论文标题
基于样条细分
Robust & Asymptotically Locally Optimal UAV-Trajectory Generation Based on Spline Subdivision
论文作者
论文摘要
由于避免碰撞和驱动限制的非凸约限制，生成本地最佳的无人机 - trajectories是具有挑战性的。我们提出了第一个基于局部优化的UAV-Trajectory Generator，同时保证了已知环境的有效性和渐近最佳性。 \ textit {有效性：}给定一个可行的初始猜测，我们的算法保证了在整个优化过程中对所有约束的满意度。 \ textIt {渐近最优性：}我们使用渐近的轨迹近似近似，并自动可调节其离散化分辨率。该轨迹在细化下收敛到确切的非凸编程问题的一阶固定点。我们的方法具有额外的实际优势，包括在轨迹和时间分配方面的关节最优性，以及对充满挑战环境的鲁棒性，如我们的实验中所证明的那样。
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.
