【作者】 周磊；

【导师】 倪涛； 孙志国；

【作者基本信息】 吉林大学 ， 机械工程（专业学位）， 2020， 硕士

【摘要】 应急救援车辆特殊的工作性质及应用场合决定了其在面对复杂路况时仍旧需要保持行驶的平顺性,而提高车辆行驶平顺性的一个有效途径是通过轴前预瞄控制技术调节主动悬架。目前,对轴前预瞄控制的研究多集中在控制算法上,而对轴前预瞄控制输入信息获取方法的研究则有很大空白。为此,本文基于2.5D重构技术及数据融合技术对车前路面工况进行了重构,并从中提取了车辆预测轨迹上的高程信息作为预瞄控制的输入信息。本文结合国家重点研发计划项目“高机动多功能应急救援车辆关键技术研究与应用示范”(编号:2016YFC0802900),研究了车前路面工况信息的2.5D重构方法及路面工况高程信息的提取方法。全文的主要研究工作如下:(1)完成了车前路面工况重构系统的硬件搭建和软件设计。根据需求对传感器进行了选择并在合适的位置安装各传感器,然后基于Win10系统通过编程实现了系统的软件设计。(2)为了提高系统稳定性及车辆位姿估计精度,建立了车辆坡路运动模型,并基于该模型的辅助实现了车辆位姿估计。首先建立了车辆坡路运动模型,并根据该模型分析了道路坡度对车辆位置估计的影响,然后基于车辆坡路运动模型的辅助应用扩展卡尔曼滤波方法实现了车辆位姿估计。针对GPS位置信息出现异常值的情况,提出了基于马氏距离减小GPS异常值影响的方法。(3)针对传统方法无法平衡车前路况信息获取的实时性与精度问题,提出了基于2.5D重构技术及数据融合技术的车前路面工况重构方法。首先分析了系统测量误差并建立了误差传递模型,然后构建了车前路况局部高程图并根据位姿数据及点云数据对高程图进行更新。为了提高精度,采用了卡尔曼滤波方法对高程值进行估计。最后基于Grid Map库实现了对车前路况数据的存储,并基于OpenGL库实现了车前路况数据的可视化。(4)考虑到轴前预瞄控制的输入信息为车前路面工况的高程信息,提出了一种车前路况高程信息提取的方法。本文根据实际情况,将车轮与地面接触的部分近似为一个椭圆,把椭圆范围内所有网格高程值的加权均值作为车轮接地处的高程值。在选取权重时,以椭圆范围内每个网格距离椭圆中心距离的远近作为权重,即距离越大,权重越小。(5)通过实验验证了方法的有效性和实用性。本文基于所搭建的硬件平台完成了车辆位姿估计实验、车前路况重构实验及车前路况高程信息提取实验。实验结果表明了本文所用的方法是有效的,能够在满足实时性要求的情况下保证路况信息的精度。更多还原

【Abstract】 The special working nature and application of emergency rescue vehicles determine that they still need to maintain the ride comfort when facing complex road conditions,and an effective way to improve the ride comfort is to adjust the active suspension through the preview control based on look-ahead.At present,researches on preview control based on look-ahead are mostly focused on control algorithms,while researches on methods of acquiring input information are few.To this end,this paper reconstructs the road conditions based on the 2.5D reconstruction technology and data fusion technology,and extracts the elevation information on the predicted trajectory of the vehicle as input information for the preview control.This paper combines the national key research and development plan project "Research and Application Demonstration of Key Technologies for High-Mobility Multifunctional Emergency Rescue Vehicles"(No.2016YFC0802900)to study the 2.5D reconstruction method of road conditions and the extraction method of road conditions elevation information.The main research work is as follows:(1)The hardware construction and software design of the road conditions reconstruction system are completed.The sensors are selected according to the needs and installed in the appropriate position.Then the software design of the system is realized by programming based on the Win10 system.(2)In order to improve the stability of the system and the accuracy of the vehicle pose,a vehicle slope motion model is established,and the vehicle pose estimation is realized based on this model.Firstly,a vehicle slope motion model is established,and the influence of road slope on vehicle position estimation is analyzed based on this model.Then the EKF method is used to realize the vehicle pose estimation based on the vehicle slope motion model.Aiming at the outliers in GPS information,a method based on Mahalanobis distance to reduce the influence of GPS outliers is proposed.(3)Aiming at the problem that traditional methods cannot balance the real-time capability and accuracy of road conditions acquisition,a method of road conditions reconstruction based on 2.5D reconstruction technology and data fusion technology is proposed.Firstly,the system measurement error is analyzed and the error transmission model is established.Then the local elevation map of road conditions is constructed and updated based on the pose data and point cloud data.To improve the accuracy,the Kalman filter method is used to estimate the elevation value.Finally,the data is stored based on the Grid Map library and visualized based on the OpenGL library.(4)Considering that the input information of the preview control is elevation information of the road conditions in front of the vehicle,a method for extracting elevation information of road conditions is proposed.According to the actual situation,this paper approximates the part of the wheel that touches the ground as an ellipse,and uses the weighted average of all the grid elevation values in the ellipse range as the elevation value of this position.When selecting weights,the distance between each grid in the ellipse and the center of the ellipse is taken as the weight,that is,the larger the distance is,the smaller the weight is.(5)The validity and practicability of the method are verified by experiments.Based on the built hardware platform,the experiments of vehicle pose estimation,road conditions reconstruction and elevation values extraction are completed.The results show that the method is effective and can guarantee the accuracy of road conditions reconstruction under the condition of meeting real-time requirements.更多还原