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Real-Time Quadrotor Trajectory Optimization with Time-Triggered Corridor Constraints

Computing Research Repository (CoRR)(2023)

Univ Texas Austin | Univ Washington

Cited 1|Views11
Abstract
One of the keys to flying quadrotors is to optimize their trajectories within the set of collision-free corridors. These corridors impose nonconvex constraints on the trajectories, making real-time trajectory optimization challenging. We introduce a novel numerical method that approximates the nonconvex corridor constraints with time-triggered convex corridor constraints. This method combines bisection search and repeated infeasibility detection. We further develop a customized C++ implementation of the proposed method, based on a first-order conic optimization method that detects infeasibility and exploits problem structure. We demonstrate the efficiency and effectiveness of the proposed method using numerical simulation on randomly generated problem instances as well as indoor flight experiments with hoop obstacles. Compared with mixed integer programming, the proposed method is about 50–200 times faster.
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Optimal Motion Planning,Aircraft Scheduling,Real-Time Planning,Delay Prediction,Path Planning
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要点】:本文提出了一种针对基于WiFi的人体活动识别(HAR)的目标导向半监督域适应方法(TOSS),通过有效利用标记和未标记的目标样本,显著提高了模型在目标环境中的适应性。

方法】:本文采用了一种结合了标记和未标记样本的目标导向半监督学习方法。

实验】:研究者使用了一种典型的元学习模型来实现TOSS,并在多个现实场景中的一对一和多源一次性域适应实验中对TOSS进行了广泛评估。结果显示,在综合实验中,TOSS在性能上大幅超越了现有先进方法。