Joint Deployment of Truck-Drone Systems for Camera-Based Object Monitoring

Truck-drone systems, wherein trucks carrying drones drive to pre-planned positions and then free drones equipped with cameras to monitor a known number of objects with reported positions, have been used for various scenarios. An object's quality of monitoring (QoM) by a camera is defined as a function of camera focal length and monitoring distance. Improving the QoM would help downstream tasks, including object detection and recognition. The monitoring utility is the fusion of all the QoMs of an object from multiple cameras. This article optimizes the Deployment Of Trucks And Drones for Object monitoring (DOTADO) problem, i.e., deploying a truck-drone system, where each drone is equipped with a varifocal camera, to maximize the overall monitoring utility for all objects. First, we model the hybrid system and define monitoring quality and utility. Then, we discretize the solution space into deployment strategies with performance bound. To select deployment strategies, we prove the submodularity of the problem and propose a two-level greedy algorithm with a bounded approximation ratio. Finally, we devise an optimal method to adjust the strategy for energy saving and communication improvement without losing monitoring utility. We perform both simulations and field experiments to verify the proposed framework.