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

Mohini Priya Kolluri, Ammar Waheed, Zohaib Hasnain

TL;DR

This work introduces FISC, a fluid-inspired framework for decentralized swarm control that treats large robot swarms as continuum systems via a minimal quartet of swarm primitives $Q=\{u_s,\rho_s, P_s, T_s\}$. By deriving these variables from agent-level states and employing a velocity-fitting procedure, the framework achieves fluid-like coherence and structure without inter-agent communication, enabling scalable control in $O(10^3)$-agent simulations. The approach leverages an isentropic flow analogy with a compressibility-based closure, mapping CFD target fields to swarm commands through control-volume partitioning and optimization over discrete agent densities. Results show that velocity fields from velocity-fitting align with CFD solutions (RMSE $0.15-0.9$ for $u_s$) and that pressure and density fields retain salient fluid-like trends (pressure RMSE up to $0.937$, density RMSE $0.61-0.98$), supporting the viability of continuum-based coordination for large swarms and highlighting pathways for future density- or pressure-aware control. The framework offers a platform-agnostic basis for decentralized coordination that can scale to diverse robotic platforms and environments while preserving macroscopic flow structure.

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.

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

TL;DR

This work introduces FISC, a fluid-inspired framework for decentralized swarm control that treats large robot swarms as continuum systems via a minimal quartet of swarm primitives . By deriving these variables from agent-level states and employing a velocity-fitting procedure, the framework achieves fluid-like coherence and structure without inter-agent communication, enabling scalable control in -agent simulations. The approach leverages an isentropic flow analogy with a compressibility-based closure, mapping CFD target fields to swarm commands through control-volume partitioning and optimization over discrete agent densities. Results show that velocity fields from velocity-fitting align with CFD solutions (RMSE for ) and that pressure and density fields retain salient fluid-like trends (pressure RMSE up to , density RMSE ), supporting the viability of continuum-based coordination for large swarms and highlighting pathways for future density- or pressure-aware control. The framework offers a platform-agnostic basis for decentralized coordination that can scale to diverse robotic platforms and environments while preserving macroscopic flow structure.

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 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.
Paper Structure (42 sections, 20 equations, 11 figures, 2 tables, 1 algorithm)

This paper contains 42 sections, 20 equations, 11 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Theoretical Framework
  4. Isentropic Flow Characteristics
  5. Flow Domain and Control Volume Formulation
  6. Swarm-Domain Primitive Variables
  7. Swarm Velocity Field: us(x,t)
  8. Swarm Density: rho_s(x,t)
  9. Swarm Pressure: Ps(x,t)
  10. Normal Stresses and Isotropic Swarm Pressure
  11. Swarm Temperature Field: Ts(x,t)
  12. Constitutive Parameters
  13. Specific heats $c_{p,s}$ and $c_{v,s}$
  14. Specific heat ratio $\gamma_s$
  15. Gas constant $R_s$
  16. ...and 27 more sections

Figures (11)

  • Figure 1: Flow diagram showing evolution of field variables in the FISC framework.
  • Figure 2: Axisymmetric CD Nozzle in XZ plane
  • Figure 3: Velocity tracking and trajectory plots for step responses of individual axes.
  • Figure 4: Aggressive maneuvers simulation demonstrating effect of feedforward gain on velocity tracking performance on different racing maneuvers.
  • Figure 5: Swarm evolution across two scenarios: Agents seeded in full tunnel (Case 1) and agents admitted at the inlet as a continuous source from a reservoir (Case 2) at time stamps of 0.5 s, 6 s, 10 s, 15 s. Control volume boundaries are shown as gray wireframes.
  • ...and 6 more figures