Due to the strong nonlinearity and nonholonomic dynamics, despite that various general trajectory optimization methods have been presented, few of them can guarantee efficient compu-tation and physical feasibility for relatively complicated fixed-wing UAV dynamics. Aiming at this issue, this paper investigates a differential flatness-based trajectory optimization method for fixed-wing UAVs (DFTO-FW), which transcribes the trajectory optimization into a lightweight, unconstrained, gradient-analytical optimization with linear time complexity in each itera-tion to achieve fast trajectory generation. Through differential flat characteristics analysis and polynomial parameterization, the customized trajectory representation is presented, which implies the equality constraints to avoid the heavy computational burdens of solving complex dynamics. Through the design of integral performance costs and deduction of analytical gradients, the original trajectory optimization is transcribed into an uncon-strained, gradient-analytical optimization with linear time com-plexity to further improve efficiency. The simulation experi-ments illustrate the superior efficiency of the DFTO-FW, which takes sub-second CPU time against other competitors by orders of magnitude to generate fixed-wing UAV trajectories in ran-domly generated obstacle environments.

翻译：由于固定翼无人机具有强非线性和非完整动力学特性，尽管已有多种通用轨迹优化方法被提出，但鲜有方法能针对相对复杂的固定翼无人机动力学模型同时保证高效计算与物理可行性。针对这一问题，本文研究了一种基于微分平坦度的固定翼无人机轨迹优化方法（DFTO-FW），该方法将轨迹优化问题转化为一种轻量级、无约束、具有梯度解析形式的优化问题，其每次迭代具有线性时间复杂度，从而实现快速轨迹生成。通过微分平坦特性分析与多项式参数化，提出了定制化的轨迹表示方法，该方法隐含了等式约束，避免了求解复杂动力学模型带来的沉重计算负担。通过设计积分性能代价函数并推导解析梯度，将原始轨迹优化问题转化为无约束、梯度解析的优化问题，并具有线性时间复杂度，从而进一步提升效率。仿真实验表明，DFTO-FW 具有卓越的计算效率，在随机生成的障碍物环境中生成固定翼无人机轨迹时，其CPU耗时仅为亚秒级，较其他对比方法快数个数量级。