基于时间函数的并联六维力传感器量程设计

针对并联六维力传感器设计与制造过程中面临的解耦计算复杂、量程确定困难、实用性不强等问题,应用螺旋理论,推导了其受六维外力向测量分支反作用力的映射矩阵;基于工程实际,提出并建立了并联六维力传感器测量分支作用反力的时间函数模型;提出了一种在特定工作环境下测量分支量程的确定方法.以Stewart并联结构为例,针对曲面打磨作业,确定了其测量分支的量程,验证了时间函数模型的正确性和可行性.研究内容对提高并联结构六维力传感器的工程应用性有重要的实践价值.     

微形变腕力传感器

介绍了一种新型机器人六维腕力传感器.采用了激光干涉法测量由外力施加在弹性元件上产生的由微形变产生的微位移测量原理,使得弹性梁的设计空间更大.特别是传感器动态特性得到了提高.本文详细讨论了弹性元件的设计,包括通过有限元方法对这种弹性元件的定量计算.对于弹性元件形变的检测也是本文的讨论重点.通过激光干涉的方法,传感器的灵敏度得到提高,同时,本文还详细讨论了六维力分别施加于弹性元件时对激光干涉的作用.     

压力传感器测试系统的动态校准及特性分析

针对利用传感器静态灵敏度对动态信号进行测试时而引入的测量误差,使用激波管对压力传感器进行动态标定,对校准结果进行分析得到传感器的动态特性,并计算出其动态灵敏度。利用校准数据对传感器模型的阶数进行判别,为以后对测试结果进行修正和补偿提供理论依据;最后对动态校准过程中误差的来源进行分析并给出传感器动态灵敏度的不确定度。     

一种基于改进遗传RBF神经网络的传感器动态特性补偿算法

为了改善传感器的动态特性,减小系统测量误差,分析了传感器动态性能补偿的基本原理,提出了一种基于改进型遗传算法(IAGA)和RBF神经网络相结合的补偿算法,给出了用IAGA-RBF补偿算法建立的数学模型,并应用到瓦斯传感器的补偿环节.实验证明,该补偿算法具有响应速度快、计算精度高和工作频带宽的特点,多项动态特性指标都得到了较大的改善,能够有效地用于传感器的动态特性补偿.     

BP神经网络在测控设备自动调平系统中的应用

Ka频段车载测控设备在航天领域的快速发展，促进了电子水平传感器自动调平系统在车载测控设备的广泛应用。目前设备都是在跟踪目标前利用调平系统传感器反馈的信号进行调平，并计算出补偿误差，而在天线运转过程中重心变化又引入了一定的系统误差，降低了设备的外测精度。利用BP神经网络在天线实时跟踪目标时电子水平传感器反馈的信号预测误差，进行动态补偿。通过实际验证，证明可以减少设备测量误差和达到提高测量精度的目的。     

船用温湿度传感器的温度特性研究

引起温湿度传感器测量误差的主要因素是背景噪声和传感器放大电路温漂,为了降低温湿度传感器的测量误差,通过设置滤波器并采用信号叠加方法抑制背景噪声;采用数据拟合技术确定放大电路温度特性,建立温度补偿模型编写温度校正软件并对放大电路温漂进行补偿。试验结果表明,温度补偿后的温湿度传感器测量精度得到明显提高。     

冲击波测试系统中传感器动态补偿装置

在测量爆炸冲击波陡峭信号时,为了得出准确的超压峰值,必须解决由于传感器的带宽不够引起的测试数据剧烈震荡的问题。为此使用了模糊神经网络算法对传感器进行逆建模进而消除传感器动态误差,此方法能够准确快速地得出动态补偿装置的权值和系数。对压电传感器进行了建模并详细分析了补偿前后传感器的时域和频域动态特性。设计了以ARM为核心的动态补偿装置。实验证明将动态补偿装置应用于工程后能够减小传感器的动态误差并准确获得超压峰值。     

