Work

Optimal flock formation induced by agent heterogeneity

Year: 2025

Type: article

Abstract: The study of flocking in biological systems has identified conditions for self-organized collective behavior, inspiring the development of decentralized strategies to coordinate the dynamics of swarms of drones and other autonomous vehicles. Previous research has focused primarily on the role of the time-varying interaction network among agents while assuming that the agents themselves are identical or nearly identical. Here, we depart from this conventional assumption to investigate how inter-individual differences between agents affect stability and convergence in flocking dynamics. We show that flocks of agents with optimally assigned heterogeneous parameters significantly outperform their homogeneous counterparts, achieving 20−40% faster convergence to desired formations across various control tasks. These tasks include target tracking, flock formation, and obstacle maneuvering. In systems with communication delays, heterogeneity can enable convergence even when flocking is unstable for identical agents. Our results challenge existing paradigms in multi-agent control and establish system disorder as an adaptive, distributed mechanism to promote collective behavior in flocking dynamics. Inter-individual differences are often viewed as obstacles to flocking and coordination. Here, the authors show that tuning heterogeneity in agent parameters can lead to faster and more robust flocking, even in the presence of communication delays and complex tasks

Source: Nature Communications

DOI: 10.1038/s41467-025-64233-0

Authors: Arthur N. Montanari, Ana Elisa D. Barioni, Chao Duan, Adilson E. Motter

Institutions: Northwestern University, Xi'an Jiaotong University

Language: English

Topic: Distributed Control Multi-Agent Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $6,990