Tracking Galloping Profile of Transmission Lines Using Wireless Inertial Measurement Units

Galloping of power transmission lines might bring about huge damage such as massive power outage and collapse of the transmission towers. To realize forecast of the galloping and provide data for study on the galloping mechanism, this paper proposes an online monitoring system for tracking galloping profile of power transmission lines based on wireless inertial measurement units (WIMUs). The system is composed of three modules: wireless inertial measurement nodes, monitoring base station, and remote monitoring station. After detailing the hardware system, the corresponding software which positions and displays galloping profile of the transmission line in real-time is outlined. The feasibility of the proposed on-line monitoring system is demonstrated through a series of experiments at the State Grid Key Laboratory of Power Overhead Transmission Line Galloping (Zhengzhou, China) by taking into account different vibration patterns.

Research on Test Data Distribution of Strapdown Inertial Measurement Unit Based on Bayesian Method

Aiming at that the successive test data set of the strapdown inertial measurement unit is always small,a Bayesian method is used to study its statistical characteristics.Its prior and posterior distributions are set up by the method and the pretest,sample and population information.Some statistical inferences can be made based on the posterior distribution.It can reduce the statistical analysis error in the case of small sample set.

Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

Reliable and accurate calibration for camera,inertial measurement unit(IMU)and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment perception.However,traditional calibrations suffer inaccuracy and inconsistency.To address these problems,this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying framework.In our method,the spatial relationship is geometrically correlated between the sensing units and robotic arm.The decoupled estimations on rotation and translation could reduce the coupled errors during the optimization.Additionally,the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and consistently.The calibration has been evaluated on our developed platform.In the experiments,the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7°and 0.01 m,respectively.The comparisons with state-of-the-art results prove our calibration consistency,accuracy and effectiveness.

DVL/RPM Based Velocity Filter Aiding in the Underwater Vehicle Integrated Inertial Navigation System

The purpose of this paper is to design a DVL-RPM based VKF (Velocity Kalman Filter) design for a performance improvement underwater integrated navigation system. The integrated navigation sensor using DVL (Doppler Velocity Log) is widely used to improve the underwater navigation performance. However, the DVL’s range of measuring varied depending on the characteristics of sensor. So, if the sea gets too deep suddenly, it cannot measure the velocity. To complement such a weak point, the VKF was additionally designed, which was made of DVL, RPM (Revolve Per Minutes) of motor, and ES (Echo Sounder). The proposed approach relies on a VKF, augmented by an altitude from ES based switching architecture to yield robust performance, even when DVL exceeds the measurement range and the measured value is unable to be valid. The proposed approach relies on two parts: 1) indirect feedback navigation Kalman filter design, 2) VKF design. To evaluate the proposed method, we compare the VKF aided navigation system with PINS (Pure Inertial Navigation System) and conventional INS-DVL navigation system through simulation results. Simulations illustrate the effectiveness of the underwater navigation system assisted by the additional DVL-RPM based VKF in underwater environment.

Implementation and Validation of a Stride Length Estimation Algorithm,Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease

As low cost and highly portable sensors, inertial measurements units (IMU) have become increas-ingly used in gait analysis, embodying an efficient alternative to motion capture systems. Mean-while, being able to compute reliably accurate spatial gait parameters using few sensors remains a relatively complex problematic. Providing a clinical oriented solution, our study presents a gy-rometer and accelerometer based algorithm for stride length estimation. Compared to most of the numerous existing works where only an averaged stride length is computed from several IMU, or where the use of the magnetometer is incompatible with everyday use, our challenge here has been to extract each individual stride length in an easy-to-use algorithm requiring only one inertial sensor attached to the subject shank. Our results were validated on healthy subjects and patients suffering from Parkinson’s disease (PD). Estimated stride lengths were compared to GAITRite? walkway system data: the mean error over all the strides was less than 6% for healthy group and 10.3% for PD group. This method provides a reliable portable solution for monitoring the in-stantaneous stride length and opens the way to promising applications.

融合表面肌电和姿势信息的轮椅绩效评价方法

目的为客观评价轮椅的使用效益,使用表面肌电设备测试轮椅使用过程中的肌电信号,并融合姿势信号IMU来构建人机评价模型。方法分别对轮椅的折叠、刹车方式及行驶坡度进行试验来评估轮椅的使用绩效,通过对比用力肌群间的疲劳状况来判定较为舒适的轮椅折叠及刹车方式。实验要求被试者在执行轮椅任务时,分别使用两种折叠方式和三种刹车方式不同的轮椅进行实验,并在操作动作任务过程中采集sEMG和IMU信号,在实验任务结束后填写NASA-TLX量表。结果根据模型的评价指标对实验数据进行了比较与分析,横向收折式折叠和凹口式刹车(手刹位于前方)的轮椅疲劳度低,在3~4°坡度范围下轮椅使用者的受力最小,较为舒适,从而验证了模型在轮椅人机评价上的可行性,为优化轮椅设计提供参考。结论人机评价模型适用于评估产品绩效,同时提出的融合表面肌电和姿势信息的轮椅绩效评价方法具有较高的精度和准确性,能够有效地评估轮椅使用者的绩效水平。

