Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the relationship between various wind conditions and its effect on aircraft maneuverability is not well understood, it is challenging to design effective robot controllers using traditional control design methods. We present Neural-Fly, a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly builds on two key observations that aerodynamics in different wind conditions share a common representation and that the wind-specific part lies in a low-dimensional space. To that end, Neural-Fly uses a proposed learning algorithm, domain adversarially invariant meta-learning (DAIML), to learn the shared representation, only using 12 minutes of flight data. With the learned representation as a basis, Neural-Fly then uses a composite adaptation law to update a set of linear coefficients for mixing the basis elements. When evaluated under challenging wind conditions generated with the Caltech Real Weather Wind Tunnel, with wind speeds up to 43.6 kilometers/hour (12.1 meters/second), Neural-Fly achieves precise flight control with substantially smaller tracking error than stateof-the-art nonlinear and adaptive controllers. In addition to strong empirical performance, the exponential stability of Neural-Fly results in robustness guarantees. Last, our control design extrapolates to unseen wind conditions, is shown to be effective for outdoor flights with only onboard sensors, and can transfer across drones with minimal performance degradation.

中文翻译：

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Neural-Fly 能够在强风中快速学习敏捷飞行

在动态高速风中执行安全和精确的飞行机动对于无人飞行器 (UAV) 的持续商品化非常重要。然而，由于各种风况之间的关系及其对飞机机动性的影响尚不清楚，因此使用传统的控制设计方法设计有效的机器人控制器具有挑战性。我们提出了 Neural-Fly，这是一种基于学习的方法，通过深度学习结合预训练的表示，可以实现快速的在线适应。Neural-Fly 建立在两个关键观察的基础上，即不同风条件下的空气动力学具有共同的表示，并且特定于风的部分位于低维空间中。为此，Neural-Fly 使用了一种提出的学习算法，领域对抗不变元学习 (DAIML)，学习共享表示，仅使用 12 分钟的飞行数据。以学习的表示为基础，Neural-Fly 然后使用复合自适应律来更新一组线性系数，以混合基础元素。当在加州理工学院真实天气风洞产生的具有挑战性的风条件下进行评估时，风速高达 43.6 公里/小时（12.1 米/秒），Neural-Fly 实现了精确的飞行控制，跟踪误差比最先进的非线性要小得多和自适应控制器。除了强大的经验性能外，Neural-Fly 的指数稳定性还可以保证稳健性。最后，我们的控制设计根据看不见的风条件进行推断，证明对于只有机载传感器的户外飞行是有效的，