Improving unmanned ground vehicle navigation by exploiting virtual sensors and multisensor fusion

This paper investigates techniques on improving navigation accuracy using multiple sensors mounted on a mobile platform and exploiting the inherent characteristic of a ground vehicle that does not move along the cross-track and off the ground, often termed nonholonomic constraints (NHC) for car-like vehicles that assume no slip or skid. The forward velocity of the vehicle is obtained using a wheel encoder. The 3D velocity vector becomes observable during the normal moving state of the vehicle by using NHC, which produces one virtual sensor. Another virtual sensor is the zero-velocity update (ZVU) condition of the vehicle; when the condition is true, the 3D velocity vector (which is zero) becomes observable. These observables were employed in an extended Kalman filter (EKF) update to limit the growth of inertial navigation system error. We designed an EKF for data fusion of inertial measurement units, global positioning systems (GPS), and motion constraints (i.e., NHC and ZVU). We analyzed the effects of utilizing these constraints on improving navigation accuracy in stationary and dynamic cases. Our proposed navigation suite provides reliable accuracy for unmanned ground vehicle applications in a GPS-denied environment (e.g., forest canopy and urban canyon).     

毫米波雷达与视觉传感器信息融合的车辆跟踪

为提高车辆前向防碰撞预警系统对前方道路环境感知的准确性,提出一种毫米波雷达与视觉传感器信息融合的车辆跟踪方法。提出的剔除雷达干扰目标算法缩短了对干扰目标的处理时间;提出的对称检测算法可对雷达目标兴趣区域进行对称检测,减小雷达目标兴趣区域的横向位置误差;提出的KCF-KF组合滤波算法可提高跟踪车辆的精度。实车试验表明,该方法可有效跟踪车辆位置信息,像素坐标系下的X、Y坐标跟踪准确率分别超过97.34%与95.19%。     

多传感器信息融合的列车定位方法研究

提出了多传感器信息融合的列车定位方法.介绍了多传感器信息融合结构及联邦卡尔曼滤波算法模型.对INS/GPS/DVS组合导航进行了Matlab仿真,结果表明,多传感器信息融合的列车定位方法能提高列车的定位精度和可靠度,更适合于城市轨道交通中的列车定位.     

Real-time Tire Parameters Observer for Vehicle Dynamics Stability Control

The performance of the vehicle dynamics stability control system(DSC) is dominated by the accurate estimation of tire forces in real-time.The characteristics of tire forces are determined by tire dynamic states and parameters,which vary in an obviously large scope along with different working conditions.Currently,there have been many methods based on the nonlinear observer to estimate the tire force and dynamic parameters,but they were only used in off-line analysis because of the computation complexity and the dynamics differences of four tires in the steering maneuver conditions were not considered properly.This paper develops a novel algorithm to observe tire parameters in real-time controller for DSC.The algorithm is based on the sensor-fusion technology with the signals of DSC sensors,and the tire parameters are estimated during a set of maneuver courses.The calibrated tire parameters in the control cycle are treated as the elementary states for vehicle dynamics observation,in which the errors between the calculated and the measured vehicle dynamics are used as the correcting factors for the tire parameter observing process.The test process with a given acceleration following a straight line is used to validate the estimation method of the longitudinal stiffness;while the test process with a given steering angle is used to validate the estimated value of the cornering stiffness.The ground test result shows that the proposed algorithm can estimate the tire stiffness accurately with an acceptable computation cost for real-time controller only using DSC sensor signal.The proposed algorithm can be an efficient algorithm for estimating the tire dynamic parameters in vehicle dynamics stability control system,and can be used to improve the robustness of the DSC controller.     

基于胎压监测的自卸车车载称重系统设计

提出了一种基于胎压监测的车载称重系统解决方案。在车辆胎压实时监测的基础上,通过胎压变化测量车辆载重反应车辆的超载情况,并通过控制车辆熄火、取力器工作、气路建立以及车载仪表显示等多种方法进行超载管控,此外通过GPS／GPRS实现车辆的定位以及与车辆远程控制中心的通信。     

基于 EM 估计的多模型车载组合导航算法

针对行车过程中车载全球卫星导航系统受遮挡产生多径效应、可见星数量少等影响,造成的定位精度差的问题,提出了 一种基于期望最大化(EM)的交互式多模型车载组合导航算法。 本文采用了混合高斯分布模型描述 GNSS 多径效应误差分布, 提出了基于 EM 的 SINS / GNSS 子系统组合导航信息融合方法,实现多径效应偏置误差的估计。 建立了基于零速约束的 SINS / OD 组合导航模型,同时利用交互式多模型算法实现了在 GNSS 信号丢失情况下的导航模型交互融合,提高了车载组合导航系 统精度。 车载实验结果表明在 GNSS 多径效应及信息丢失条件下,本文所提出算法能有效提高导航精度,多径效应的混合高斯 模型偏置为 10 m 条件下,偏置估计误差小于 0. 5 m,水平最大定位误差为 2 m,比传统交互式多模型算法定位误差降低 84. 62% 。     

