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应用于无人机全局航迹规划的改进双向RRTs算法

姜香菊 黄炳德 杨潇洁

姜香菊,黄炳德,杨潇洁. 应用于无人机全局航迹规划的改进双向RRTs算法[J]. 机械科学与技术,2024,43(5):897-903 doi: 10.13433/j.cnki.1003-8728.20220280
引用本文: 姜香菊,黄炳德,杨潇洁. 应用于无人机全局航迹规划的改进双向RRTs算法[J]. 机械科学与技术,2024,43(5):897-903 doi: 10.13433/j.cnki.1003-8728.20220280
JIANG Xiangju, HUANG Bingde, YANG Xiaojie. An Improved Bidirectional RRTs Algorithm for UAV Global Path Planning[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(5): 897-903. doi: 10.13433/j.cnki.1003-8728.20220280
Citation: JIANG Xiangju, HUANG Bingde, YANG Xiaojie. An Improved Bidirectional RRTs Algorithm for UAV Global Path Planning[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(5): 897-903. doi: 10.13433/j.cnki.1003-8728.20220280

应用于无人机全局航迹规划的改进双向RRTs算法

doi: 10.13433/j.cnki.1003-8728.20220280
基金项目: 国家自然科学基金项目(72171106)
详细信息
    作者简介:

    姜香菊,副教授,硕士生导师,63411721@qq.com

  • 中图分类号: V279

An Improved Bidirectional RRTs Algorithm for UAV Global Path Planning

  • 摘要: 针对无人机复杂环境下的全局航迹规划问题,将人工势场法与双向RRTs(Rapidly-exploring random trees)算法结合,提出一种改进双向RRTs算法。首先,目标偏置策略引导采样点以一定概率顺着目标点生成,同时随机树新节点受到障碍物斥力和目标点引力的合力影响有效避开障碍物生长,提高航迹搜寻效率,其次对随机树的节点扩展考虑了无人机飞行性能约束条件,最后采用3阶贝塞尔函数进一步航迹优化。仿真结果表明:二维和三维复杂环境中改进双向RRTs算法相比传统RRT、双向RRTs算法航迹搜索耗时减少了71.3%、24.7%和41.0%、18.6%,验证了改进算法全局搜索能力的快速性和有效性,能很好的应用于无人机离线全局航迹规划场合。
  • 图  1  双向RRTs算法原理图

    Figure  1.  Principles of bidirectional RRTs algorithm

    图  2  改进算法节点生长原理

    Figure  2.  Improved algorithm node growth principle

    图  3  变步长策略示意图

    Figure  3.  Variable step size strategy diagram

    图  4  无人机飞行性能约束

    Figure  4.  UAV flight performance constraints

    图  5  航迹平滑示意图

    Figure  5.  Track smoothing diagram

    图  6  改进算法流程图

    Figure  6.  Improved algorithm flow chart

    图  7  复杂阶梯障碍物环境仿真

    Figure  7.  Complex ladder barrier environment simulation

    图  8  复杂方形障碍物环境仿真

    Figure  8.  Complex square environment simulation

    图  9  三维复杂环境仿真

    Figure  9.  3D complex environment simulation

    表  1  复杂阶梯障碍物环境算法性能对比

    Table  1.   Complex ladder environment algorithm comparison

    算法 l/m t/s n/个
    文献[14] 1327.19 30.26 535
    文献[15] 1229.21 16.43 428
    改进双向RRTs 1021.83 13.18 209
    下载: 导出CSV

    表  2  复杂方形障碍物环境算法性能对比

    Table  2.   Complex square environment algorithm comparison

    算法 l/m t/s n/个
    文献[14] 864.33 24.61 429
    文献[15] 811.16 9.38 202
    改进双向RRTs 750.92 7.06 113
    下载: 导出CSV

    表  3  三维复杂环境算法性能对比

    Table  3.   Complex 3D environment algorithm performance comparison

    算法 l/m t/s n/个
    文献[14] 418.93 17.55 259
    文献[15] 407.36 12.73 158
    改进双向RRTs 331.70 10.36 78
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-18
  • 刊出日期:  2024-05-31

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