Abstract
This paper presents a novel control strategy for drone networks to improve the quality of 3D structures reconstructed from aerial images by drones. Unlike the existing coverage control strategies for this purpose, our proposed approach simultaneously controls both the camera orientation and drone translational motion, enabling more comprehensive perspectives and enhancing the map's overall quality. Subsequently, we present a novel problem formulation, including a new performance function to evaluate the drone positions and camera orientations. We then design a QP-based controller with a control barrier-like function for a constraint on the decay rate of the objective function. The present problem formulation poses a new challenge, requiring significantly greater computational efforts than the case involving only translational motion control. We approach this issue technologically, namely by introducing JAX, utilizing just-in-time (JIT) compilation and Graphical Processing Unit (GPU) acceleration. We finally conduct extensive verifications through simulation in ROS (Robot Operating System) and show the real-time feasibility of the controller and the superiority of the present controller to the conventional method.
Abstract (translated)
本文提出了一种新的无人机网络控制策略,旨在通过无人机从高空图像中重构3D结构来提高无人机生成的3D结构的质量。与现有的覆盖控制策略不同,我们的方法同时控制摄像机方向和无人机平移运动,使得无人机可以获得更全面的视角,并提高地图的整体质量。接着,我们提出了一个新问题陈述,包括一个新的性能函数来评估无人机的位置和摄像机方向。然后,我们设计了一个基于QP的控制器,该控制器具有类似于控制壁垒的功能,用于约束目标函数的衰减率。当前问题陈述提出了一个新的挑战,需要比仅涉及平移运动控制的案例更大的计算努力。我们通过技术方法来解决这个问题,即通过引入JAX、即时编译和图形处理器(GPU)加速来利用。最后,我们在ROS(机器人操作系统)中进行广泛的仿真验证,证明了控制器和现有方法的优势。
URL
https://arxiv.org/abs/2404.13915
