• ISSN 0258-2724
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Volume 54 Issue 3
Jun.  2019
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Article Contents
Journal of Southwest Jiaotong University  > 2019  >  54(3): 611-618, 632.
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
shu

Multi-objective Indoor Path Planning Method with Dynamic Environment Awareness

doi: 10.3969/j.issn.0258-2724.20180098
  • Received Date: 06 Mar 2018
  • Rev Recd Date: 27 Apr 2018
    • Available Online: 23 Feb 2019
  • Publish Date: 01 Jun 2019
    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.

     

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