留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

不确定风场下平流层浮空器全局路径规划

翟嘉琪 杨希祥 邓小龙 龙远 张经伦 柏方超

翟嘉琪,杨希祥,邓小龙,等. 不确定风场下平流层浮空器全局路径规划[J]. 北京航空航天大学学报,2023,49(5):1116-1126 doi: 10.13700/j.bh.1001-5965.2021.0380
引用本文: 翟嘉琪,杨希祥,邓小龙,等. 不确定风场下平流层浮空器全局路径规划[J]. 北京航空航天大学学报,2023,49(5):1116-1126 doi: 10.13700/j.bh.1001-5965.2021.0380
ZHAI J Q,YANG X X,DENG X L,et al. Global path planning of stratospheric aerostat in uncertain wind field[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(5):1116-1126 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0380
Citation: ZHAI J Q,YANG X X,DENG X L,et al. Global path planning of stratospheric aerostat in uncertain wind field[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(5):1116-1126 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0380

不确定风场下平流层浮空器全局路径规划

doi: 10.13700/j.bh.1001-5965.2021.0380
基金项目: 国家自然科学基金(61903369);湖南省自然科学基金(2018JJ3587,2017JJ3590);国家部委基金(20191A0X0233)
详细信息
    通讯作者:

    E-mail:kyangxixiang@163.com

  • 中图分类号: V274

Global path planning of stratospheric aerostat in uncertain wind field

Funds: National Natural Science Foundation of China (61903369); Natural Science Foundation of Hunan Province (2018JJ3587,2017JJ3590); National Ministry Fund of China (20191A0X0233)
More Information
  • 摘要:

    平流层浮空器是开展临近空间应用的重要平台,其在不确定风场中的路径规划是开展应用的关键。研究对象是具有一定水平驱动能力的平流层浮空器,针对其低动态、大尺寸及易受风场影响等特点,基于马尔可夫决策过程(MDP)提出一种浮空器在不确定风场作用下的二维全局路径规划方法,寻找由当前位置快速部署到目标区域所需时间最短的最优路径。由于风场中可能存在误差,将风场不确定性引入MDP模型中,设计相关模型参数,建立确定风场和不确定风场2种环境模型。通过仿真,分析具有不同驱动能力的浮空器在2种风场模型下的区域可达性、最优路径和最优动作序列的选择,结果表明:最优路径和最优动作会根据起始点/目标点位置、浮空器水平驱动能力及风场模型的不同发生比较大的变化;浮空器的水平驱动能力越强,区域可达性越高,2种风场模型对浮空器的二维全局路径规划影响的差异也随之减弱。

     

  • 图 1  区域离散化示意图

    Figure 1.  Diagram of regional discretization

    图 2  浮空器状态转移方向

    Figure 2.  Transition direction of a stratospheric aerostat

    图 3  MDP模型

    Figure 3.  Model of Markov decision processes

    图 4  双线性差值法示意图

    Figure 4.  Diagram of bilinear interpolation method

    图 5  二维平面权值确定示意图

    Figure 5.  Diagram of determination of two-dimensional plane weights

    图 6  不同高度的二维风场分布对比

    Figure 6.  Comparison of two-dimensional wind field distribution at different heights

    图 7  不确定模型下风向和风速概率分布

    Figure 7.  Probability distribution of wind direction and speed under uncertain model

    图 8  浮空器基本动作方向

    Figure 8.  Basic action direction of stratospheric aerostat

    图 9  不确定风场下浮空器转移概率分布

    Figure 9.  Transition probability distribution of stratospheric aerostate under uncertain model

    图 10  全局路径规划流程

    Figure 10.  Flowchart of global path planning

    图 11  确定风场下不同最大抗风能力期望到达时间分布

    Figure 11.  Distribution of expected arrival time at different speeds in certain wind field

    图 12  不确定风场下不同速度期望到达时间分布

    Figure 12.  Distribution of expected arrival time at different speeds in uncertain wind field

    图 13  确定风场下路径规划和最优动作序列

    Figure 13.  Path planning and optimal velocity sequence in certain wind field

    图 14  不确定风场下路径规划和最优动作序列

    Figure 14.  Path planning and optimal velocity sequence in uncertain wind field

  • [1] BELMONT A D, DARTT D G, NASTROM G D. Variations of stratospheric zonal winds, 20-65km, 1961-1971[J]. Journal of Applied Meterology, 2010, 14(4): 585-594.
    [2] 洪延姬. 临近空间飞行器技术[M]. 北京: 国防工业大学出版社, 2012: 9-12.

    HONG Y J. Aircraft technology of near space[M]. Beijing: National University of Defense Technology Press, 2012: 9-12 (in Chinese).
    [3] 黄宛宁, 李颖思, 周书宇, 等. 现代浮空器军事应用[J]. 科技导报, 2017, 35(15): 20-27.

