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Towards autonomous tracking and landing on moving target

机译:进行自主跟踪并着陆在移动目标上

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The battery capacity of Unmanned Aerial Vehicle (UAV) is the main limitation, but with the rapid growth of UAV deployment in both military and civilian application, there is an urgent need to development the reliable and automated landing procedure. This paper is aim to propose a basic framework for autonomous landing on moving target for the Vertical Take-off and Landing (VTOL) UAVs. The VTOL vehicle is assumed to equip with a Global Navigation Satellite System (GNSS) system and a stereo vision system which could generate the point cloud within 20 meters. We applied a particle filter based Visual Servo in the UAV vision system to detect and track the moving the target at real time. We also combine the inertial measurement unit (IMU) data with the stereo vision based visual odometry to make the relative accurate pose estimation. The relative position, orientation and velocity to the landing area on the moving carrier is obtained by a modified optical flow method. The control method used in this framework combined tracking and approaching base on the range distance. We has applied our proposed framework on both simulation and landing task on a moving vehicle, and the result shows the efficiency and extended ability of our framework.
机译:无人飞行器(UAV)的电池容量是主要限制,但是随着在军事和民用应用中无人机部署的迅速增长,迫切需要开发可靠的自动降落程序。本文旨在为垂直起降无人机(VTOL)的无人飞行目标提出一个自动着陆的基本框架。假定VTOL车辆配备了可以在20米之内生成点云的全球导航卫星系统(GNSS)系统和立体视觉系统。我们在无人机视觉系统中应用了基于粒子滤波器的Visual Servo,以实时检测和跟踪目标的移动。我们还将惯性测量单位(IMU)数据与基于立体视觉的视觉里程表相结合,以进行相对准确的姿势估计。通过改进的光流方法获得相对于移动载体上的着陆区域的相对位置,方向和速度。在该框架中使用的控制方法基于距离距离将跟踪和逼近相结合。我们已经将我们提出的框架应用到了移动车辆的仿真和着陆任务上,结果表明了该框架的效率和扩展能力。

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