A Generalized Potential Game Approach of UAV Swarm Coordination for Hidden Target Localization

Considering a swarm of Unmanned Aerial Vehicles (UAVs) carrying sensors with nondeterministic detection and noisy localization measurements, while sharing observations with neighboring UAVs, we address the problem of localization of a hidden target in continuous space and discrete time. The goal is to coordinate UAVs to maximize the information gathered while minimizing their individual energy costs. We formulate the problem as a time-varying non-cooperative game with coupling constraints. We show that the target is localized in finite time with probability one and that the game has a generalized potential structure. Further, we provide an exact best-response algorithm for UAVs to iteratively compute their trajectories. Finally, we numerically compare the potential game to the team-based approach, demonstrating comparable performance under different communication graph structures and assessing the impact of the swarm size on various metrics.