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.

无人机的高度可行的轨迹规划在某些任务中非常重要，但尚未引起足够的研究关注。当前大多数研究使用具有一些状态约束的简化无人机模型来规划轨迹，但是可行性降低了，因为简化模型与实际无人机系统有很大差异，因此无法充分考虑无人机的跟踪特性。本文提出了一种新型的面向控制的无人机高度可行的轨迹规划方法。首先，在航迹规划阶段采用低水平控制器与UAV 6 DOF非线性模型相结合的无人机闭环模型预测方法来预测飞行轨迹。这种复杂的模型与实际的无人机系统非常相似，因为它综合考虑了控制器性能和详细的无人机模型，但是效率也很差。因此，提出了一种使用深度学习方法的轨迹映射网络（TMN），以提高规划效率。此外，提出了一种新颖的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计划成本。这样，计划的轨迹将是高度可行的。仿真结果证明了该方法的有效性。提出了一种使用深度学习方法的轨迹映射网络（TMN），以提高规划效率。此外，提出了一种新颖的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计划成本。这样，计划的轨迹将是高度可行的。仿真结果证明了该方法的有效性。提出了一种使用深度学习方法的轨迹映射网络（TMN），以提高规划效率。此外，提出了一种新颖的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计划成本。这样，计划的轨迹将是高度可行的。仿真结果证明了该方法的有效性。