基于PE-ANGO的MIMU现场标定方法

针对当前微惯性测量单元(MIMU)的现场标定方法存在标定步骤复杂、不利于非专业人员操作等问题,提出了一种基于先验知识增强的自适应北苍鹰优化(PE-ANGO)算法的MIMU现场标定方法。首先分析了MIMU涉及的误差并建立了加速度计和陀螺仪的目标函数,然后使用PE-ANGO算法求解目标函数并得到最优参数。为了使得标定工作易于现场操作,引入了一种用于传感器数据采集的手持MIMU来验证所提算法。仿真结果表明:PE-ANGO算法的标定精度相较于北苍鹰优化算法提高了一个数量级。实测实验表明:标定前后对俯仰角和横滚角累积误差的抑制效果分别提高了约89%和87%;与传统标定方法相比,对俯仰角和横滚角累积误差的抑制效果分别提高了71%和68%,验证了所提方法的有效性。

基于足间距信息辅助的行人三维惯性定位算法

针对行人惯性导航系统误差随时间累积致使定位精度严重下降的问题,提出了一种基于足间距信息辅助的行人三维惯性定位算法。该算法在零速修正算法的基础上,利用足部安装的超声波测距模块实时测量行人双足相对距离,构建了基于超声测距的足间距约束模型,通过随机森林算法实现行人运动模式识别,并针对上下楼梯场景,利用台阶高度和足间距信息进行高度解算,最终实现行人三维惯性定位。在实际路线上开展了三维定位实验,数据显示,所提算法平面闭环误差为总路程的0.64%,与零速修正算法相比下降了55.56%,高度误差为0.06 m,与零速修正和气压计联合算法相比下降了64.70%,能够实现导航误差在总路程的0.50%以内的三维定位。实验结果表明,所提算法具有良好的工程应用价值。

基于改进LSTM网络的无人机MEMS-IMU零偏在线标定方法

针对在卫星信号拒止、视觉系统退化场景中无人机MEMS-IMU零偏无法准确估计并补偿导致导航误差迅速发散的问题,提出一种基于改进长短时记忆(LSTM)网络的零偏在线标定方法。首先,为解决MEMS-IMU零偏数据非线性强、传统循环时间网络训练效果差的问题,设计序列到序列的LSTM神经网络结构,引入教师强迫机制,提高了网络特征学习能力。然后,在导航过程中使用训练后的网络对MEMS-IMU零偏在线标定,补偿后的IMU量测与视觉信息联合优化,保证了导航定位精度。实验结果表明,在纯惯性导航实验中,所提方法的绝对位置误差比传统LSTM方法减小了6.5%;在EUROC数据集下进行的视觉惯性组合导航实验中,所提方法的平均绝对位置误差比传统LSTM方法减小了15%。

Automated pavement horizontal curve measurement methods based on inertial measurement unit and 3D profiling data

Pavement horizontal curve is designed to serve as a transition between straight segments,and its presence may cause a series of driving-related safety issues to motorists and drivers.As is recognized that traditional methods for curve geometry investigation are time consuming,labor intensive,and inaccurate,this study attempts to develop a method that can automatically conduct horizontal curve identification and measurement at network level.The digital highway data vehicle(DHDV) was utilized for data collection,in which three Euler angles,driving speed,and acceleration of survey vehicle were measured with an inertial measurement unit(IMU).The 3D profiling data used for cross slope calibration was obtained with PaveVision3 D Ultra technology at 1 mm resolution.In this study,the curve identification was based on the variation of heading angle,and the curve radius was calculated with kinematic method,geometry method,and lateral acceleration method.In order to verify the accuracy of the three methods,the analysis of variance(ANOVA) test was applied by using the control variable of curve radius measured by field test.Based on the measured curve radius,a curve safety analysis model was used to predict the crash rates and safe driving speeds at horizontal curves.Finally,a case study on 4.35 km road segment demonstrated that the proposed method could efficiently conduct network level analysis.