基于非线性拟合的自动气象站风向传感器校准控制研究

风向传感器是自动气象站的重要组成元件,由于其运行数据存在的噪声与缺失问题导致测量误差增加,因此设计基于非线性拟合的自动气象站风向传感器校准控制方法。分析自动气象站风向传感器的组成结构和工作原理,构建自动气象站等效模型。采用循环采集的方式获取风向传感器的实时运行数据,通过滤波、缺失补偿等步骤完成数据预处理。以数据预处理结合为基础,利用非线性拟合技术计算风向传感器误差,通过装设风向传感器校准控制器,完成风向传感器校准控制。实验结果表明,所设计方法与传统校准控制方法相比传感器的测量误差降低了0.759°,误差变化率有所下降,即证明了该方法的自动气象站风向传感器校准控制效果好。     

基于RBF神经网络的车辆牵引性能测试数据融合技术

针对目前车辆牵引性能测试中,牵引力传感器由于环境温度变化出现测量误差,影响了测试精度的问题,采用基于神经网络的数据融合技术对其进行补偿,不但避免了硬件补偿的复杂性,且提高了测试精度,取得较好的效果。实验证明:采用基于径向基函数(RBF)神经网络的数据融合技术补偿牵引力传感器中由于温度漂移而引起的误差较传统的补偿方式,具有较大的优势,有一定的实用性和推广价值。     

航天机器人用六维腕力传感器动态特性研究

本文主要论述航天机器人用六维腕力传感器动态特性的相关问题．讨论了传感 器动态性能的标准问题，给出了该传感器动态补偿器的设计方法，对实现中的一些问题进行 了初步探讨，最后给出实际的测试结果．值得一提的是，这是我们首次在传感器实际应用中 实现了动态补偿，为传感器动态性能的提高迈出了坚实的步伐．     

温度检测系统的非线性动态补偿研究

在温度测控系统中,对于温度传感器在测量时存在较大非线性动态偏移误差,提出基于参考模型的利用扩展卡尔曼滤波算法设计温度传感器的动态补偿的方法.用扩展卡尔曼滤波进行补偿器的参数辨识,从而得到比较精确的补偿器,因此温度传感器的动态偏移误差得到自动补偿.本文所设计的系统可以对温度进行有效的控制,且具有一定的鲁棒性和较好的响应速度.     

基于MAX1457的传感器智能化温度补偿的实现

MAX1 4 57是一种专用传感器信号处理器 ,它可以补偿硅压阻式传感器的温度误差和非线性误差 ,使传感器总的重复性精度达到 0 2 %以内。MAX1 4 57对传感器进行温度补偿时需要经过一系列操作步骤和参数的选择计算。本文分析了MAX1 4 57的工作原理和补偿过程 ,在此基础上开发的温度补偿软件 ,可以实现参数的自动计算 ,自动调整 ,补偿的每一个操作步骤均辅以向导式的提示。该系统的使用使得MAX1 4 57对传感器的温度补偿过程大为简化     

Measurement error analysis and accuracy enhancement of 2D vision system for robotic drilling

Robotic drilling for aircraft structures demands higher accuracy on industrial robots than their traditional applications. Positioning error measurement and compensation based on 2D vision system is a cost-effective way to improve the positioning accuracy in robotic drilling. In this paper, we first discuss the principle of error measurement and compensation with a 2D vision system for robotic drilling and the determination of tool center point of the vision system so that the Abbe errors are eliminated in the measurement process. Measurement errors due to nonideal measurement conditions, i.e. nonperpendicularity of the camera optical axis to the workpiece surface and incorrect object distance, are mathematically modeled and experimentally verified. A method utilizing four laser displacement sensors is proposed to ensure perpendicularity of the camera optical axis to the workpiece surface and correct object distance in the measurement process, and hence to achieve high accuracy in 2D vision-based measurement. Experiments performed on a robotic drilling system show that the 2D vision system can achieve an accuracy of approximately 0.1 mm with the proposed method.     

