  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.
