Decentralized Control of UAV Swarms for Bandwidth-Aware Video Surveillance Using NMPC

This paper proposes a framework for controlling a drone swarm to achieve two goals: i) covering a desired region of interest through onboard cameras that capture videos to be sent in real-time to a Ground Control Station (GCS), and ii) ensuring the highest video quality possible given the available Internet network bandwidth. Indeed, the quality of received videos depends on both the available bandwidth, which directly influences video encoding bitrate, and the altitude of drones, which influences pixel density. Thus, contrary to the conventional assumption of uniform drone altitudes, we let drones to track a reference altitude that is function of the time-varying available bandwidth to improve visual quality. To achieve the aforementioned goals, we propose a leader-follower multi-agent system formation control problem. In this setup, the leader tracks a desired path using Nonlinear Model Predictive Control (NMPC) to cover the area of interest. Follower agents track the leader using NMPC, aiming at maximizing both the total coverage area and the quality of the videos sent to the GCS, considering the constraints imposed by the network available bandwidth. At the same time, we formulate the NMPC problem to ensure that the swarm maintains a formation characterized by a given overlap percentage between the videos captured by the drones while avoiding collisions. This allows dynamical stitching of the received videos at the GCS, enabling the execution of computer vision algorithms for tasks such as object detection and surveillance.