Accurate prediction of aerodynamic forces in real-time is crucial for
autonomous navigation of unmanned aerial vehicles (UAVs). This paper presents a
data-driven aerodynamic force prediction model based on a small number of
pressure sensors located on the surface of UAV. The model is built on a linear
term that can make a reasonably accurate prediction and a nonlinear correction
for accuracy improvement. The linear term is based on a reduced basis
reconstruction of the surface pressure distribution, where the basis is
extracted from numerical simulation data and the basis coefficients are
determined by solving linear pressure reconstruction equations at a set of
sensor locations. Sensor placement is optimized using the discrete empirical
interpolation method (DEIM). Aerodynamic forces are computed by integrating the
reconstructed surface pressure distribution. The nonlinear term is an
artificial neural network (NN) that is trained to bridge the gap between the
ground truth and the DEIM prediction, especially in the scenario where the DEIM
model is constructed from simulation data with limited fidelity. A large
network is not necessary for accurate correction as the linear model already
captures the main dynamics of the surface pressure field, thus yielding an
efficient DEIM+NN aerodynamic force prediction model. The model is tested on
numerical and experimental dynamic stall data of a 2D NACA0015 airfoil, and
numerical simulation data of dynamic stall of a 3D drone. Numerical results
demonstrate that the machine learning enhanced model can make fast and accurate
predictions of aerodynamic forces using only a few pressure sensors, even for
the NACA0015 case in which the simulations do not agree well with the wind
tunnel experiments. Furthermore, the model is robust to noise.

本文提出了一种基于少量位置布置的压力传感器的数据驱动空气动力学力预测模型，包括线性项和非线性校正，其中线性项通过减少的基础重构预测表面压力分布，而非线性项则是视情况而定的人工神经网络，用于提高精度。该模型在模拟和实验数据中被验证为快速准确地预测无人机的气动力。

基于稀疏压力传感器输入的机器学习增强实时气动力预测

Machine learning enhanced real-time aerodynamic forces prediction based  on sparse pressure sensor inputs

We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a
means of efficiently moving scattered objects into a target receptacle. Due to
the chaotic nature of aerodynamic forces, a blowing controller must (i)
continually adapt to unexpected changes from its actions, (ii) maintain
fine-grained control, since the slightest misstep can result in large
unintended consequences (e.g., scatter objects already in a pile), and (iii)
infer long-range plans (e.g., move the robot to strategic blowing locations).
We tackle these challenges in the context of deep reinforcement learning,
introducing a multi-frequency version of the spatial action maps framework.
This allows for efficient learning of vision-based policies that effectively
combine high-level planning and low-level closed-loop control for dynamic
mobile manipulation. Experiments show that our system learns efficient
behaviors for the task, demonstrating in particular that blowing achieves
better downstream performance than pushing, and that our policies improve
performance over baselines. Moreover, we show that our system naturally
encourages emergent specialization between the different subpolicies spanning
low-level fine-grained control and high-level planning. On a real mobile robot
equipped with a miniature air blower, we show that our simulation-trained
policies transfer well to a real environment and can generalize to novel
objects.

该研究介绍通过气动非抓取性操纵（吹气）来有效地将散乱的物体移动到目标容器。研究通过基于深度强化学习的空间动作地图框架实现了高水平的规划和低水平的闭环控制的有效组合。结果表明，吹气比推动等方法更有效，也证明了在低水平微观控制和高水平规划等不同子策略之间存在专业化的合作。在实际机器人上，该系统的仿真训练策略成功地转移到了实际环境，并可以泛化到新颖物体上。