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首页> 外文期刊>Aerospace science and technology >Control-oriented UAV highly feasible trajectory planning: A deep learning method
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Control-oriented UAV highly feasible trajectory planning: A deep learning method

机译：面向控制的无人机高度可行的轨迹规划：深度学习方法

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摘要

The highly feasible trajectory planning of unmanned aerial vehicle (UAV) is very important in some tasks but has not yet attracted sufficient study attention. Most current studies use simplified UAV model with some state constraints to plan the trajectory, but the feasibility is reduced, because the simplified model is very different from the actual UAV system, so that the tracking characteristics of UAV cannot be fully considered. In this paper, a novel control-oriented UAV highly feasible trajectory planning method is proposed. First, a UAV closed-loop model prediction method, which is the combination of a low-level controller and a UAV 6 DOF nonlinear model, is adopted in the trajectory planning phase to predict the flight trajectory. This complicated model is very similar to the actual UAV system because it comprehensively considers the controller performance and the detailed UAV model, but it also has poor efficiency. Therefore, a trajectory-mapping network (TMN) is proposed using a deep learning approach to improve the planning efficiency. Furthermore, a novel time-series convolutional neural network (TSCNN) is proposed for the TMN to further improve its computation speed and prediction accuracy. Finally, the flight trajectory predicted by the TMN is used to evaluate the planning cost. In this way, the planned trajectory will be highly feasible. The effectiveness of the proposed method is demonstrated by simulations. (C) 2020 Elsevier Masson SAS. All rights reserved.
机译：无人驾驶飞行器（UAV）的高度可行的轨迹规划在一些任务中非常重要，但尚未吸引足够的研究。大多数当前研究使用简化的UAV模型具有一些状态约束来规划轨迹，但可行性降低，因为简化模型与实际的UAV系统非常不同，因此无法完全考虑UAV的跟踪特性。本文提出了一种新型控制的无人机的无人机高度可行的轨迹规划方法。首先，在轨迹规划阶段采用了一种UAV闭环模型预测方法，该预测方法是低级控制器和UAV 6 DOF非线性模型的组合，以预测飞行轨迹。这种复杂的模型与实际的UAV系统非常相似，因为它全面考虑了控制器性能和详细的UAV模型，但效率也很差。因此，使用深度学习方法提出了一种轨迹映射网络（TMN）来提高规划效率。此外，提出了一种新型时间序列卷积神经网络（TSCNN），用于TMN以进一步提高其计算速度和预测精度。最后，TMN预测的飞行轨迹用于评估规划成本。通过这种方式，计划的轨迹将是非常可行的。通过模拟证明了所提出的方法的有效性。 （c）2020 Elsevier Masson SAS。版权所有。

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  • 来源
    《Aerospace science and technology》 |2021年第3期|106435.1-106435.11|共11页
  • 作者

    Liu Yiheng; Wang Honglun; Fan Jiaxuan; Wu Jianfa; Wu Tiancai;

  • 作者单位

    Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China|Beihang Univ Shenyuan Honors Coll Beijing 100191 Peoples R China|Beihang Univ Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China;

    Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China|Beihang Univ Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China;

    China Acad Launch Vehicle Technol Res & Dev Ctr Beijing 100071 Peoples R China;

    Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China|Beihang Univ Shenyuan Honors Coll Beijing 100191 Peoples R China|Beihang Univ Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China;

    Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China|Beihang Univ Shenyuan Honors Coll Beijing 100191 Peoples R China|Beihang Univ Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Highly feasible trajectory planning; Trajectory-mapping network; Deep learning; Unmanned aerial vehicle;

    机译：高度可行的轨迹规划;轨迹映射网络;深度学习;无人驾驶飞行器;
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