FISC: A Fluid-Inspired Framework for Decentralized and Scalable Swarm Control

TL;DR

This paper introduces FISC, a fluid-inspired decentralized control framework for large robotic swarms, enabling scalable coordination without explicit communication by modeling the swarm as a fluid flow, validated through simulations.

Contribution

The paper presents a novel fluid-inspired approach for decentralized swarm control, bridging fluid dynamics principles with multi-agent robotic systems for improved scalability.

Findings

Quantitative agreement with CFD simulations in velocity, density, and pressure fields.

Effective decentralized control achieved without explicit inter-agent communication.

Scalable coordination demonstrated with thousands of quadcopters.

Abstract

Achieving scalable coordination in large robotic swarms is often constrained by reliance on inter-agent communication, which introduces latency, bandwidth limitations, and vulnerability to failure. To address this gap, a decentralized approach for outer-loop control of large multi-agent systems based on the paradigm of how a fluid moves through a volume is proposed and evaluated. A relationship between fundamental fluidic element properties and individual robotic agent states is developed such that the corresponding swarm "flows" through a space, akin to a fluid when forced via a pressure boundary condition. By ascribing fluid-like properties to subsets of agents, the swarm evolves collectively while maintaining desirable structure and coherence without explicit communication of agent states within or outside of the swarm. The approach is evaluated using simulations involving $O(10^3)$ quadcopter agents and compared against Computational Fluid Dynamics (CFD) solutions for a converging-diverging domain. Quantitative agreement between swarm-derived and CFD fields is assessed using Root-Mean-Square Error (RMSE), yielding normalized errors of 0.15-0.9 for velocity, 0.61-0.98 for density, 0-0.937 for pressure. These results demonstrate the feasibility of treating large robotic swarms as continuum systems that retain the macroscopic structure derived from first principles, providing a basis for scalable and decentralized control.