Emergence of longitudinal queue behavior based on topological interaction and asynchronous dynamics.

Area of Science:

• Collective behavior
• Biophysics
• Swarm intelligence

Background:

• Classical collective motion models often overlook longitudinal queue dynamics in biological groups like bird flocks.
• Existing models typically focus on metric-based interactions and synchronous behaviors, limiting their applicability to phenomena like migration queues.

Purpose of the Study:

• To investigate the mechanisms driving longitudinal queue formation in biological groups.
• To explore how topological interactions, asynchronous dynamics, and limited perception influence collective motion.
• To provide theoretical guidance for understanding animal migration and designing robotic swarms.

Main Methods:

• Utilized a modified self-propelled particle model.
• Incorporated topological interactions, gliding asynchrony, and a limited view angle.
• Conducted simulations to analyze queue emergence and stability.

Main Results:

• Stable queue formation requires interaction with only two topological neighbors.
• An optimal view angle range of [200°, 270°] balances tracking and collision avoidance.
• Gliding asynchrony significantly enhances queue formation efficiency and reduces interaction complexity compared to synchronous or random updates.
• Topological interactions promote stable, highly connected networks essential for queue maintenance, unlike metric-based methods.

Conclusions:

• Limited sensory perception, low neighbor connectivity, and asynchronous dynamics are crucial for self-organized migration queues.
• Topological interactions are fundamental for maintaining linear order in collective motion.
• Findings offer theoretical insights into animal collective behavior and inform robotic swarm design.