Flocking for Multiple Subgroups of Multi-Agents With Different Social Distancing

Abstract

In this paper, considering the difference in social distancing among individuals, according to the extent of social distancing, a group composed of N mobile agents is divided into multiple different subgroups. Especially, from the perspective of differential game theory, the flocking problem of different subgroups can be regarded as collision avoidance between neighboring agents, or obstacle avoidance between agents and virtual static/dynamic obstacles. To explore the internal mechanism of this interesting problem, a novel flocking algorithm with multiple virtual leaders is designed. The proposed algorithm is a modified version of the traditional flocking and semi-flocking algorithms. Based on the Lyapunov stability theorem and LaSalle's invariance principle, the stability analysis of the proposed algorithm is then proven. Furthermore, considering the complex environment that swarm robots or unmanned aerial vehicles (UAVs) may face when performing military missions such as surveillance, reconnaissance, and rescue, etc., we also investigate the flocking problem of multi-agents in both virtual static and dynamic obstacles environment. Finally, three kinds of simulation results are provided to demonstrate the effectiveness of the proposed results.