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An Empirical Model of Rotorcraft UAV Downwash for Disturbance Localization and Avoidance

机译:旋翼无人机下水扰动定位与避免的经验模型

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Proximity operations of small rotary wing air vehicles pose a unique challenge due to the downwash they generate. These vertical flow disturbances may cause substantial and potentially hazardous flight disruption to nearby vehicles. This paper presents a downwash detection and localization strategy intended for use on small unmanned rotorcraft. A probabilistic framework estimates the position of neighboring rotorcraft operating at a higher altitude by combining onboard velocity measurements with a reduced-order flow model. A series of ground-based experiments that characterize the flow beneath a representative group of small rotary wing vehicles are described. A custom pressure probe designed to be carried by small rotary wing vehicles is mounted on an automated two-axis Cartesian carriage system, which enables accurate sensor placement. Velocity measurements collected in a prescribed path are assimilated into an axis-symmetric downwash model. Tests show that disturbances can be localized to the region directly beneath each vehicle while the sensor is still approaching, demonstrating basic downwash detection and localization capabilities for three different rotorcraft configurations.
机译:小型旋翼空气车辆的接近操作由于它们产生的醉干而构成了独特的挑战。这些垂直流动干扰可能对附近车辆造成大幅且潜在的危险飞行中断。本文介绍了用于在小无人旋翼飞机上使用的挖醉检测和本地化策略。概率框架估计邻近旋翼机通过用阶速度测量与依次阶流量模型组合的情况来估计在更高的高度下运行的位置。描述了一系列基于基础的实验,其特征在于小型旋翼车辆的代表小组下面的流动。设计用于由小型旋翼车辆承载的定制压力探头安装在自动双轴笛卡尔架系统上,这使得能够精确传感器放置。在规定路径中收集的速度测量被同化为轴对称的淋浴模型。测试表明,在传感器仍在接近,干扰可以直接位于每个车辆下方的区域,展示三种不同旋翼配置的基本挖掘检测和定位功能。

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