Generating locally optimal UAV-trajectories is challenging due to the non-convex constraints of collision avoidance and actuation limits. We present the first local, optimization-based UAV-trajectory generator that simultaneously guarantees validity and asymptotic optimality. \textit{Validity:} Given a feasible initial guess, our algorithm guarantees the satisfaction of all constraints throughout the process of optimization. \textit{Asymptotic Optimality:} We use a conservative piecewise approximation of the trajectory with automatically adjustable resolution of its discretization. The trajectory converges under refinement to the first-order stationary point of the exact non-convex programming problem. Our method has additional practical advantages including joint optimality in terms of trajectory and time-allocation, and robustness to challenging environments as demonstrated in our experiments.

翻译：生成当地最佳的无人驾驶飞行器轨道具有挑战性,因为避免碰撞和起动限制存在非混凝土的限制。我们提出了第一个本地的、基于优化的无人驾驶飞行器轨道生成器,该生成器同时保证有效性和无药可循的最佳性。\ textit{validity:}根据可行的初步猜测,我们的算法保证了在优化过程中满足所有制约因素。\textit{Asymptatic 最佳性:}我们使用一种稳妥的轨迹近似,并自动调整其离散性分辨率。轨迹正在细化中,与精确的非convex编程问题的第一阶定点相交汇。我们的方法还有其他实际优势,包括在轨迹和时间定位方面共同达到最佳性,以及对我们实验中显示的具有挑战性的环境保持稳健健。