A fast coordination approach for large-scale drone swarm

Abstract

With the advances in artificial intelligence, robotics, and data fusion, large numbers of drones operating in a coordinated manner will become commonplace for a wide range of commercial and military uses. At present, the application methods of drone swarms are mainly divided into fully autonomous methods and controlled methods with human participation. Because of the limited level of artificial intelligence, controlled drone swarms will be the main way for the application of large-scale drone swarms for a long time. However, there is less research on achieving global coordination in a limited time for a controlled large-scale drone swarm. Therefore, a new large-scale drone swarm framework is proposed firstly in this paper, which achieves global coordination through local interaction and reduces the impact of limited channel resources. Secondly, this paper proposes a local interaction-based fast coordination method and introduces a prediction mechanism, to ensure that large-scale drone swarms can quickly achieve coordination even in the presence of node loss. Moreover, the numerical integration method is used to update the consensus state, so that the drones can increase the iteration period, reduce the number of packets, and further reduce the channel burden. Finally, considering that large-scale drones swarm are usually composed of drone swarms launched at different locations and times, a consensus algorithm considering the merging behavior of drone swarms is also proposed. The simulation results show that the large-scale drone swarm using the proposed architecture can achieve the leader–follower consensus in a very short time and even in a confrontational environment with poor communication conditions. Besides, after the merger of multiple drone swarms, the consensus problem can still be solved in very few iteration cycles.