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

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

周艳^1,2,,
陈红¹,
张叶廷³,
黄悦莹¹,
张鹏程⁴,
杨卫军⁴

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
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- 文章访问数: 509
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- PDF下载量: 27
- 被引次数: 0
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

Multi-objective Indoor Path Planning Method with Dynamic Environment Awareness

摘要

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%.
- 环境语义感知 /
- 路径规划 /
- Dijkstra算法 /
- 室内导航
Abstract: A dynamic environment-aware multi-objective indoor path planning method is proposed, aimed at satisfying the multi-objective navigation requirements of users in complex indoor environments. Multi-dimensional indoor environment semantics such as indoor path complexity, the degree of congestion, and blocking events were take into account. The node-edge representation indoor navigation network model was also expanded, and a navigation traffic cost function was established by precisely quantifying the multi-dimensional indoor environment semantics. The value of the navigation traffic cost function was then taken as the side length of the model, and a multi-objective indoor path planning algorithm based on Dijkstra was designed and implemented. The results of the simulation show that navigation path planning can avoid unavailable connection edges by adding the vertical components with directional semantics and considering the blocking events factor in the extended indoor navigation network model. After the path complexity and traffic congestion were considered, the travel time is saved by an average of 17% in three traffic patterns, i.e., light, mild and heavy congestion.
- environmental semantic awareness /
- path planning /
- Dijkstra algorithm /
- indoor navigation

HTML全文

图 1 扩展的室内导航路网模型

Figure 1. Enhanced indoor navigation road network model

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图 2 室内导航路网建模实例

Figure 2. An example for indoor navigation road network modelling

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图 3 室内垂直组件导航路径建模

Figure 3. Navigation route modelling of indoor vertical components

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图 4 室内多目标路径规划流程

Figure 4. Flow chart of indoor multi-objective routing

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图 5 三维室内导航路网模型

Figure 5. 3D indoor navigation road network model

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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

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图 7 路径规划的通行时间比较

Figure 7. Comparison of travel time in path planning

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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

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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

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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

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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	无	无	0	26-163-105-234
2	t₁时刻	无	无	0	26
	t₂时刻	1楼到2楼楼梯区域	轻度	26	105
	t₃时刻	2楼到3楼楼梯区域	缓慢	105	163
	t₄时刻	3楼到4楼电梯区域	堵塞	163	234

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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	无	无	24	28-102-159-234
2	任意时刻	无	无/下行	24	28-102-159-234
3	t₁时刻	1楼黄色区域/轻度	无	24	28
	t₂时刻	2楼橘色区域/缓慢	发生/无	28	159
	t₃时刻	4楼红色区域/堵塞	无	159	234
	t₄时刻	无	无	234	102

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