UBAT: On Jointly Optimizing UAV Trajectories and Placement of Battery Swap Stations
This addresses the limited flight time challenge for UAVs by optimizing charging station deployment, representing an incremental improvement in methodology.
The paper tackles the problem of optimizing UAV trajectories and battery swap station placement to maximize system performance, proposing UBAT, a heuristic framework based on ant colony optimization, which achieves solutions within 8.3% and 7.3% of the true optimal for trajectories and stations, respectively.
Unmanned aerial vehicles (UAVs) have been widely used in many applications. The limited flight time of UAVs, however, still remains as a major challenge. Although numerous approaches have been developed to recharge the battery of UAVs effectively, little is known about optimal methodologies to deploy charging stations. In this paper, we address the charging station deployment problem with an aim to find the optimal number and locations of charging stations such that the system performance is maximized. We show that the problem is NP-Hard and propose UBAT, a heuristic framework based on the ant colony optimization (ACO) to solve the problem. Additionally, a suite of algorithms are designed to enhance the execution time and the quality of the solutions for UBAT. Through extensive simulations, we demonstrate that UBAT effectively performs multi-objective optimization of generation of UAV trajectories and placement of charging stations that are within 8.3% and 7.3% of the true optimal solutions, respectively.