研,确定一种具备大角度、高精度在线测角能力的激光干涉仪——SIOS SP-TR2000激光干涉仪对扫描系统转动角度进行检测。SP-TR2000激光干涉仪主要是用于高精度测距,开发用于高精度测角使用,因此采用雷尼绍激光干涉仪+RT300 (KUNZ)转台作为检测系统对SP-TR2000激光干涉仪测角精度进行检定,检定后SP-TR2000激光干涉仪系统测角精度为0.245″,满足扫描系统1″测角精度需求。对基于SP-TR2000激光干涉仪的在线静态及动态测角方案进行阐述,并根据误差分析可知在线测试系统测角误差为0.247″,表明基于SIOS SP-TR2000激光干涉仪的大角度高精度在线测角方法可行。     

Assessment of sheet-metal bending requirements using neural networks

The springback behaviour of a sheet-metal is dependent on the properties of the metal and the bending conditions, namely the thickness of the sheet-metal, geometry of the tooling and the amount of force used for bending. Sheet-metal component manufacturing often requires near zero springback angle to obtain the correct shape of the product. An attempt has been made to model the non-linear relation between properties of the metal, the springback angle, geometry of the tooling and the bending force applied. Multilayer perceptron neural networks with a backpropagation learning algorithm were used to model the bending process. One set of data from bending experiments in a laboratory environment was used to train the networks. The networks were tested with the remaining set of experimental results. Then, the neural networks were used to predict the forces required for a number of bending experiments to achieve a zero springback angle. Validation of the neural network predictions was performed by trying to apply the predicted amounts of bending force in the physical experiments. The springback angles achieved were within ±1 degree, which is an acceptable range for the work. The research clearly demonstrates the applicability of neural networks to modelling the sheet-metal bending process.     

In-process angle measurement and control for flexible sheet metal manufacture

A new method of controlling springback in small-radius pressbrake bending operations has been developed. This method provides a more accurate bending process, necessary for the further development of precision, small-lot sheet metal assembly manufacture. The pursuit of this research has led to the development of an inexpensive, high-resolution, on-line angle sensor. In addition, a simplified analytic model of the bending process was developed to predict springback in terms of material and tooling geometry variables. Finally, a springback control system has been developed with demonstrated accuracy of one-third of a degree in right-angle bends for cold-rolled steel samples covering a range of material properties.     

Design of a sensor for on-line measurement of loaded bend angle for pressbrake control

Controlling springback in small radius pressbrake bending operations is motivated by the need to produce small lot parts of high quality. A new technique for springback control has been developed based on a simplified analytic model of material and tooling geometry variables. This technique requires the on-line measurement of loaded angle with a robust, high resolution optical sensor which is insensitive to material surface finish. The design of the sensor minimizes systematic error due to placement on the press bed. Loaded angle measurement accuracy of less than one arc minute is achieved. In combination with a press ram position control scheme, this sensor provides a more accurate bending process necessary for the further development of precision, small-lot sheet metal assembly manufacture.     

RV 减速器针齿受力分析及设计改进

RV(Rotor Vector)减速器是一种工业机器人关节高精密减速传动装置,通常由第一级渐开线行星传动机构和第二级摆线针轮传动机构组成。该研究工作基于针齿理论受力模型,参照针齿的实际磨损情况,对传统针齿受力分析方法进行了改进。通过改进方法分析发现针齿上存在明显的剪切力和弯曲应力分布,为了消除相关应力影响,该文采用短针齿取代传统长针齿。实验结果显示,新的设计方案不仅能够有效减少剪切力和消除弯曲应力,而且能够显著减少啮合时打滑现象的发生,具有更高的安全系数。     

pH传感器温度补偿模型研究

水质pH值在线监测对提高水产品健康养殖至关重要,本文将无线传感器网络与pH值传感器相集成,设计了pH值监测的无线传感器网络系统。为克服pH值测量过程中温度对测量结果的影响,提出了一种pH传感器温度补偿模型,该模型通过pH传感器和PT100铂电阻温度传感器分别测量溶液的pH值和温度,利用最小二乘法对pH值和电压进行线性分析,建立pH传感器的温度补偿模型。实验结果表明,该补偿模型测量精度高,能够实现较精确的pH值在线监测。     

Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components. To surmount the springback occurring in sheet metal forming processes, numerous studies have been performed to develop compensation methods. However, for most existing methods, the development cycle is still considerably time-consumptive and demands high computational or capital cost. In this paper, a novel theory-guided regularization method for training of deep neural networks (DNNs), implanted in a learning system, is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter, e.g., loading stroke, in sheet metal bending processes. By directly bridging the workpiece shape to the process parameter, issues concerning springback in the process design would be circumvented. The novel regularization method utilizes the well-recognized theories in material mechanics, Swift’s law, by penalizing divergence from this law throughout the network training process. The regularization is implemented by a multi-task learning network architecture, with the learning of extra tasks regularized during training. The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base, respectively. One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface. In this research, the neural models were found to outperform a traditional machine learning model, support vector regression model, in experiments with different amount of training data. Through a series of studies with varying conditions of training data structure and amount, workpiece material and applied bending processes, the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs, especially when only scarce and scattered experiment data are available for training which is often the case in practice. The theory-guided DNN could also be applicable to other sheet metal forming processes. It provides an alternative method for compensating springback with significantly shorter development cycle and less capital cost and computational requirement than traditional compensation methods in sheet metal forming industry.      

Rotary drying process modeling and online compensation

Rotary drying process modeling is a complex procedure due to the difficulties in measurement and estimation of kinetic model parameters. To solve the problem, a hybrid modeling method with online compensation is proposed in the present study. A mathematical model is built to describe the axial characteristics of rotary drying process. The drying rate which is the key parameter in the model is estimated by using a SVR-based fuzzy modeling approach, which can automatically extract fuzzy IF-THEN rules from support vectors. Laboratory experiments are conducted to obtain the drying rate sample data for the modeling purpose. In order to reduce the modeling errors for an industrial rotary dryer and improve the hybrid model prediction accuracy, an online matching coefficient is introduced, and a method based on improved online SVR is then applied for modeling error compensation. The experiment dada based modeling results have verified the effectiveness and demonstrated the accuracy and adaptability of the proposed hybrid modeling method.     

基于数据优先级的雷达目标跟踪偏差补偿方法

传统雷达目标跟踪偏差补偿方法雷达跟踪目标偏差补偿的精准程度较低,导致研究成果可靠性及稳定性较差。为了解决上述问题,基于数据优先级提出一种雷达目标跟踪偏差补偿方法,利用雷达极化测量目标信号,并建立三维空间坐标,引导操作数据,通过对相位控制偏差的校准操作实现对数据目标的精准测量,提升系统检验的准确性,根据数据优先级原则,对极化脉冲进行角度测量,选取适宜测量方案,设置雷达目标方向图,进行雷达目标超分辨成像,加强实验研究力度,综合考虑优化信息与操作条件,实现对雷达目标跟踪偏差补偿方法的研究。实验结果表明,基于数据优先级的雷达目标跟踪偏差补偿方法具备较高的雷达跟踪目标偏差补偿的精准程度,研究可靠性较高,稳定性有了显著提升。     

低速风洞测控自动化应用技术

大型低速风洞测控自动化的难点是大型动力系统的自动化和大型变角控制系统的自动化。文中介绍动力系统应用独创的给定值补偿调节法，解决大延时速压（风速）参数的高精度控制；姿态角控制系统应用独创的异步电动机步进化控制技术解决大型变角控制系统的自动化；测控系统应用异步通讯控制方式实现低速风洞测控全自动化和外挂物投放实验系统的自动化。该项技术为国内低速风洞中首创，已在国内低速风洞中逐步推广应用。     

Improve the robot calibration accuracy using a dynamic online fuzzy error mapping system.

Traditional robot calibration implements model and modeless methods. The compensation of position error in modeless method is to move the end-effector of robot to the target position in the workspace, and to find the position error of that target position by using a bilinear interpolation method based on the neighboring 4-point's errors around the target position. A camera or other measurement devices can be utilized to find or measure this position error, and compensate this error with the interpolation result. This paper provides a novel fuzzy interpolation method to improve the compensation accuracy obtained by using a bilinear interpolation method. A dynamic online fuzzy inference system is implemented to meet the needs of fast real-time control system and calibration environment. The simulated results show that the compensation accuracy can be greatly improved by using this fuzzy interpolation method compared with the bilinear interpolation method.