Toward Intelligent Cooperation of UAV Swarms: When Machine Learning Meets Digital Twin

With high mobility, low cost and outstanding maneuverability properties, unmanned aerial vehicle (UAV) swarm has attracted worldwide attentions in both academia and industry. Nevertheless, the complex and coherent characteristics of the intelligent cooperation of UAV swarm greatly restrict its wide application. The recent development of artificial intelligence provides new methodologies for intelligent cooperation of UAV swarm. However, these methods are resource-in-tensive that cannot be directly applied in the computation and storage constrained UAVs. In this article, we propose a novel digital twin (DT)-based intelligent cooperation framework of UAV swarm. In the framework, a digital twin model is established to reflect the physical entity (i.e., UAV swarm) with high-fidelity and monitors its whole life cycle. Next, the decision model that integrates a machine learning algorithm is built to explore the global optimal solution and controls the behaviors of UAV swarm. To demonstrate the effectiveness of our proposed framework, a case study on intelligent network reconstruction is introduced, and simulation results are presented. Finally, a representative application provided by the framework is discussed.

Automated summary

Unmanned aerial vehicle (UAV) swarm has high mobility and low cost, but the complex characteristics of intelligent cooperation restrict its wide application. The recent development of artificial intelligence provides new methods, but they are resource-intensive and cannot be directly applied to constrained UAVs. This article proposes a digital twin-based intelligent cooperation framework for UAV swarm, with a case study on intelligent network reconstruction and simulation results to demonstrate its effectiveness.