测绘通报 ›› 2019, Vol. 0 ›› Issue (11): 26-30.doi: 10.13474/j.cnki.11-2246.2019.0346

• 路径优化算法及应用 • 上一篇    下一篇

顾及复杂环境约束的无人机三维航迹快速规划

唐俊   

  1. 中铁第四勘察设计院集团有限公司, 湖北 武汉 430063
  • 收稿日期:2019-04-25 修回日期:2019-07-02 发布日期:2019-12-02
  • 作者简介:唐俊(1994-),男,助理工程师,主要研究方向为地理信息系统。E-mail:vgetang@163.com
  • 基金资助:
    中国铁建股份有限公司重大专项(2019-A02);中铁第四勘察设计院集团有限公司科技研究开发计划(2019D082)

Fast three-dimensional path planning method for UAV considering complex environment constraints

TANG Jun   

  1. China Railway SiYuan Survey and Design Group Co., Ltd., Wuhan 430063, China
  • Received:2019-04-25 Revised:2019-07-02 Published:2019-12-02

摘要: 针对在地形、禁飞区、气象、危险物、通视约束等复杂环境下,无人机三维航迹规划存在效率差与精度低的问题,本文通过设计多层扩展A*算法,提出了一种顾及复杂环境约束的无人机三维航迹快速规划方法。该方法剖析典型复杂环境对无人机航迹的多种约束信息等,构建三维航迹规划环境模型;通过结合无人机自身性能约束,设计多层扩展A*算法进行三维分层扩展,搜索获取无人机最优参考航迹;采用线简化和线平滑的方式对参考航迹进行简化与优化;最后选择典型案例区域开展试验分析,证明本文方法能够快速准确地规划出复杂环境下的最优可行航线。

关键词: 无人机三维航迹, 复杂环境约束, 多层扩展A*算法, 快速规划, 航迹优化

Abstract: Aiming at the inefficiency and inaccuracy of UAV's three-dimensional path planning in complex environments such as terrain, no-fly zone, meteorology, dangerous object and visual constraints, the paper proposes a fast three-dimensional path planning method for UAV considering complex environmental constraints by designing a multi-layer extended A* algorithm. This method analyses the typical complex environments on the UAV path, and constructs a three-dimensional path planning environment model. By combining with the UAV's own performance constraints, a multi-layer extended A* algorithm is designed for three-dimensional hierarchical expansion to search for the optimal reference path of UAV. Line simplification and line smoothing are used to simplify and optimize the reference path. Finally, the typical case area is selected to carry out the experimental analysis, which proves that the proposed method can quickly and accurately plan the optimal feasible path in complex environment.

Key words: unmanned aerial vehicle 3D path, complex environment constraints, multi-layer extended A* algorithms, fast planning, path optimization

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