使用惯性、距离无线电和部分GNSS的sUAS群导航

M. U. de Haag, Svenja Huschbeck, Joel Huff
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引用次数: 5

摘要

小型无人机系统(sUAS)的操作需求和复杂性都在增加。使用多个合作sUAS(即蜂群)可能是有益的,有时需要独立或协作执行某些任务(例如,精准农业,测绘,监视)。然而,在障碍物和导航方面具有挑战性的环境中,自主和实时控制多个sUAS的飞行需要高度精确的绝对位置和相对位置以及群中所有平台的速度信息。这些信息对于有效和高效地解决sUAS之间可能发生的碰撞也是必要的。在我们的蜂群中,每个平台都配备了一个全球导航卫星系统(GNSS)传感器,一个惯性测量单元(IMU),一个气压高度计和一个相对距离传感器(距离无线电)。当GNSS可用时,其测量与IMU,气压高度计和距离无线电测量紧密结合,以获得平台的绝对和相对位置。当GNSS由于外部因素(如障碍物、干扰)而无法使用时,位置和速度估计器会切换到基于IMU、气压和相对距离测量的综合解决方案。该解决方案使系统能够保持准确的相对位置估计,并减少群体绝对位置估计的漂移,这是典型的基于imu的系统。已经开发了多个多直升机数据收集平台,并配备了由俄亥俄大学开发的GNSS、惯性传感器和距离无线电。本文概述了基础方法,平台硬件组件(三架多旋翼机和一个地面站),并使用仿真和sUAS飞行测试数据分析和讨论了性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
sUAS Swarm Navigation using Inertial, Range Radios and Partial GNSS
Small Unmanned Aerial Systems (sUAS) operations are increasing in demand and complexity. Using multiple cooperative sUAS (i.e. a swarm) can be beneficial and is sometimes necessary to perform certain tasks (e.g., precision agriculture, mapping, surveillance) either independent or collaboratively. However, controlling the flight of multiple sUAS autonomously and in real-time in a challenging environment in terms of obstacles and navigation requires highly accurate absolute and relative position and velocity information for all platforms in the swarm. This information is also necessary to effectively and efficiently resolve possible collision encounters between the sUAS. In our swarm, each platform is equipped with a Global Navigation Satellite System (GNSS) sensor, an inertial measurement unit (IMU), a baro-altimeter and a relative range sensor (range radio). When GNSS is available, its measurements are tightly integrated with IMU, baro-altimeter and range-radio measurements to obtain the platform's absolute and relative position. When GNSS is not available due to external factors (e.g., obstructions, interference), the position and velocity estimators switch to an integrated solution based on IMU, baro and relative range measurements. This solution enables the system to maintain an accurate relative position estimate, and reduce the drift in the swarm's absolute position estimate as is typical of an IMU-based system. Multiple multi-copter data collection platforms have been developed and equipped with GNSS, inertial sensors and range radios, which were developed at Ohio University. This paper outlines the underlying methodology, the platform hardware components (three multi-copters and one ground station) and analyzes and discusses the performance using both simulation and sUAS flight test data.
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