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 the validity and asymptotic optimality for known environments. \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 an asymptotic exact piecewise approximation of the trajectory with an 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机轨道由于避免碰撞和启动限制的非混凝土限制而具有挑战性。 我们展示了第一个本地的、基于优化的无人机轨道生成器,该生成器同时保证已知环境的有效性和无症状的最佳性。\ textit{ 有效性:}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 优化性:}我们使用轨迹的零星精确近似法,自动调整其离散性。该轨迹正在与准确的非convex编程问题的第一阶定点相融合。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的共同最佳性,以及我们实验中显示的对挑战性环境的稳健性。