Institute for Mechatronics Engineering & Cyber-Physical Systems (IMECH), Universidad de Málaga, 29071 Málaga, Spain.
Instituto Superior Técnico (IST), Universidade de Lisboa, 1049-001 Lisboa, Portugal.
Sensors (Basel). 2023 Jan 11;23(2):829. doi: 10.3390/s23020829.
Interest in Unmanned Aerial Vehicles (UAVs) has increased due to their versatility and variety of applications, however their battery life limits their applications. Heterogeneous multi-robot systems can offer a solution to this limitation, by allowing an Unmanned Ground Vehicle (UGV) to serve as a recharging station for the aerial one. Moreover, cooperation between aerial and terrestrial robots allows them to overcome other individual limitations, such as communication link coverage or accessibility, and to solve highly complex tasks, e.g., environment exploration, infrastructure inspection or search and rescue. This work proposes a vision-based approach that enables an aerial robot to autonomously detect, follow, and land on a mobile ground platform. For this purpose, ArUcO fiducial markers are used to estimate the relative pose between the UAV and UGV by processing RGB images provided by a monocular camera on board the UAV. The pose estimation is fed to a trajectory planner and four decoupled controllers to generate speed set-points relative to the UAV. Using a cascade loop strategy, these set-points are then sent to the UAV autopilot for inner loop control. The proposed solution has been tested both in simulation, with a digital twin of a solar farm using ROS, Gazebo and Ardupilot Software-in-the-Loop (SiL); and in the real world at IST Lisbon's outdoor facilities, with a UAV built on the basis of a DJ550 Hexacopter and a modified Jackal ground robot from DJI and Clearpath Robotics, respectively. Pose estimation, trajectory planning and speed set-point are computed on board the UAV, using a Single Board Computer (SBC) running Ubuntu and ROS, without the need for external infrastructure.
由于其多功能性和各种应用，人们对无人机 (UAV) 的兴趣日益增加，然而，它们的电池寿命限制了它们的应用。异构多机器人系统可以通过允许无人地面车辆 (UGV) 作为空中无人机的充电站来解决这个限制。此外，空中和地面机器人之间的合作使它们能够克服其他单个机器人的限制，例如通信链路覆盖范围或可达性，并解决高度复杂的任务，例如环境探索、基础设施检查或搜索和救援。这项工作提出了一种基于视觉的方法，使无人机能够自主检测、跟踪和降落在移动地面平台上。为此，ArUcO 基准标记用于通过处理安装在无人机上的单目相机提供的 RGB 图像来估计无人机和 UGV 之间的相对姿态。姿态估计被馈送到轨迹规划器和四个解耦控制器，以生成相对于无人机的速度设定点。使用级联环策略，然后将这些设定点发送到无人机自动驾驶仪进行内环控制。该解决方案已在仿真中进行了测试，使用 ROS、Gazebo 和 Ardupilot 软件在环 (SiL) 模拟一个太阳能农场的数字双胞胎；并在 IST 里斯本的户外设施中进行了实际测试，使用基于 DJ550 六旋翼无人机和分别来自 DJI 和 Clearpath Robotics 的改装 Jackal 地面机器人的无人机。姿态估计、轨迹规划和速度设定点在安装在无人机上的单板计算机 (SBC) 上计算，该 SBC 运行 Ubuntu 和 ROS，无需外部基础设施。
