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无人机反应式扰动流体路径规划

吴健发 王宏伦 王延祥 刘一恒

吴健发, 王宏伦, 王延祥, 刘一恒. 无人机反应式扰动流体路径规划. 自动化学报, 2021, 47(x): 1−16 doi: 10.16383/j.aas.c210231
引用本文: 吴健发, 王宏伦, 王延祥, 刘一恒. 无人机反应式扰动流体路径规划. 自动化学报, 2021, 47(x): 1−16 doi: 10.16383/j.aas.c210231
Wu Jian-Fa, Wang Hong-Lun, Wang Yan-Xiang, Liu Yi-Heng. UAV reactive interfered fluid path planning. Acta Automatica Sinica, 2021, 47(x): 1−16 doi: 10.16383/j.aas.c210231
Citation: Wu Jian-Fa, Wang Hong-Lun, Wang Yan-Xiang, Liu Yi-Heng. UAV reactive interfered fluid path planning. Acta Automatica Sinica, 2021, 47(x): 1−16 doi: 10.16383/j.aas.c210231

无人机反应式扰动流体路径规划

doi: 10.16383/j.aas.c210231
基金项目: 国家自然科学基金(62173022, 61673042, 61175084)资助
详细信息
    作者简介:

    吴健发:北京控制工程研究所博士后. 主要研究方向为飞行器智能决策与协同控制. E-mail: jianfa_wu@163.com

    王宏伦:北京航空航天大学自动化科学与电气工程学院教授. 主要研究方向为飞行器自主与智能控制、抗扰动控制、无人系统路径规划与精确跟踪. 本文通信作者. E-mail: hl_wang_2002@126.com

    王延祥:北京航空航天大学自动化科学与电气工程学院博士研究生. 主要研究方向为无人机路径规划, 空中加油精准引导与控制. E-mail: wyxjy51968@163.com

    刘一恒:北京航空航天大学自动化科学与电气工程学院博士研究生. 主要研究方向为飞行控制, 轨迹规划, 机器学习. E-mail: 18810010709@163.com

UAV Reactive Interfered Fluid Path Planning

Funds: Supported by National Natural Science Foundation of China (62173022, 61673042, 61175084)
More Information
    Author Bio:

    WU Jian-Fa Postdoctor at the Beijing Institute of Control Engineering. His research interest covers intelligent decision-making and coordinated control of flight vehicles

    WANG Hong-Lun Professor at the School of Automation Science and Electrical Engineering, Beihang University. His research interest covers autonomous and intelligent control of flight vehicles, anti-disturbance control, and path planning and precise tracking control of unmanned systems

    WANG Yan-Xiang Ph.D. candidate at the School of Automation Science and Electrical Engineering, Beihang University. His research interest covers UAV path planning and precision guidance and control of air refueling

    LIU Yi-Heng Ph.D. candidate at the School of Automation Science and Electrical Engineering, Beihang University. His research interest covers flight control, trajectory planning, and machine learning

  • 摘要: 针对复杂三维障碍环境, 提出一种基于深度强化学习的无人机反应式扰动流体路径规划架构. 该架构以一种受约束扰动流体动态系统算法作为路径规划的基本方法, 根据无人机与各障碍的相对状态以及障碍物类型, 通过经深度确定性策略梯度算法训练得到的动作网络在线生成对应障碍的反应系数和方向系数, 继而可计算相应的总和扰动矩阵并以此修正无人机的飞行路径, 实现反应式避障. 此外, 还研究了与所提路径规划方法相适配的深度强化学习训练环境规范性建模方法. 仿真结果表明, 在路径质量大致相同的情况下, 所提方法在实时性方面明显优于基于预测控制的在线路径规划方法.
  • 图  1  不同反应系数和方向系数组合对规划路径的影响

    Fig.  1  Effects of different combinations of reaction coefficients and direction coefficients on planned paths

    图  2  所提反应式路径规划的DDPG训练机制

    Fig.  2  DDPG training mechanism of the proposed reaction path planning

    图  3  评价网络和动作网络结构

    Fig.  3  Structures of critic networks and actor networks

    图  4  基于深度强化学习的反应式扰动流体路径规划总体流程图

    Fig.  4  Overview flow chart of the DRL-based reaction interfered fluid path planning

    图  5  无人机相对初始位置设定的差异: 以静态半球体障碍和动态球体威胁为例

    Fig.  5  Differences in the setting of UAV initial locations: Taking the static hemispherical obstacle and the dynamic spherical threat as examples

    图  6  针对静态半球体障碍的无人机反应式路径规划训练环境

    Fig.  6  Training environment of UAV reaction path planning for static hemispherical obstacles

    图  7  初始$ {\theta _h} $的概率分布

    Fig.  7  Probability distribution of the initial $ {\theta _h} $

    图  8  采用IFDS和C-IFDS时部分受约束的状态和规划路径的对比情况

    Fig.  8  Comparisons of some constrained states and planned paths when using IFDS and C-IFDS

    图  9  DDPG训练过程中的奖励函数情况

    Fig.  9  Reward functions in the DDPG training process

    图  10  案例2中在线规划的三维路径

    Fig.  10  3D online planned paths in Case. 2

    图  11  案例2中与动态威胁等效表面的最近距离

    Fig.  11  Closest distances to the equivalent surface of the dynamic threat in Case. 2

    图  12  案例2中规划时间对比

    Fig.  12  Comparison of the planning time in Case. 2

    图  13  未采用所提环境建模方法时DDPG训练过程中的奖励函数情况: 以静态半球体障碍为例

    Fig.  13  Reward functions in the DDPG training process when the proposed environment modeling method is not adopted: Taking the static hemispherical obstacle as an example

    图  14  案例3中不同时刻无人机的航迹与规划路径

    Fig.  14  UAV flight paths and planned paths at different times in Case. 3

    图  15  案例3中与各动态威胁等效表面的最近距离

    Fig.  15  Closest distances to the equivalent surface of each dynamic threat in Case. 3

    表  1  案例2中规划路径长度和平滑性指标对比

    Table  1  Comparison of the length and the smooth indexes for planned paths in Case. 2

    指标本文方法对比项1对比项2对比项3
    长度(km)7.568.497.687.65
    平滑性0.13180.35060.15930.1528
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-29
  • 录用日期:  2021-09-17
  • 网络出版日期:  2021-10-15

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