AGENT: An Adaptive Grouping Entrapping Method of Flocking Systems
This addresses the challenge of multi-target entrapment in flocking systems, which is incremental as it builds on existing methods like artificial potential fields.
The study tackled the problem of enabling a swarm of agents to adaptively group and entrap multiple targets through distributed decision-making and smooth flocking, achieving well-distributed entrapping as validated by simulation experiments.
This study proposes a distributed algorithm that makes agents' adaptive grouping entrap multiple targets via automatic decision making, smooth flocking, and well-distributed entrapping. Agents make their own decisions about which targets to surround based on environmental information. An improved artificial potential field method is proposed to enable agents to smoothly and naturally change the formation to adapt to the environment. The proposed strategies guarantee that the coordination of swarm agents develops the phenomenon of multiple targets entrapping at the swarm level. We validate the performance of the proposed method using simulation experiments and design indicators for the analysis of these simulation and physical experiments.