🤖 AI Summary
Robust flocking control of dynamic multi-agent systems (MAS) in constrained environments remains challenging when agents rely solely on local bearing-distance sensing. Method: This paper proposes a regionalized formation control framework featuring a novel four-zone behavioral partition—repulsion, conflict avoidance, attraction, and surveillance—integrated with behavior-biased vector synthesis for control and a forward-priority directional obstacle-avoidance mechanism. A strategic separation strategy is introduced to accommodate heterogeneous agents, and a single-perception-zone simplified model is designed to reduce communication and computational coupling. Contribution/Results: The approach significantly enhances formation flexibility, scalability, and environmental adaptability. Experimental evaluations demonstrate efficient and robust dynamic flocking control in large-scale heterogeneous MAS under resource-constrained conditions, outperforming existing bearing-only or distance-only methods in both convergence reliability and real-time responsiveness.
📝 Abstract
This paper presents a novel zone-based flocking control approach suitable for dynamic multi-agent systems (MAS). Inspired by Reynolds behavioral rules for $boids$, flocking behavioral rules with the zones of repulsion, conflict, attraction, and surveillance are introduced. For each agent, using only bearing and distance measurements, behavioral deviation vectors quantify the deviations from the local separation, local and global flock velocity alignment, local cohesion, obstacle avoidance and boundary conditions, and strategic separation for avoiding alien agents. The control strategy uses the local perception-based behavioral deviation vectors to guide each agent's motion. Additionally, the control strategy incorporates a directionally-aware obstacle avoidance mechanism that prioritizes obstacles in the agent's forward path. We also introduce a simplified flocking model where agents interact with their neighbors within a single perception zone, without dividing it into separate interaction zones. Simulation results validate the effectiveness of the models in creating flexible, adaptable, and scalable flocking behavior.