Multi-objective Indoor Path Planning Method with Dynamic Environment Awareness
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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%.
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关键词:
- 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.-
Key words:
- environmental semantic awareness /
- path planning /
- Dijkstra algorithm /
- indoor navigation
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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 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 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 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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