    HUANG W N, LI Y S, ZHOU S Y, et al. Military applications of modern lighter-than-air vehicles[J]. Science and Technology Innovation Herald, 2017, 35(15): 20-27(in Chinese).
    [4] 杨希祥, 杨晓伟, 邓小龙. 反步法与神经网络融合的平流层飞艇轨迹鲁棒控制方法[J]. 宇航学报, 2021, 42(3): 351-358. doi: 10.3873/j.issn.1000-1328.2021.03.010

    YANG X X, YANG X W, DENG X L. Robust trajectory control method for stratospheric airships with combination of backstepping and neural network[J]. Journal of Astronautics, 2021, 42(3): 351-358(in Chinese). doi: 10.3873/j.issn.1000-1328.2021.03.010
    [5] 邓小龙, 丛伟轩, 李魁, 等. 风场综合利用的新型平流层浮空器轨迹设计[J]. 宇航学报, 2019, 40(7): 748-757.

    DENG X L, CONG W X, LI K, et al. Trajectory design of a novel stratospheric aerostat based on comprehensive utilization of wind fields[J]. Journal of Astronautics, 2019, 40(7): 748-757(in Chinese).
    [6] JIANG Y, LV M, QU Z, et al. Performance evaluation for scientific balloon station-keeping strategies considering energy management strategy[J]. Renewable Energy, 2020, 156: 290-302. doi: 10.1016/j.renene.2020.04.011
    [7] 林康, 马云鹏, 郑泽伟, 等. 基于副气囊的平流层浮空器高度控制研究[J]. 北京航空航天大学学报, 2021, 47(7): 1-13.

    LIN K, MA Y P, ZHENG Z W, et al. Height control of stratospheric aerostat based on secondary airbag[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(7): 1-13(in Chinese).
    [8] JIANG Y, LV M, LI J. Station-keeping control design of double bal loon system based on horizontal region constraints[J]. Aerospace Science and Technology, 2020, 100: 105792. doi: 10.1016/j.ast.2020.105792
    [9] 李智斌, 黄宛宁, 张钊, 等. 2020年临近空间科技热点回眸[J]. 科技导报, 2021, 39(1): 54-68.

    LI Z B, HUANG W N, ZHANG Z, et al. Summary of the hot spots of near space science and technology in 2020[J]. Science & Technology Review, 2021, 39(1): 54-68 (in Chinese).
    [10] BELLEMARE M G, CANDIDO S, CASTRO P S, et al. Autonomous navigation of stratospheric balloons using reinforcement learning[J]. Nature, 2020, 588(7836): 77-82. doi: 10.1038/s41586-020-2939-8
    [11] 孙玉山, 王力锋, 吴菁, 等. 智能水下机器人路径规划方法综述[J]. 舰船科学技术, 2020, 4(1): 1-7. doi: 10.3404/j.issn.1672-7649.2020.01.001

    SUN Y S, WANG L F, WU J, et al. A general overview of path planning methods for autonomous underwater vehicle[J]. Ship Science and Technology, 2020, 4(1): 1-7(in Chinese). doi: 10.3404/j.issn.1672-7649.2020.01.001
    [12] SUN J, TANG J, LAO J. Collision avoidance for cooperative UAVs with optimized artificial potential field algorithm[J]. IEEE Access, 2017, 5: 18382-18390. doi: 10.1109/ACCESS.2017.2746752
    [13] 黄东晋, 蒋晨凤, 韩凯丽. 基于深度强化学习的三维路径规划算法[J]. 计算机工程与应用, 2020, 56(15): 30-36.

    HUANG D J, JIANG C F, HAN K L. 3D path planning algorithm based on deep reinforcement learning[J]. Computer Engineering and Applications, 2020, 56(15): 30-36(in Chinese).
    [14] TAI L, LIU M. Towards cognitive exploration through deep reinforcement learning for mobile robots[J]. IEEE IROS, 2016: 465-488.
    [15] WOLF M T, BLACKMORE L, KUWATA Y, et al. Probabilistic motion planning of balloons in strong, uncertain wind fields[C]//2010 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2010: 1123-1129.
    [16] 陈魁. 基于马尔可夫决策过程的码垛机器人路径规划研究 [D]. 南京: 南京航空航天大学, 2012.

    CHEN K. Research on path planning based on Markov decision processes for palletizing robot [D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2012 (in Chinese).
    [17] YU X, ZHOU X, ZHANG Y. Collision-free trajectory generation and tracking for UAVs using Markov decision process in a cluttered environment[J]. Journal of Intelligent & Robotic Systems, 2019, 93(1): 17-32.
    [18] KULARATNE D, HAJIEGHRARY H, HSIEH M A. Optimal path planning in time-varying flows with forecasting uncertainties[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE Press, 2018: 4857-4864.
    [19] NANAZ F, LARS B, YOSHIAKI K, et al. Feasibility studies on guidance and global path planning for wind-assisted montgolfière in titan[J]. IEEE Systems Journal, 2013, 8(4): 1112-1125.
    [20] PASHENKOVA E, RISH I, DECHTER R. Value iteration and policy iteration algorithms for Markov decision problem[C]//Department of Information and Computer Science. Irvine: University of Salifornia, 1996.
    [21] SUTTON R S, BARTO A G. Reinforcement learning: An introduction [M]. Cambridge: The MIT Press, 2018: 33-35.
  • 加载中
图(14)
计量
  • 文章访问数:  324
  • HTML全文浏览量:  73
  • PDF下载量:  34
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-07-08
  • 录用日期:  2021-08-09
  • 网络出版日期:  2021-09-28
  • 整期出版日期:  2023-05-31

目录

    /

    返回文章
    返回
    常见问答