Promoting collective motion of self-propelled agents by distance-based influence

TL;DR

This paper introduces a distance-weighted influence model for self-propelled agents, demonstrating how influence parameters affect collective motion and identifying optimal conditions for consensus.

Contribution

It presents a novel dynamic model with adjustable influence based on distance, revealing how parameters impact collective behavior and optimal influence settings.

Findings

Optimal influence parameter maximizes direction consensus.

Optimal parameter increases with system size.

Optimal parameter decreases with velocity, sensing radius, and noise.

Abstract

We propose a dynamic model for a system consisting of self-propelled agents in which the influence of an agent on another agent is weighted by geographical distance. A parameter $\alpha$ is introduced to adjust the influence: the smaller value of $\alpha$ means that the closer neighbors have stronger influence on the moving direction. We find that there exists an optimal value of $\alpha$, leading to the highest degree of direction consensus. The value of optimal $\alpha$ increases as the system size increases, while it decreases as the absolute velocity, the sensing radius and the noise amplitude increase.