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.