Accelerating Emergence of Aerial Swarm

Herein, we present a methodology and framework for exploiting certain interdisciplinary studies that can particularly benefit from integration. In this paper, rigorous derivation of control theory and statistical analysis of simulation results are organically unified for testifying and optimizing the emergence of order in aerial swarming scenarios under free boundary conditions. Each Unmanned Aerial Vehicle (UAV) is regulated by a simplified mathematical model, based on which a distributed flocking protocol is proposed as a feasible solution for aerial swarms. On condition that the initial interaction network is connected, the LaSalle–Krasovskii invariance principle is implemented to verify the effectiveness of the above algorithm. However, most existing results on flocking are far from being engineering applications. A basic challenge is how to present a low-cost energy and time saving solution on account of the limited flight capability of these UAVs and real-time operational requirements. As is well known, energy consumption can be reduced if unnecessary interactions among individuals are eliminated. Therefore, another contribution of this paper is to propose a precise optimization of an existing flocking algorithm for UAVs with respect to interaction requirements. Energy and time measurements, as well as scalability effects, are assessed in terms of statistical significance and strength. The results indicate that the flocking control protocol adopting the minimal interaction is the most promising swarm.