An adaptive navigation method for a small unmanned aerial rotorcraft under complex environment

Focusing on low navigation performance of small unmanned aerial rotorcraft under complex environment, a composite navigation method combined with adaptive Kalman filtering and radial basis function neural network prediction method is proposed to improve navigation performance during GPS outages. When the GPS signal is available, an adaptive Kalman filter based on covariance scaling is introduced to deal with the process noise in real time. Meanwhile, a radial basis function neural network is trained on line to construct the projection among input (output of the inertial measurement unit, attitude and GPS losing time) and output (position error and velocity error). During GPS outages, the radial basis function neural network can provide high performance error estimation for position and velocity to improve state information. Finally, a land vehicle test and a flight test have confirmed that the proposed method can improve the navigation performance largely under complex environment.     

分布式电驱动车辆的无味粒子滤波状态参数联合观测

针对目前分布式电驱动车辆动力学领域中存在的状态参数观测体系不完善、观测精度低的问题,利用分布式电驱动车辆多信息源的特点,提出无味粒子滤波状态参数联合观测方法。基于非线性车辆动力学模型对分布式电驱动车辆的纵向速度、质心侧偏角、横摆角速度及各轮侧向力进行联合观测。同时,为提高侧向力的观测精度,采用非线性动态轮胎模型。在完成模型搭建的基础上,考虑到所搭建的车辆动力学模型具有强非线性,设计适用于强非线性模型的无味粒子滤波器对多个状态变量进行联合观测。而为进一步提高状态观测精度,进行量测噪声协方差的自适应调节。仿真和试验结果表明,所提出的状态观测方法能够提高分布式电驱动车辆状态参数观测精度和鲁棒性。     

智能网联车即插即用组网模型及决策融合算法

针对智能网联车（ICV）多传感器融合前组网流程繁琐的问题,提出了一种车载即插即用（PnP）环境感知传感器“自搜索(SS)自识别(SI)自标定（SC）”组网模型（简称“S-SIC”）。采用深度优先搜索规划算法自搜索PnP传感器并接入ADAS域;采用广播报文自识别PnP传感器并进行初始化;采用车体坐标系坐标转换矩阵及补偿算法自标定PnP传感器。针对ICV多传感器数据及特征级融合时设备难以兼容的问题,提出了K-m.AW决策级融合算法。实验结果表明：S-SIC模型搜索成功率达92%,搜索平均时间为1.79 s;在前车轨迹估计中,基于K-m.AW算法的多场景融合估计准确率较对比算法分别提高了7.6%、11.8%。     

引入滑模观测器的GPS/INS组合导航滤波方法

由低成本器件组成的卫星/惯性(GPS/INS)组合导航系统中,存在较大的非线性与不确定性,为改善这一问题,本文提出一种引入滑模观测器(SMO)的滤波方法。首先,该方法建立了组合导航系统模型,介绍了扩展卡尔曼滤波(EKF)计算过程并分析存在的不足。然后,介绍了滑模观测器的基本原理,根据系统构建观测器。最后,说明了引入滑模观测器的EKF组合导航算法实现流程,滑模观测器将模型误差、状态估计以及均值方差融入EKF算法,修正系统输出。通过轨迹仿真实验与车载实验验证了所提方法优于传统EKF算法,具有更高的滤波精度。在车载实验中,卫星信号失锁15 s情况下,与EKF方法相比,所提方法的东向位置误差降低了53%,北向位置误差降低了37%,证明该方法能够有效抑制GPS/INS组合导航误差发散,为以后工程实践提供一定的参考价值。     

Angle error compensation in wheel force transducer

Wheel force transducers (WFTs) have performance characteristics that make them attractive for applications in endurance evaluation of road vehicles, ride and handling optimization, tire development and vehicle dynamics. Since the WFT is mounted on the wheel and rotates with it, the rotational angle of the wheel is indispensable to calculate the real wheel forces. Unfortunately, an angle error caused by the steering of the vehicle will be incorporated into the measurement of the rotational angle, resulting in great error in the wheel force calculation. A new compensation algorithm is proposed in this paper to eliminate this angle error. In this algorithm, the GPS speed has been introduced to modify the measurement of the rotational angle in real time. Simulations with designed vehicle movement are carried out to demonstrate the effectiveness of the compensation algorithm. Furthermore, the results of real vehicle test show that this algorithm can be successfully used in practice to get more reasonable wheel loads.     

