针对城市环境非规则障碍密布与固定翼无人机动力学强非线性等因素导致轨迹规划效率低、安全性难保证的问题，开展基于微分平坦的固定翼无人机时空分层轨迹规划算法研究。构建“路径引导-走廊约束-轨迹优化”分层规划框架，通过启发式图搜索规划复杂障碍环境下的可行路径，引导区域膨胀迭代快速生成安全飞行走廊，高效确定轨迹初值与可行域；建立固定翼无人机的微分平坦轨迹时空参数化模型，将轨迹规划问题转换为求解广义时空变量，消除非线性动力学、始末终端、安全走廊等约束，降低问题复杂度；通过定制飞行性能约束罚函数并推导解析梯度，将轨迹规划问题转化为梯度解析的无约束非线性优化问题；提出基于微分平坦的时空分层轨迹规划算法，实现飞行轨迹的快速求解。仿真结果表明，所提算法能够在复杂城市障碍环境下快速生成安全的飞行轨迹，求解耗时在10^-2~10^-1 s量级，满足轨迹规划的在线求解需求。

The presence of dense irregular obstacles in complex urban environments and strong nonlinear dynamics of fixed-wing Unmanned Aerial Vehicle （UAV） cause low efficiency of trajectory planning and safety risks for fixed-wing UAVs. To address these issues， this paper investigates a spatial-temporal hierarchical trajectory planning method for fixed-wing UAVs based on differential flatness. A hierarchical planning framework of “path planning-safe corridors generation-trajectory optimization” is constructed. Firstly， heuristic graph search is utilized to obtain the reference path as the initial motion. Guided by the path planning results， safe flight corridors are generated through the iterative regional inflation method， which efficiently provides the initial values and feasible regions of trajectories. Subsequently， to reduce the complexity of trajectory optimization， a differential flatness-based spatial-temporal trajectory parameterization model for fixed-wing UAVs is established， enabling elimination of nonlinear dynamics， and terminal and safety constraints. Additionally， by designing penalty costs to handle flight performance constraints and deriving their analytical gradients， the original problem of trajectory planning is transformed into an unconstrained nonlinear optimization with analytical gradients. Finally， a Differential Flatness-Based Spatial-Temporal Hierarchical Trajectory Planning （STH-DFTP） algorithm is proposed to achieve efficient trajectory generation. The simulation results illustrate that the proposed STH-DFTP has superior efficiency， which only takes 10^-2–10^-1 s to generate the flight trajectories for fixed-wing UAVs in complex urban environments， meeting the requirements for online trajectory planning in practice.