计算机应用 ›› 2015, Vol. 35 ›› Issue (7): 2093-2095.DOI: 10.11772/j.issn.1001-9081.2015.07.2093

• 行业与领域应用 • 上一篇    下一篇

基于多点协作的团队出行路径优化算法

邱吉刚1,2, 李汶隆1, 杨佳1   

  1. 1. 四川九洲电器集团责任有限公司 通信技术所, 成都 610041;
    2. 清华大学 电子工程系, 北京 100083
  • 收稿日期:2015-02-11 修回日期:2015-03-21 出版日期:2015-07-10 发布日期:2015-07-17
  • 通讯作者: 邱吉刚(1976-),男,四川南溪人,博士,主要研究方向:无线通信、物联网,380431810@qq.com
  • 作者简介:李汶隆(1981-),男,四川南充人,主要研究方向:无线通信、移动互联网; 杨佳(1987-),女,四川广安人,硕士,主要研究方向:物联网、计算机网络。
  • 基金资助:

    国家发改委应用示范项目(2013GZX0037)。

Path optimization algorithm for team navigation based on multiple point collaboration

QIU Jigang1,2, LI Wenlong1, YANG Jia1   

  1. 1. Telecommunication Technology Institute, Sichuan Jiuzhou Electric Group Company Limited, Chengdu Sichuan 610041, China;
    2. Department of Electronic Engineering, Tsinghua University, Beijing 100083, China
  • Received:2015-02-11 Revised:2015-03-21 Online:2015-07-10 Published:2015-07-17

摘要:

针对团队出行过程中因信息孤岛导致出行路径非优化和延时等待等问题,提出了一种以团队成员信息共享为基础,以集中式计算为手段的协作式路径优化算法。该算法统筹考虑成员间会合的便捷性、路径/时间最短化等多种因素基础上,通过引入团队会合优先度因子对路径计算进行加权处理,从而实现整个团队出行路径的最优化。理论分析表明,协作式路径优化算法的计算复杂度随团队成员的数量线性增长,与传统的最短路径算法计算复杂度基本相当。仿真结果表明,会合优先度因子值的高低,将会影响会合点及出行路径的选择,因此,可根据实际需求设置会合优先度因子,实现团队会合和路径最短化的动态均衡。最后,以协作式路径优化算法的一个具体的工程应用,阐述团队成员间如何提供支持和帮助,从而安全、高效和有序地到达目的地。

关键词: 最短路径, 协作式导航, 团体出行, 动态导航, 路径优化, 信息共享

Abstract:

Concerning the path non-optimization and the delay due to mutual-waiting caused by information island in the team travel, a collaborative path optimization algorithm was proposed which employed centralized computing based on information sharing among team members. The algorithm calculated the optimum navigation path weighted by the factor of meeting priority, taking meeting convenience and path/time shortening into overall consideration. Theoretical analysis shows that the computation complexity increases linearly with the number of team members, and is approximately equal to that of the traditional path optimization algorithm. The simulation results show that the factor of meeting priority has a great influence on optimization path and meeting place. So, the factor of meeting priority needs to be set according to the actual requirement to ensure the dynamic balance between team cooperation and shortening path. A typical application solution of collaborative path optimization algorithm was given to illustrate how to support and to help each other among team members, and to travel together to the destination in order, safely and quickly.

Key words: shortest path, collaborative navigation, team travel, dynamic navigation, path optimization, information sharing

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