Centralized UAV Swarm Formation Estimation with Relative Bearing Measurements and Unreliable GPS

John Akagi, Randall S. Christensen, M. Harris
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引用次数: 1

Abstract

Unmanned Aerial Vehicles (UAVs) are used in a variety of tasks from package delivery to infrastructure inspection. In these situations UAVs typically rely on GPS signals to measure their current position. When operating in certain locations, like within urban canyons or where GPS signals are being disrupted, UAVs need alternate means to estimate their position and avoid collision. Current techniques include using LIDAR or camera measurements to detect nearby UAV and other objects. This paper presents a centralized Extended Kalman filter where the states of a UAV swarm are estimated using line of sight measurements from a camera. A single UAV is assumed to be able to receive sporadic GPS measurements but that information is able to propagate through the swarm via the relative measurements. Other research has shown that camera measurements can be used in a leader-follower situation but the contribution of this work is applying camera measurements to a swarm setting. The results show that this method does allow for the GPS denied UAVs to maintain a reasonable estimate of their state. Additionally, the availability of the line of sight measurements is adjusted and the change in state estimation accuracy is seen to depend primarily on the formation geometry. These results allow designers to gauge what measurement frequency is necessary to support a desired level of state estimation.
基于相对方位测量和不可靠GPS的集中式无人机群编队估计
无人驾驶飞行器(uav)用于从包裹递送到基础设施检查的各种任务。在这些情况下,无人机通常依靠GPS信号来测量其当前位置。当无人机在特定地点运行时,比如在城市峡谷或GPS信号被干扰的地方,无人机需要替代方法来估计其位置并避免碰撞。目前的技术包括使用激光雷达或相机测量来探测附近的无人机和其他物体。本文提出了一种集中式扩展卡尔曼滤波器,该滤波器利用摄像机的视线测量来估计无人机群的状态。假定一架无人机能够接收零星的GPS测量,但是该信息能够通过相对测量在蜂群中传播。其他研究表明,相机测量可以用于领导者-追随者的情况,但这项工作的贡献是将相机测量应用于群体设置。结果表明,该方法能使GPS拒绝无人机保持对自身状态的合理估计。此外,视线测量的可用性进行了调整,状态估计精度的变化主要取决于地层的几何形状。这些结果使设计人员能够衡量需要什么样的测量频率来支持期望的状态估计水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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