AGENT: An Adaptive Grouping Entrapping Method of Flocking Systems

TL;DR

This paper introduces a distributed, adaptive grouping algorithm for flocking systems that enables agents to effectively entrap multiple targets through decision-making, smooth formation changes, and well-distributed coordination, validated by simulations.

Contribution

It presents a novel distributed algorithm with an improved artificial potential field for adaptive, multi-target entrapping in flocking systems, enhancing coordination and environmental responsiveness.

Findings

Successful simulation validation of the algorithm.

Effective multi-target entrapping demonstrated.

Smooth formation adaptation achieved.

Abstract

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.