Rodriguez Salazar Leopoldo, Cobano Jose A, Ollero Anibal
Robotics, Vision and Control Group, Universidad de Sevilla, 41092 Sevilla, Spain.
Sensors (Basel). 2016 Dec 23;17(1):8. doi: 10.3390/s17010008.
This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz . Predictions show a convergence time with a 95% confidence interval of approximately 30 s .
本文提出了一种用于识别阵风和风切变等风特征的系统。在小型无人机系统（UAS）的节能导航背景下，这些特征尤为重要。所提出的系统生成实时风矢量估计值以及一种用于生成风场预测的新颖算法。估计基于现成的导航系统与空速读数在所谓直接方法中的整合。风预测使用大气模型通过不同的统计分析来表征风场。在预测阶段，该系统能够以大数据方法纳入来自先前飞行的风测量数据，以增强近似值。风估计值被分类并拟合到威布尔概率密度函数中。利用遗传算法（GA）确定分布的形状和尺度参数，这些参数用于确定特定位置最可能的风速。该系统使用此信息来表征风切变或离散阵风，还利用高斯过程回归来表征连续阵风。风特征的知识对于计算低成本和低有效载荷的节能轨迹至关重要。因此，该系统提供了一种无需任何额外传感器的解决方案。系统架构呈现出模块化分散方法，其中系统的主要部分在模块中分离，信息交换由通信处理器管理，以提高可升级性和可维护性。通过提供模拟和软件在环测试的初步结果来进行验证。从在西班牙安达卢西亚塞维利亚大都市区进行的实际飞行中收集的遥测数据用于测试。结果表明，风估计和预测可以以1Hz的频率计算，风图可以以0.4Hz的频率更新。预测显示收敛时间在95%置信区间内约为30秒。