Time-of-flight sensor and color camera calibration for multi-view acquisition

This paper presents a multi-view acquisition system using multi-modal sensors, composed of time-of-flight (ToF) range sensors and color cameras. Our system captures the multiple pairs of color images and depth maps at multiple viewing directions. In order to ensure the acceptable accuracy of measurements, we compensate errors in sensor measurement and calibrate multi-modal devices. Upon manifold experiments and extensive analysis, we identify the major sources of systematic error in sensor measurement and construct an error model for compensation. As a result, we provide a practical solution for the real-time error compensation of depth measurement. Moreover, we implement the calibration scheme for multi-modal devices, unifying the spatial coordinate for multi-modal sensors. The main contribution of this work is to present the thorough analysis of systematic error in sensor measurement and therefore provide a reliable methodology for robust error compensation. The proposed system offers a real-time multi-modal sensor calibration method and thereby is applicable for the 3D reconstruction of dynamic scenes.     

Iterative-learning error compensation for autonomous parking of mobile manipulator in harsh industrial environment

In wide-area and multi-sites manufacturing scenarios, the mobile manipulator suffers from inadequate autonomous parking performance due to the harsh industrial environment. Instead of struggling to model various errors or calibrate multiple sensors, this paper resolves the above challenge by proposing an iterative-learning error compensation scheme that consists of offline pre-regulation and online compensation, which can improve the compensation efficiency and accommodate the error fluctuations caused by environmental fluctuations. Integrating an improved Monte-Carlo localization and eye-in-hand vision technique, an effective measurement system is firstly developed to accurately obtain the parking data without requiring superfluous facilities or cumbersome measurement. Then, after removing the data outliers utilizing the Grubbs test, offline pre-regulation is achieved to give a suitable initial value and increase the compensation convergence. To reduce the time-varying systematic errors and parking error fluctuations, online compensation is presented by offering an efficacious estimation of environmental fluctuations using fuzzy logic rules and providing an adaptive iterative-learning law. Finally, the feasibility and effectiveness of the presented compensation method are validated by extensive experiments implemented on a self-developed mobile manipulator.     

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.     

锰铜压力计的温度补偿模型研究

针对锰铜压力计灵敏度和零点温度漂移大且硬件补偿困难的问题,提出了一种有效的温度补偿模型.设计了超高压压力发生装置,并在此基础上改进了实验装置结构以减少数据误差,通过采用自动化数据采集系统,实现了多变量的同步采集并提高了采集速度和数据量.通过对大量数据的特征分析,推导出数学补偿模型.实验结果表明:该补偿模型能很好地反映锰铜压力计在温度、压力共同作用下的特征,使锰铜压力计在高温下也能较为准确地测量压力.     

主被动传感器自适应量测融合算法

针对观测平台和运动对象间的距离参数会对传感器随机测量误差带来影响的问题,提出了一种基于模糊距离阈值的主被动传感器量测融合算法。讨论了根据距离参数选择主被动融合跟踪模式的方法,采用指数函数和模糊处理技术,利用已有信息实时改变主、被动传感器在量测融合过程中所占的权重。仿真结果表明,当传感器和运动对象间的距离对随机测量误差的影响不能忽略时,基于模糊距离阈值的主被动传感器变权重融合算法和传统的固定权重融合算法相比更加稳定,能够充分发挥主、被动传感器间的互补特性。     

提高振弦式传感器测量精度的方法研究

振弦式传感器是水利工程和结构监测中应用较为广泛的设备之一.针对振弦式传感器输出信号微弱、放大检测中容易受到干扰而造成的测量精度下降问题,通过一个大坝高边坡安全监测工程应用实例的研究,从硬件的合理设计和软件的数据处理两方面详细分析和有效解决了振弦式传感器测量过程中的干扰问题,大大提高了振弦式传感器的测量精度.     

基于相对坐标的机器人末端位姿测量方法

为了解决机器人末端6维位姿难以测量的问题,提出了一种末端位姿的间接测量方法,并给出了适用条件．该方法只需测量末端平台6个目标点在单方向上的相对位移,便可得到机器人末端的6维位姿数据,而且可以消除测量工具的零值系统误差．分析了各几何误差对测量精度的影响,得到了误差影响矩阵以指导测量精度补偿．最后,以一台6自由度运动模拟器为例验证了该方法的有效性．