轮胎稳态运动学与六分力预报Ⅰ:理论与方法

提出一种新的轮胎运动学描述和六分力预报理论。滚动接触是汽车轮胎力学、轮轨动力学的核心问题,由于涉及刚体转动与有限变形,滚动接触运动学与动力学的描述与求解非常困难。用拉格朗日—欧拉混合描述法分析大变形滚动接触结构的速度场、加速度场和接触变形。以车轮定位角为卡尔丹角,用拉格朗日描述,得到了包含刚体转动和弹性变形的轮胎速度场。而接触区域的变形和受力用欧拉描述,通过欧拉网格和拉格朗日网格的信息传递,完成滚动结构动力学分析。所提出的理论可以退化到Fiala模型,并可以从理论上解释子午线轮胎的伪侧偏和伪侧倾现象。基于所建立的运动学理论和非线性有限元,建立轮胎六分力预报方法。针对某轿车子午线轮胎,分析轮胎接地面滑移速度、接地面积、接地压力、侧向剪力分布等随着侧偏角的变化规律,并研究该轮胎侧偏力和回正力矩随着胎面刚度和摩擦因数的参数敏感性。结果表明轮胎侧偏刚度和回正刚度主要受结构刚度控制,而峰值侧偏力和峰值回正力矩主要受摩擦因数控制。将利用所建立的方法和试验,探讨带束层结构对大规格子午线轮胎侧偏特性的影响规律,进一步验证所提出的理论和方法的正确性。所提出的理论和方法开辟了直接从轮胎设计预报轮胎六分力的新途径。     

基于AMESim和ADAMS铰接车全液压转向系统分析

铰接式车辆因其机动性好、适应性强且生产效率高而被广泛采用,而其不足之处在于转向时横向稳定性较差,翻车事故时有发生,为解决此问题,应用虚拟样机技术对此类车辆的转向过程进行分析。基于液压系统与多体动力学系统的联合仿真,在ADAMS中建立六轮电驱动铰接车的多体动力学模型,在AMESim中搭建其全液压转向系统模型,以实现铰接车的转向过程。 通过PID控制转向油缸的油量使其铰接角维持一个定值,对铰接车的行驶转向进行分析,并考虑车速对铰接车稳态转向的影响。获得铰接车行驶转向下各个轮胎的运动轨迹,各个轮胎所受侧向力、纵向力及垂直力随时间的变化曲线和转向油缸中活塞杆的受力。结果表明:随着行驶速度的增大,铰接车的外侧各个轮胎的受力均明显的增大;且铰接车的转向半径也随着增大;全液压转向系统具有明显的不足转向特性。     

基于联邦滤波的智能车辆多传感器组合导航的研究

为满足复杂环境下自主驾驶智能车辆的高可靠性导航要求,对智能车辆的多传感器组合导航进行了研究。提出了一种基于自适应联邦卡尔曼滤波的智能车辆SINS/CP-DGPS/双向光电测速仪组合导航方法,根据联邦滤波的分散滤波结构,分别建立了各滤波器的模型,进行了仿真试验验证。结果表明,该组合导航系统能为智能车辆提供丰富的导航信息,具有100Hz的高频输出、厘米级的导航精度和较强的容错能力,便于实现对发生故障的传感器的隔离。当GPS较长时间中断时,组合系统通过SINS/光电测速仪所构成的局部滤波器的辅助仍能为智能车辆提供可靠的导航数据。

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INS/SFGPS PPP紧组合系统动态补偿滤波算法

惯性/卫星组合导航系统结合精密单点定位技术可有效提高导航定位精度。但精密单点定位技术一般需采用双频接收机,成本较高;同时该系统中采用载波相位作为部分或全部观测量,极容易受到周跳的影响而导致精度下降和系统不稳定。针对上述问题,设计了一种惯性/卫星精密定位紧组合导航系统以及基于动态周跳补偿的鲁棒滤波算法。该系统采用低成本的单频接收机(SFGPS),以精密单点定位技术(PPP)处理过的伪距和载波相位作为观测信息,与惯性导航系统(INS)等效观测量进行紧组合,建立了相应紧组合观测模型并引入周跳作为信息融合滤波状态模型中的状态量,以滤波器信息构建周跳检测统计量并对周跳幅值进行识别和估计,实时补偿观测量以提高观测信息精度,同时以前述周跳估计的结果对状态模型中周跳状态量部分滤波参数进行实时调节。上述方法通过动态补偿周跳误差提高导航精度,通过滤波器参数自适应调节提高滤波稳定性。仿真结果验证了该系统模型及算法的有效性。     

