AGENT: an adaptive grouping and entrapping method for flocking systems

This study proposes a distributed algorithm that enables agents' adaptive grouping and entrapment of multiple targets via automatic decision making, smooth flocking, and well-distributed entrapping. In this study, an agent distributed decision framework is proposed. Agents make their own decisions about which targets to surround based on environmental information. Meanwhile, a modified Vicsek model is proposed to enable agents to smoothly change formations to adapt to the environment, while forming an entrapping effect on the target. In addition, we provide an optional rotary entrapping function for this model to achieve better effect. We validate the performance of proposed method using simulation and physical experiments.

Automated summary

This study proposes a distributed algorithm that enables agents' adaptive grouping and entrapment of multiple targets via automatic decision making, smooth flocking, and well-distributed entrapping. The study introduces an agent distributed decision framework where agents make their own decisions on which targets to surround based on environmental information. Additionally, a modified Vicsek model is proposed to allow agents to smoothly change formations, adapt to the environment, and effectively entrap the target. The performance of the proposed method is validated through simulation and physical experiments.