Is clinically measured knee range of motion after total knee arthroplasty‘good enough?’:A feasibility study using wearable inertial measurement units to compare knee range of motion captured during physical therapy versus at home

Total knee arthroplasty is highly successful,in part due to range of motion(RoM)recovery.This is typically estimated goniometrically/visually by physical therapists(PTs)in the clinic,which is imprecise.Accordingly,a validated inertial measurement unit(IMU)method for capturing knee RoM was deployed assessing postoperative RoM both in and outside of the clinical setting.The study's objectives were to evaluate the feasibility of continuously capturing knee RoM pre-/post-op via IMUs,dividing data into PT/non-PT portions of each day,and comparing PT/non-PT metrics.We hypothesized IMU-based clinical knee RoM would differ from IMU-based knee RoM captured outside clinical settings.10 patients(3 M,69±13 years)completed informed consent documents following ethics board approval.A validated IMU method captured long duration(8–12 h/day,~50 days)knee RoM pre-/post-op.Post-op metrics were subdivided(PT versus non-PT).Clinical RoM and patient reported outcome measures were also captured.Compliance and clinical disruption were evaluated.ANOVA compared post-op PT and non-PT means and change scores.Maximum flexion during PT was less than outside PT.PT stance/swing RoM and activity level were greater than outside PT.No temporal variable differences were found PT versus non-PT.IMU RoM measurements capture richer information than clinical measures.Maximum PT flexion was likely less than non-PT due to the exercises completed(i.e.high passive RoM vs.low RoM gait).PT gait flexion likely exceed non-PT because of‘white coat effects’wherein patients are closely monitored clinically.This implies data captured clinically represents optimum performance whereas data captured non-clinically represents realistic performance.

基于激光诱导石墨烯的木制惯性测量单元

针对商用低精度惯性测量单元具有高成本、制造工艺复杂、废弃后污染环境、不能生物降解等缺点,提出一种低成本、可生物降解的木制惯性测量单元。该设计包含平衡振子和非平衡振子单元,分别用于测量3轴加速度和3轴角加速度。采用激光诱导石墨烯的工艺在木梁上制备应变传感器阵列,并形成多组惠斯顿电桥测量电路。结果表明:加速度方面,X轴灵敏度为0.006 m V/g,Y轴灵敏度为8.695×10^(-4)m V/g,Z轴灵敏度为0.200 m V/g;角加速度方面,X轴灵敏度为0.285 m V/(rad/s^(2)),绕Y轴旋转的灵敏度为0.305 m V/(rad/s^(2)),绕Z轴旋转的灵敏度为0.765 m V/(rad/s^(2))。与有限单元法仿真结果对比,实验测量误差在10%以内,且具有良好的重复测量精度。该惯性测量单元在木制船舶、木制载具、木制家具等方面具有潜在的应用前景。

基于局部摄影的单目视觉输电线路弧垂测量

定期监测输电线路中导线弧垂的大小对输电线路的稳定运行有重要意义。传统的弧垂测量方法操作复杂且耗时较长,应用单目视觉获取完整导线图像是一种便捷的弧垂测量方法,但是当输电线路的档距过大时,相机受视场角和分辨率的限制,无法拍摄完整的导线,难以测量弧垂。针对上述问题,设计了一种便捷可用的局部摄影测量方法,将IMU中的加速度计传感器与高分辨率工业单目相机结合,在已知少量输电线路参数的条件下,只需拍摄待测导线的左侧部分即可。获得单幅导线图像后,引入语义分割技术提取图像中的导线,以摄影测量模型为基础将导线的实际点与对应成像点建立联系,以此建立非线性方程组,经非线性求解恢复导线的三维形状,进而计算导线弧垂。弧垂的测量相对误差可以控制在5%以内,满足实际应用需求。

考虑设计姿态辅助IMU/ODO的轨道不平顺检测算法

采用GNSS定位的轨道几何状态测量仪在隧道、地铁等GNSS拒止环境中无法工作.由于即使铁路轨道发生变形,其仍然接近设计线形,实际轨道位置与其设计值的差异始终保持在一定范围内.本文结合铁路设计参数与惯导/里程计信息,提出一种考虑设计姿态辅助IMU/ODO的轨道不平顺检测算法,该方法将设计姿态与惯导解算姿态相结合进行卡尔曼滤波,并使用里程计速度进行航位推算.通过计算实验,分析了轨道设计姿态信息对轨道不平顺检测精度的提升作用.实验结果表明:铁路设计姿态信息能够显著提高轨道不平顺检测精度,所提方法较基于全站仪辅助的动态检测方法,在30 m弦轨道不平顺检测精度相当,且整体检测效率较高,可以满足日常轨道检测的需要.

