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。

基于惯性测量单元的旋挖钻机钻具姿态测量方法

为实时获取钻孔过程中钻具的姿态信息,提高机手掌握成孔垂直度,设计了一种基于惯性测量单元的钻具姿态测量方法。该方法采用三轴加速度计、磁力计和陀螺仪构成惯性测量单元,建立基于四元数的姿态测量非线性模型,并采用基于四元数的扩展卡尔曼滤波算法,有效降低振动等干扰信号的影响,提高钻具姿态解算的准确性。实验结果表明,该方法能够有效避免振动对动态测量的干扰,实现旋挖钻机姿态的准确测量,为旋挖钻机施工提供实时、准确的姿态监测手段,为桩基施工质量提供重要保障。

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。

天文导航高精度自适应航姿与时间同步技术

船用天文导航设备因采用数学解算平台,在前端原始数采样时需进行滤波处理,数据输出与同步脉冲信号输出存在时延,一旦同步脉冲信号被干扰,对外输出数据将失去实时性。针对此问题,本文提出一种高精度自适应航姿同步技术,通过建立自适应基准脉冲为预处理模块与导航解算模块提供容错空间,结合外部时间信息实时进行时间同步自校正,避免同步信号丢失造成的时间同步异常,实现上级系统对船用导航系统多样化同步需求下高频实时数据输出。试验结果表明,该方法可行有效,可提高航姿信息实时性与可靠性。

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 PaveVision3D 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 ki- nematic 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。

GNSS/INS组合导航中IMU参数设定与优化

针对惯性测量单元(IMU)参数设置不合理导致全球卫星导航系统(GNSS)/惯性导航系统(INS)组合导航无法充分发挥IMU性能的问题,提出了一套IMU参数设定与优化方案,建立了以GNSS短期中断期间导航漂移误差统计值、理论估计精度一致性以及IMU误差参数估计稳定性为评估对象的考核准则,使优化后的IMU参数可以确保GNSS/INS组合导航解算中状态预测方差阵的准确性,实现预测信息与观测信息权重合理分配,进而保障GNSS/INS组合导航最优性能。实测验证采用ADIS16465和ICM206022款典型车规级和消费级微机电(MEMS)IMU进行性能测试分析,测试结果表明:初始IMU参数与GNSS/INS组合导航算法适配度较低,无法得到最优组合导航结果;优化后,IMU参数在保障组合导航最优性能基础之上,也可实现理论估计精度与实际精度高度一致。

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。

一种双路并行的大规模手势识别模型

文章以大规模手势为研究对象,提出一种基于肌电信号(electromyography,EMG)分支和惯性测量单元(inertial measurement unit,IMU)分支的双路并行手势识别模型。首先,设计双路并行模型来充分提取数据特征,EMG分支利用二维卷积神经网络设计双流结构,分别关注EMG信号的空间和通道变化,IMU分支在卷积长短时记忆(convolutional long short-term memory,ConvLSTM)网络基础上引入时间机制,将空间信息与时间信息融合;其次,对模型预训练并根据预训练模型进行参数微调,提高模型泛化性;最后,在500个常用的中国手语手势上进行测试,结果表明,该模型平均识别率为82.1%,与SignSpeaker和CG-Recognizer相比分别提高了21.0%和6.8%。

基于IMU与激光雷达融合的无人弹药补给车SLAM系统研究

针对单一传感器建图精度低、实时性不足的问题,将IMU融合到激光雷达SLAM算法中。首先,采用手眼标定方法对2种传感器坐标系外参进行标定,实现传感器在时间与空间上的对齐。然后,结合因子图优化模型,解决在建图过程中产生的漂移现象,并将IMU融合到激光雷达LeGO-LOAM算法中。最后,在室外场景下搭建了无人弹药补给车SLAM实验平台,分别进行了LeGO-LOAM算法融合IMU前后的建图和定位试验。结果表明,融合IMU后的SLAM算法建图和定位精度都明显提高,满足了在未知环境下无人弹药补给车建图和定位的性能要求。

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.

GNSS/IMU/LiDAR融合定位研究

为提升低成本卫星接收机和惯性测量单元(inertial measurement unit,IMU)条件下传统组合导航定位的抗干扰性和定位精度,本文通过融合GNSS、IMU、激光雷达(laser radar,LiDAR)来提高定位的鲁棒性及定位精度.在高楼遮挡等复杂环境下由于卫星信号丢失导致卫星定位结果降低,可通过GNSS与IMU的组合来提升导航定位的鲁棒性及其精度.如果卫星信号缺失时间过长,那么低成本条件下的GNSS/IMU组合定位精度仍不理想,本文提出利用LiDAR里程计输出的位置信息与传统组合导航通过扩展卡尔曼滤波(extended Kalman filter,EKF)进行融合定位.实验得出:在无遮挡的环境下融合定位标准差(standard deviation,STD)精度较之卫星定位提升53.7%,均方根误差(root mean square error,RMSE)精度提升56%,较之GNSS/IMU组合定位STD精度提升37.9%,RMSE精度提升38.6%.在有遮挡的环境下融合定位STD精度较之卫星定位提升59.4%,RMSE精度提升71.3%,较之GNSS/IMU组合定位STD精度提升26.3%,RMSE精度提升33.7%.