曲靖师范学院学报 ›› 2023, Vol. 42 ›› Issue (6): 38-44.

• 计算机科学研究 • 上一篇    下一篇

一种面向地震场景的无人机协同搜索路径规划算法

沈秀娟1, 曹媛丽1, 卫连1, 胡蝶2   

  1. 1.曲靖师范学院 数学与统计学院,云南 曲靖 655011;
    2.曲靖市第二小学,云南 曲靖 655000
  • 收稿日期:2023-09-04 出版日期:2023-11-26 发布日期:2023-12-07
  • 通讯作者: 胡 蝶,曲靖市第二小学教师,主要从事数学教学研究.
  • 作者简介:沈秀娟,曲靖师范学院数学与统计学院讲师,主要从事数理统计研究.
  • 基金资助:
    云南省教育厅科学研究项目“无人机协同搜索最优路径规划问题”(2023J1028)、“基于人脸特征的智能身份认证算法研究”(2023J1030).

A Path Planning Algorithm for Unmanned Aerial Vehicle Collaborative Search in Earthquake Scenarios

SHEN Xiujuan1, CAO Yuanli1, WEI Lian1, HU Die2   

  1. 1. School of Mathematics and Statistics, Qujing Normal University, Qujing Yunnan 655011;
    2. No. 2 Primary School of Qujing, Qujing Yunnan 655000, China
  • Received:2023-09-04 Published:2023-11-26 Online:2023-12-07

摘要: 面向地震场景,提出了多无人机协同搜索的路径规划算法,该方法是一种基于深度优先搜索和生成树思想改进的路径搜索优化算法.算法分为两步:第一步可以在指定步数约束条件下得到无人机所有的合理搜索路径;第二步可以在指定无人机数量的条件下得到搜索概率较大的规定路径组合数.把算法运用到抗震救灾理论模拟实验中,结果表明,所提出的路径搜索算法保证了路径的穷尽性、唯一性,在满足约束条件的搜索路径组合中,发现目标概率最大的路线主要覆盖人口密集的居民区,与实际相符.根据所得到的最优路径组合,无人机能够在指定时间内通过拍照返回更多的灾情现场信息,为搜救团队制定救援计划提供更加精准的方向.

关键词: 无人机协同, 最优搜索路线, 深度优先搜索, 生成树, 搜索概率

Abstract: For earthquake scenarios, the article proposes a path planning algorithm for multi drone collaborative search. This method is an improved path search optimization algorithm based on depth first search and spanning tree ideas. The algorithm is divided into two steps. The first step can obtain all reasonable search paths of UAV under the specified step constraint. The second step can obtain the specified path combination number with high search probability under the condition of the specified number of UAVs. The calculation results show that the proposed path search algorithm guarantees the exhaustiveness and uniqueness of the path. Among the search path combinations satisfying the constraint conditions, the route with the highest target probability is found to mainly cover the densely populated residential areas, which is consistent with the reality. Moreover, through experimental simulation, the UAV can return more disaster site information by taking photos within the specified time, providing more accurate direction for the search and rescue team to make rescue plans.

Key words: unmanned aerial vehicle coordination, optimal search path, depth-first search, spanning tree, searching probability

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