Alignment with neighbours enables escape from dead ends in flocking models.

Area of Science:

• Collective behavior
• Animal movement
• Agent-based modeling

Background:

• Coordinated animal movement is well-studied in open spaces.
• Real-world environments present obstacles that complicate collective movement.
• Existing models often lack mechanisms for navigating complex environments.

Purpose of the Study:

• To investigate collective behavior of simulated agents in environments with concave obstacles.
• To identify emergent strategies for escaping dead ends in group movement.
• To understand the role of flocking rules and obstacle repulsion in navigation.

Main Methods:

• Simulated agents following flocking rules with obstacle repulsion.
• Testing agent groups in environments with concave obstacles (dead ends).
• Analyzing emergent behaviors and comparing group vs. solitary agent performance.

Main Results:

• Groups spontaneously exhibited 'flocking escapes' from dead ends.
• Flocking escapes were not observed in solitary agents.
• Agent alignment reduced effective turning speed, enabling group persistence and escape.

Conclusions:

• Flocking escapes are an emergent collective behavior enabling groups to navigate complex environments.
• Agent alignment is key to forming stable clusters that facilitate escape.
• This behavior may be relevant for various animal species navigating obstacles.