GPS/INS组合导航系统的研究

讨论了飞机惯性导航系统(INS)与全球卫星导航系统(GPS)的利与弊以及卡尔曼滤波方法在组合定位中的应用情况,进一步提出了基于神经网络数据融合方法的GPS/INS组合导航系统.系统神经网络结构采用单隐层的三层神经网络,输入输出神经元数目是4个,基于256个训练样本由经验公式求得隐层神经元数目为8个,同时还建立了惯导系统的数学模型和数据融合的数学模型.给出了利用MATLAB编制的神经网络训练程序并对这一神经网络进行了训练和仿真.实验表明,组合导航系统经度误差可达9m,纬度误差可达8m,与单独GPS定位和INS定位相比精度得到了提高.     

Implementation of real-time positioning system using extended Kalman filter and artificial landmark on ceiling

Most localization algorithms use a range sensor or vision in a horizontal view, which usually imparts some disruption from a dynamic or static obstacle. By using landmarks on ceiling which the vehicle position were vertically measured, the disruption from horizontal view was reduced. We propose an indoor localization and navigation system based on an extended Kalman filter (EKF) and real-time vision system. A single upward facing digital camera was mounted on an autonomous vehicle as a vision sensor to recognize the landmarks. The landmarks consisted of multiple circles that were arranged in a defined pattern. Information on a landmark’s direction and its identity as a reference for an autonomous vehicle was produced by the circular arrangements. The pattern of the circles was detected using a robust image processing algorithm. To reduce the noise that came from uneven light, the process of noise reduction was separated into several regions of interest. The accumulative error caused by odometry sensors (i.e., encoders and a gyro) and the vehicle’s position were calculated and estimated, respectively, using the EKF algorithm. Both algorithms were tested on a vehicle in a real environment. The image processing method could precisely recognize the landmarks, and the EKF algorithm could accurately estimate the vehicle’s position. The experimental results confirmed that the proposed approaches are implementable.     

A new method of seamless land navigation for GPS/INS integrated system

For the last few years, integrated navigation systems have been widely used to calculate positions and attitudes of vehicles. The strapdown inertial navigation system (SINS) provides velocity, attitudes and position information, whereas the global positioning system (GPS) provides velocity and position information. A method using neural network (NN) and wavelet-based de-noising technology is introduced into the SINS/GPS/magnetometer integrated navigation system, because system accuracy may decrease during GPS outages. When the GPS is working well, NN is trained using the velocity and position information provided by SINS as input and the corresponding errors as output. Wavelet multi-resolution analysis (WMRA) is also introduced to de-noise the errors, the desired output of NN. Test results showed that velocity accuracies improved using NN, but other accuracies remained poor. By re-training NN with WMRA, the system accuracies improved to the level of using normal GPS signal. In addition, NN trained with WMRA also improved the approximation to the actual model, further enhancing alignment accuracy.     

An accuracy-improvement method for GPS/INS kinematic levelling for use in linear engineering surveying projects

This paper proposes a high-efficiency and continuous levelling method for linear objects, such as highways and railways, by applying a vehicle-borne GPS/INS kinematic surveying system which integrates the Global Positioning System (GPS) and the Inertial Navigation System (INS). Generally, the raw ellipsoid height directly outputted by GPS/INS needs to be transformed to the local orthometric height. However, because of various systematic errors and random errors, such as the measuring error and the quasi-geoid model error, the accuracy of the orthometric height would rarely meet engineering requirements. To solve this problem, a novel sliding least-squares collocation (SLSC) model based on ground control points was constructed to improve the accuracy of GPS/INS kinematic levelling. Two data sets collected from the Chang Ying highway located in Jilin province, China, were used to validate the proposed method. The experimental results indicate that the proposed method improves the trajectory points’ orthometric height accuracy by at least 160%.     

无线传感器网络在智能交通系统中的应用研究

提出一种新的基于无线传感器网络的弯道车辆防碰撞机制。对基于无线传感网络的智能交通信息采集系统进行了研究,提出了改进的多层无线传感器网络应用结构,并对信息采集的关键技术——车辆识别进行了深入研究,提出了利用信号处理、信息融合等技术,提高车辆正确识别率的模型,并进行了试验。