基于振动陀螺的动态零位校准及其误差补偿

根据固态振动陀螺输出信号和噪声特点对其构成的低成本惯性测量单元(IMU)的原始传感信号进行了快速小波滤波和灰色理论建模处理。运用累加生成操作(AGO)方法得到有规则的单值对应非线性函数,并获得陀螺零位输出在三维空间中的单值映射模型。以时间和温度为输入,根据灰色神经网络建立陀螺的漂移模型,对累加生成方法生成的单值对应非线性函数进行逼近,从而提高了动态测量精度。同时采用活动阈值融合算法,优化陀螺和加速度计动态测量数据。实验证明,上述方法和算法有效提高了系统测量精度。     

小型无人机飞行控制系统的硬件实现

介绍了基于PCI04的小型无人机飞行控制系统的硬件实现，给出了系统整体方案的设计和具体的硬件选型，详细对惯性测量单元和电子罗盘进行了分析，系统具有设计精炼，可靠性高，可移植性强等特点     

基于多IMU融合的甲板变形监测

大型舰船甲板存在变形,其变形对舰载武器系统的精度有很大影响.为监测舰船甲板的变形,提出基于多个惯性测量单元 (inertial measuring unit,IMU)数据融合的变形测量方法,基于某船的实测数据建立甲板变形测量模型,利用最小二乘估计对甲板的变形进行分析.仿真结果表明,将该方法用于末安装IMU战位点处的甲板变形的实时监测是完全可行的; IMU的布局对甲板变形的监测有较大影响,IMU在舰船上的布置应依照舰载武器系统的布局及其精度确定.     

A machine learning approach to detect changes in gait parameters following a fatiguing occupational task

The purpose of this study is to provide a method for classifying non-fatigued vs. fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction ($1 Recognizer) along with the support vector machine model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm. A single inertial measurement unit on the ankle was used, providing a minimally intrusive and inexpensive tool for monitoring. The classifier distinguished between states using distance-based scores from the recogniser and the step duration. The results of fatigue detection showed an accuracy of 90% across data from 20 recruited subjects. This method utilises the minimum amount of data and features from only one low-cost sensor to reliably classify the state of fatigue induced by a realistic manufacturing task using a simple machine learning algorithm that can be extended to real-time fatigue monitoring as a future technology to be employed in the manufacturing facilities.

Practitioner Summary: We examined the use of a wearable sensor for the detection of fatigue-related changes in gait based on a simulated manual material handling task. Classification based on foot acceleration and position trajectories resulted in 90% accuracy. This method provides a practical framework for predicting realistic levels of fatigue.     

基于改进MSCKF算法的室内机器人定位方法

针对传统多状态约束卡尔曼滤波算法(MSCKF)在实现机器人室内定位时,速度和位置状态方程需要对IMU中加速度计的测量数据进行积分,存在漂移和累计误差,且加速度计受重力干扰问题,本文提出改进MSCKF算法.改进MSCKF算法避免使用加速度计传感器,利用轮式里程计传感器对平移测量较为精确的优点,将IMU中陀螺仪和轮式里程计的数据进行融合,改进MSCKF算法的扩展卡尔曼(EKF)状态方程.首先利用陀螺仪传感器的角速度数据得到改进EKF姿态方程,然后利用轮式里程计传感器的平移数据,结合姿态方程中的旋转信息得到改进EKF速度和位置方程.最后在机器人操作系统(ROS)上实现MSCKF及其改进算法,并结合Turtlebot2机器人在室内进行实验验证.实验结果表明,改进MSCKF算法的运动轨迹更接近于真实轨迹,定位精度较改进前所有提高,改进前平均闭环误差是0.429 m,改进后平均闭环误差是0.348 m.     

基于VR设备中IMU的头部姿态感知算法研究

随着头戴式显示设备的发展,基于虚拟现实(Virtual Reality,VR)的教育培训随之流行开来。在基于VR设备的教育培训中,存在用户与设备间进行交互的场景。在这些场景中,VR设备对使用者头部姿态的感知尤为重要。为了保证较高的姿态解算精度,同时降低系统的计算量,设计了四种基于头戴式惯性测量单元(Inertial Measurement Unit,IMU)的姿态解算方法,并对比了这四种算法的姿态解算精度和运算效率。实验结果表明,相比于其他三种算法,使用四元数微分方程的三阶泰勒展开递推式更新四元数,同时利用间接扩展卡尔曼滤波器融合地磁信息进行修正的姿态解算方法保证了较高的解算精度和较少的运算时间。该基于地磁修正+三阶泰勒展开法的头部姿态感知算法计算所得欧拉角与SBG公司生产的IG-500N型号IMU中提供的姿态角具有1.1×10-2度的总体平均偏差,且该算法使用MATLAB R2013a平台计算14000组数据的耗时为5.1秒。     

下肢外骨骼研究进展及关节运动学解算综述

随着传感融合、移动计算、智能驱动等技术的发展以及研究者对人体运动中下肢重要生物力学功能认知的逐步深化,下肢外骨骼机器人作为一种与下肢并联,能为穿戴者行走助力的可穿戴智能设备愈发受到世界各研究机构的重视。本文根据下肢外骨骼的用途和结构详细综述了近年下肢外骨骼的研究进展,并借此对下肢外骨骼的未来发展进行展望。并针对下肢外骨骼在实时运动学检测与控制上对小型传感器的迫切需求,提出一种能够用于控制下肢外骨骼的基于惯性测量单元的人体下肢关节运动学测量与解算技术,在基于惯性测量的单自由度关节角度结算上得到较好结果。     

基于惯性测量单元的欠驱动球形机器人惯性参数辨识

针对欠驱动球形机器人,提出基于惯性测量单元的惯性参数辨识方法。首先,根据Kane方法建立欠驱动球形机器人的动力学模型;其次,利用该模型中的三个欠驱动方程推导出球形机器人惯性参数辨识模型;最后,以一种新型的球形机器人为实验平台,根据安装在球形机器人框架上的惯性测量单元实测信息,对本文方法进行实验验证。实验结果表明,辨识结果具有合理性。该方法简单可行,为其他欠驱动机器人惯性参数辨识提供了一种新的思路。     

An error-based micro-sensor capture system for real-time motion estimation

A wearable micro-sensor motion capture system with 16 IMUs and an error-compensatory complementary filter algorithm for real-time motion estimation has been developed to acquire accurate 3D orientation and displacement in real life activities. In the proposed filter algorithm, the gyroscope bias error, orientation error and magnetic disturbance error are estimated and compensated, significantly reducing the orientation estimation error due to sensor noise and drift. Displacement estimation, especially for activities such as jumping, has been the challenge in micro-sensor motion capture. An adaptive gait phase detection algorithm has been developed to accommodate accurate displacement estimation in different types of activities. The performance of this system is benchmarked with respect to the results of VICON optical capture system. The experimental results have demonstrated effectiveness of the system in daily activities tracking, with estimation error 0.16 ±0.06 m for normal walking and 0.13 ±0.11 m for jumping motions.