面向三维多目标追踪的运动补偿优化方法

三维多目标追踪是自动驾驶系统中的关键模块之一,其结果的优劣主要取决于追踪模块中数据关联过程的准确度.现有的追踪方法大多从外观特征或运动特征出发计算两帧之间物体的相似度,而基于运动特征的方法通常根据当前帧和历史帧三维包围框之间的交并比(Intersection over Union,IoU)进行关联,然而这种方式在观测点物体自身运动时存在严重缺陷.在观测点物体自身进行运动时,观测到的两帧数据将处于不同的局部坐标系,导致无法使用运动模型准确预测已追踪物体在下一帧中的位置.本文针对上述问题,通过引入观测点自身的惯性测量单元(Inertial Measurement Unit,IMU)或全球定位系统(Global Positioning System,GPS)数据,在一帧数据到达之后计算当前帧局部坐标系与上一帧局部坐标系之间的旋转和平移关系,并对已追踪的物体状态按得到的坐标变换关系进行运动补偿,使其抵消因观测点自身运动造成的偏移量.这种运动补偿增强了追踪模块的数据关联环节,提高追踪时三维包围框的关联成功率,降低误关联数量,改善三维多目标追踪的精度.在相关追踪框架及KITTI数据集上的原型验证表明,所提的运动补偿优化方法实现了1%左右的精度提升.

基于IMU数据的应变解析技术及其应用

长输埋地油气管线在严重的地灾作用下会引发管道变形,如果变形的管段存在缺陷,将极易导致管道失效,造成严重的环境污染和经济损失。准确地获取管道的变形情况并进行安全评价能够为保障管道安全稳定运行发挥重要的作用。基于IMU检测数据,通过小波变换降噪和油气管道设计规范进行特征识别的应变解析算法,能够有效地识别管线上的热偎弯管、冷弯管、弹性敷设段和重点关注段。该算法计算的管段弯曲拉伸应变值将结合ExxonMobil提出的拉伸应变容量预测模型对环焊缝进行基于应变的安全评价。在基于应变设计的高钢级油气管道已在国内油气运输行业得到广泛应用的背景下,可以参考该评价方法开展高钢级管道环焊缝的安全评价。

高速铁路线路轨面坡度及竖曲线半径动态测量方法及应用研究

为揭示高速铁路线路纵断面参数变化,以及和轨道不平顺、路基大桥沉降关系,针对国内尚无轨道纵断面参数动态检测装备,提出基于惯组的轨面坡度及竖曲线半径动态测量方法。首先,建立转向架构架及惯性坐标系,并以重力加速度、水平加速度极小相关性为基准对齐两坐标系;其次,基于转向架构架纵向加速度解算轨面坡度,并补偿列车俯仰运动及与轨道平面纵向倾角误差;然后,以坡度测量值为基础解算竖曲线半径;最后,以高速综合检测列车为平台,在国内某新建高速铁路实车试验。结果表明:坡度和竖曲线曲率重复性偏差绝对均值分别为0.07‰、0.71×10^(-3)rad/km,极差分别为0.22‰、0.005 rad/km。

基于因子图的多传感器融合定位方法

针对移动机器人在室内环境中使用单一传感器或松耦合定位存在定位精度低和鲁棒性不足的问题,提出一种基于因子图的多传感器紧耦合定位算法。该算法分别接收来自惯性测量单元(IMU)、轮式编码器和2D激光雷达的数据,并构建IMU预积分因子、轮式里程计因子、位姿先验因子以及激光里程计因子;通过因子图对这些因子进行增量优化后输出得到移动机器人的状态信息,同时实时估计IMU的漂移量并进行校正。实验结果表明,无论是在未知环境还是已知环境下,该定位算法都可以有效提高移动机器人的定位精度和鲁棒性。

煤矿井下移动机器人同步定位关键技术研究

同步定位是实现智能矿山设备自动化的关键技术,然而由于煤矿复杂的工况环境使得激光雷达的同步定位容易退化,导致移动机器人定位和测绘失败。基于此,提出了一种鲁棒的激光雷达同步定位方法。该方法通过集成激光雷达和惯性测量单元(IMU)数据来检测和补偿退化的场景。利用扰动模型检测由于线、面特征约束不足而导致退化的方向和程度,从而获得退化的因子和矢量。将IMU预积分得到的位姿投影到平面特征上,用于局部地图匹配,实现两步退化补偿。最后通过因子图优化实现了全局一致的激光雷达同步定位。

应用赤池信息量准则优选惯性元器件的随机误差模型

针对当前基于目视Allan方差曲线进行惯性元件随机误差建模中存在随机误差类别识别困难,人工干预较多的问题,提出了一种应用赤池信息量准则(AIC)优选惯性元件随机误差模型的方法。首先,采用加权最小二乘拟合法对不同间隔的Allan方差数据进行合理的加权,并将随机误差系数用指数形式表示作为待估系数,避免了随机误差系数估计值为负的问题。其次,采用AIC对模型中包含的随机误差类型进行优选。最后,基于光纤陀螺仪测量数据对所提方法进行了实验验证,实验结果表明,所提方法能根据AIC的值自动识别出被测光纤陀螺的随机误差类别为:角度随机游走、零偏不稳定性和角速度随机游走。所选模型模拟的功率谱密度与实测数据的功率谱密度吻合度较好。验证了所提方法的有效性。