欢迎访问《兵工学报》官方网站,今天是 分享到:

兵工学报 ›› 2019, Vol. 40 ›› Issue (4): 680-688.doi: 10.3969/j.issn.1000-1093.2019.04.002

• 论文 • 上一篇    下一篇

分布式电驱动无人高速履带车辆越野环境轨迹预测方法研究

赵梓烨, 刘海鸥, 陈慧岩   

  1. (北京理工大学 机械与车辆学院, 北京 100081)
  • 收稿日期:2018-10-12 修回日期:2018-10-12 上线日期:2019-06-10
  • 作者简介:赵梓烨(1992—),男,博士研究生。E-mail:zzybit@gmail. com
  • 基金资助:
    国家自然科学基金项目(51675039)

Research on Trajectory Prediction Method of Distributed High Speed Electric Drive Unmanned Tracked Vehicle in Off-roadConditions

ZHAO Ziye, LIU Haiou, CHEN Huiyan   

  1. (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)
  • Received:2018-10-12 Revised:2018-10-12 Online:2019-06-10

摘要: 越野环境下,无人车辆轨迹预测是车辆轨迹跟踪和精确导航的核心模块,预测误差将直接影响无人车辆行驶任务完成的准确程度。为实现速差转向式履带车辆在复杂越野环境下无人行驶轨迹准确预测的目的,搭建了分布式电驱动无人履带车辆系统,实现了车辆动态过程中的无人系统数据和车辆底层状态数据的同步采集。建立了速差转向车辆运动学模型,分析了履带车辆滑动转向特性。分别采用扩展卡尔曼滤波(EKF)方法和Levenberg-Marquardt方法对转向过程中的滑动参数进行估计,并完成了车辆轨迹预测。基于真实越野环境下的实车数据进行了验证。试验结果表明:相比于履带车辆理想预测模型,所采用的两种轨迹预测方法都大幅降低了车辆轨迹预测误差;对误差均值而言,EKF方法预测轨迹优于Levenberg-Marquardt方法;对误差标准差而言,后者优于前者,且随着转向程度的增加而增大。

关键词: 无人履带车辆, 轨迹预测, 越野环境, 转向程度, 统计分析

Abstract: The unmanned vehicle trajectory prediction module is a core module of vehicle trajectory tracking and precise navigation in the off-road conditions. The prediction error has direct effect on the accuracy of the completion of unmanned vehicle driving tasks. In order to realize the accurate prediction of trajectory of skid-steered unmanned tracked vehicle in the complex off-road conditions, a distributed electric drive unmanned tracked vehicle system was built, which realizes the synchronous acquisition of unmanned system data and vehicle state data in the vehicle dynamic process. A kinematic model of skided-steered tracked vehicle is established, and the sliding steering characteristics of tracked vehicle are analyzed. The extended Kalman filter (EKF) method and the Levenberg-Marquardt(L-M) method are used to estimate the sliding parameters in the steering process, and the vehicle trajectory prediction is completed. The verification analysis is based on real vehicle data in real off-road conditions. The statistical analysis method is used to compare the prediction errors of two prediction methods. The test results show that, compared with the ideal prediction model of tracked vehicles, the two trajectory prediction methods greatly reduce the prediction error of vehicle trajectory. For the mean of error, EKF method is better than L-M method in the trajectory prediction; for the standard deviation, the latter is better than the former, and the standard deviation increases with the increase in the degree of steering. Key

Key words: unmannedtrackedvehicle, trajectoryprediction, off-roadenvironment, steeringdegree, statisticalanalysis

中图分类号: