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动态环境感知的多目标室内路径规划方法

周艳 陈红 张叶廷 黄悦莹 张鹏程 杨卫军

周艳, 陈红, 张叶廷, 黄悦莹, 张鹏程, 杨卫军. 动态环境感知的多目标室内路径规划方法[J]. 西南交通大学学报, 2019, 54(3): 611-618, 632. doi: 10.3969/j.issn.0258-2724.20180098
引用本文: 周艳, 陈红, 张叶廷, 黄悦莹, 张鹏程, 杨卫军. 动态环境感知的多目标室内路径规划方法[J]. 西南交通大学学报, 2019, 54(3): 611-618, 632. doi: 10.3969/j.issn.0258-2724.20180098
ZHOU Yan, CHEN Hong, ZHANG Yeting, HUANG Yueying, ZHANG Pengcheng, YANG Weijun. Multi-objective Indoor Path Planning Method with Dynamic Environment Awareness[J]. Journal of Southwest Jiaotong University, 2019, 54(3): 611-618, 632. doi: 10.3969/j.issn.0258-2724.20180098
Citation: ZHOU Yan, CHEN Hong, ZHANG Yeting, HUANG Yueying, ZHANG Pengcheng, YANG Weijun. Multi-objective Indoor Path Planning Method with Dynamic Environment Awareness[J]. Journal of Southwest Jiaotong University, 2019, 54(3): 611-618, 632. doi: 10.3969/j.issn.0258-2724.20180098

动态环境感知的多目标室内路径规划方法

doi: 10.3969/j.issn.0258-2724.20180098
基金项目: 国家重点研发计划资助项目(2018YFB0505501,2016YFB0502303);国家自然科学基金项目(41871321,41471332,41571392);中央高校基本科研业务费专项资金资助项目(ZYGX2015J113)
详细信息
    作者简介:

    周艳(1976—),女,副教授,博士,研究方向为地理信息系统和空间大数据分析,E-mail:zhouyan_gis@uestc.edu.cn

  • 中图分类号: V221.3

Multi-objective Indoor Path Planning Method with Dynamic Environment Awareness

  • 摘要: 为了满足复杂室内环境中用户的多目标导航需求,提出了动态环境感知的多目标室内路径规划方法. 该方法顾及室内路径复杂度、拥挤程度与阻断事件等多维室内环境语义,扩展了节点-边表示的室内导航路网模型,通过量化表征多维室内环境语义,建立了能够综合感知室内环境语义变化的导航通行成本函数,然后,将顾及室内动态环境语义的导航通行成本函数值作为室内导航路网模型的边长,设计实现了基于Dijkstra的多目标室内路径规划算法. 通过模拟实验分析比较室内路径规划结果,实验结果表明:由于扩展后的室内导航路网模型增加了具有方向性语义的垂直组件,考虑了阻断事件因素,导航路径规划能够避开不可用连接边;在路径拥挤程度分别为轻度、缓慢和堵塞情况下,由于考虑了路径复杂度和拥挤程度,节约的通行时间平均提升了17%.

     

  • 图 1  扩展的室内导航路网模型

    Figure 1.  Enhanced indoor navigation road network model

    图 2  室内导航路网建模实例

    Figure 2.  An example for indoor navigation road network modelling

    图 3  室内垂直组件导航路径建模

    Figure 3.  Navigation route modelling of indoor vertical components

    图 4  室内多目标路径规划流程

    Figure 4.  Flow chart of indoor multi-objective routing

    图 5  三维室内导航路网模型

    Figure 5.  3D indoor navigation road network model

    图 6  拥挤情况对多目标室内路径规划的影响

    Figure 6.  Impact of indoor multi-objective planning route with degree of crowdedness

    图 7  路径规划的通行时间比较

    Figure 7.  Comparison of travel time in path planning

    图 8  顾及环境语义的多目标规划路径

    Figure 8.  Multi-objective planning route with environment semantics

    表  1  拥挤程度与人流量对应情况

    Table  1.   Degree of crowdedness and corresponding flow density

    拥挤程度畅通轻度缓慢堵塞
    人流密度
    /(人•m–2
    [0,0.75](0.75,2.00](2.00,3.50]>3.50
    通行速度
    /(m•s–1
    >1.40(1.08,1.40](0.30,1.08]≤0.30
    下载: 导出CSV

    表  2  经典最优路径规划算法

    Table  2.   Classic optimal path planning algorithms

    算法优点缺点
    Dijkstra算法简单,全局最优解长距离路径规划的效率较低
    A*启发式搜索效率高局部最优解
    Floyd支持负权边,用于有向图算法复杂度过高
    BF支持负权边,可用于所有图算法复杂度过高
    下载: 导出CSV

    表  3  实验1环境语义

    Table  3.   Environment semantics of experiment 1

    情景编号请求时间拥挤区域拥挤程度起点终点访问顺序
    1任意时刻026-163-105-234
    2t1时刻026
    t2时刻1楼到2楼楼梯区域轻度26105
    t3时刻2楼到3楼楼梯区域缓慢105163
    t4时刻3楼到4楼电梯区域堵塞163234
    下载: 导出CSV

    表  4  实验2环境语义

    Table  4.   Environment semantics of experiment 2

    情景编号请求时间拥挤区域/拥挤程度事件/上下行起点终点访问顺序
    1任意时刻2428-102-159-234
    2任意时刻无/下行2428-102-159-234
    3t1时刻1楼黄色区域/轻度2428
    t2时刻2楼橘色区域/缓慢发生/无28159
    t3时刻4楼红色区域/堵塞159234
    t4时刻234102
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-03-06
  • 修回日期:  2018-04-27
  • 网络出版日期:  2019-02-23
  • 刊出日期:  2019-06-01

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