Distributed online assignment of charging stations in persistent coverage control tasks based on LP relaxation and ADMM

This paper investigates distributed online assignment of charging stations for a drone network in a persistent coverage control task. To ensure persistency not only in motion but also in energy, drones need to go back to charging stations before running out of their batteries. Coverage control schemes with energy persistency were presented in the literature based on so-called control barrier functions. These methodologies, however, assume a fixed correspondence between a drone and a charging station, but always returning to a preassigned station is not necessarily an efficient decision, namely the constraint may hinder the monitoring behaviour of the drones. Dynamically reassigning charging stations to drones is thus expected to enhance the coverage performance. To this end, we formulate an online assignment problem of charging stations with parameters determined by the control barrier function values in real time, and exactly relax the formulated optimization problem to a linear programming problem. We then propose a distributed solution to the problem based on ADMM and the overall partially distributed control architecture including persistent coverage control and online assignment of charging stations. The control system is finally demonstrated through Monte Carlo simulation.