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无人驾驶矿用运输车辆感知及控制方法

李宏刚 王云鹏 廖亚萍 周彬 余贵珍

李宏刚, 王云鹏, 廖亚萍, 等 . 无人驾驶矿用运输车辆感知及控制方法[J]. 北京航空航天大学学报, 2019, 45(11): 2335-2344. doi: 10.13700/j.bh.1001-5965.2019.0521
引用本文: 李宏刚, 王云鹏, 廖亚萍, 等 . 无人驾驶矿用运输车辆感知及控制方法[J]. 北京航空航天大学学报, 2019, 45(11): 2335-2344. doi: 10.13700/j.bh.1001-5965.2019.0521
LI Honggang, WANG Yunpeng, LIAO Yaping, et al. Perception and control method of driverless mining vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2335-2344. doi: 10.13700/j.bh.1001-5965.2019.0521(in Chinese)
Citation: LI Honggang, WANG Yunpeng, LIAO Yaping, et al. Perception and control method of driverless mining vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2335-2344. doi: 10.13700/j.bh.1001-5965.2019.0521(in Chinese)

无人驾驶矿用运输车辆感知及控制方法

doi: 10.13700/j.bh.1001-5965.2019.0521
基金项目: 

国家重点研发计划 2016YFB0101001

详细信息
    作者简介:

    李宏刚  男, 博士研究生。主要研究方向:无人矿卡智能感知及控制

    周彬   男, 博士。主要研究方向:感知信息融合技术

    通讯作者:

    周彬. E-mail:binzhou@buaa.edu.cn

  • 中图分类号: U471.15

Perception and control method of driverless mining vehicle

Funds: 

National Key R & D Program of China 2016YFB0101001

More Information
  • 摘要:

    针对当前矿区生产作业效率低下、安全事故频发等问题,提出了一种矿用运输车辆无人驾驶感知及控制方法。感知部分,设计出基于激光雷达和毫米波雷达融合的多目标识别架构,在数据关联的基础上,应用基于卡尔曼滤波的联合概率数据关联(JPDA)算法实现矿区环境下多目标识别;控制部分,采用路径预瞄-跟踪的方式,将横向控制与纵向控制进行解耦,并通过反馈机制实时进行偏差修正,实现无人驾驶矿用运输车辆精准的横向与纵向控制。此外,搭建了矿车无人驾驶系统平台,在矿区不同场景下对上述感知及控制方法进行了测试。实验结果表明,感知算法能够实现道路可行驶区域的精确检测,并可识别出多种障碍物类型,控制算法在上下坡等场景下可实现无人驾驶矿用运输车辆纵向速度和横向位置的精准控制,满足实际应用需求。

     

  • 图 1  矿用运输车辆无人驾驶研究技术路线

    Figure 1.  Technical research route of driverless technology for mining vehicles

    图 2  JPDA算法流程图

    Figure 2.  Flowchart of JPDA algorithm

    图 3  纵向控制方案

    Figure 3.  Longitudinal control scheme

    图 4  车辆圆周运动示意图

    Figure 4.  Schematic diagram of circular motion of vehicle

    图 5  车辆航向角误差反馈

    Figure 5.  Error feedback of vehicle heading angle

    图 6  横向控制方案

    Figure 6.  Vehicle lateral control scheme

    图 7  矿用运输车辆无人驾驶系统组成

    Figure 7.  Composition of driverless system for mine transportation

    图 8  对道路可行驶区域的检测效果

    Figure 8.  Detection effect of road drivable area

    图 9  对行人和路边障碍物的检测效果

    Figure 9.  Detection effect of pedestrian and roadside obstacles

    图 10  对轿车的检测效果

    Figure 10.  Car detection effect

    图 11  对卡车和路边障碍物的检测效果

    Figure 11.  Detection effect of trucks and roadside obstacles

    图 12  装载点停靠路径

    Figure 12.  Docking path of mount point

    图 13  卸载点停靠路径

    Figure 13.  Docking path of unloading point

    图 14  曲率最大时轨迹

    Figure 14.  Trajectory with maximum curvature

    图 15  曲率较小时轨迹

    Figure 15.  Trajectory with small curvature

    图 16  第1次测试结果

    Figure 16.  The first test results

    图 17  第2次测试结果

    Figure 17.  The second test results

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出版历程
  • 收稿日期:  2019-09-24
  • 录用日期:  2019-10-14
  • 刊出日期:  2019-11-20

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