On the Scheduling of Spatio-Temporal Charging Windows for Autonomous Drone Fleets

On the Scheduling of Spatio-Temporal Charging Windows for Autonomous Drone Fleets

Blog Article

The availability of low-cost unmanned aerial vehicles (UAVs), or drones, has made their organisation in fleets more feasible.The required coordination for managing these fleets comes with an increased complexity.When used for long-durability, autonomous inspection missions, it is necessary to recharge the drones due to their limited battery capacity.

By providing a set of nearby charging stations, the fleets can autonomously recharge and sustain indefinite missions.In order to reduce congestion at these charging stations, effective scheduling of charging cycles can have a significant impact on the mission execution read more time.In this paper, we propose a novel centralized method for scheduling charging time windows, taking into account the travel distances and occupation of charging stations.

We formulate a mixed-integer linear program (MILP) model here with two extensions to reduce the computational complexity.The solution to this problem assigns a set of charging windows to each drone, minimizing the mission execution time and ensuring batteries will not fully deplete.The performance of our proposed method is evaluated through a series of experiments, based on a discrete-event simulator.

Our results reveal a clear benefit over a greedy approach, reducing the mission execution time by up to 39.8%.Through careful parameter selection, a trade-off between mission execution time and scheduling time can be found.