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Multi-Agent Coordination Fluid Flow Modeling and Experimental Evaluation

Harshvardhan Uppaluru, Mohammad Ghuran, Hossein Rastgoftar

TL;DR

The paper addresses reliable, collision-free coordination of multiple agent teams operating in a shared space with potential failures and obstacles. It introduces a novel fluid-flow navigation framework based on an ideal fluid flow with a potential field $\phi_l$ and a stream function $\psi_l$, and develops analytic and numerical solutions to enable streamlines-guided motion. Four operation modes—SNCF, TVNC, TVC, and SOLE—cover failures, cooperative/non-cooperative dynamics, and obstacle-rich environments, with validation via indoor Crazyflie experiments. The results demonstrate robust coordination by wrapping obstacles with streamlines and aligning agent motion along tangent directions, offering a scalable approach for safe multi-agent deployment in constrained spaces.

Abstract

Reliability is a critical aspect of multi-agent system coordination as it ensures that the system functions correctly and consistently. If one agent in the system fails or behaves unexpectedly, it can negatively impact the performance and effectiveness of the entire system. Therefore, it is important to design and implement multi-agent systems with a high level of reliability to ensure that they can operate safely and move smoothly in the presence of unforeseen agent failure or lack of communication with some agent teams moving in a shared motion space. This paper presents a novel fluid flow navigation model that, in an ideal fluid flow, divides agents into cooperative (non-singular) and noncooperative (singular) agents, with cooperative agents sliding along streamlines safely enclosing noncooperative agents in a shared motion space. A series of flight experiments utilizing crazyflie quadcopters will experimentally validate the suggested model.

Multi-Agent Coordination Fluid Flow Modeling and Experimental Evaluation

TL;DR

The paper addresses reliable, collision-free coordination of multiple agent teams operating in a shared space with potential failures and obstacles. It introduces a novel fluid-flow navigation framework based on an ideal fluid flow with a potential field and a stream function , and develops analytic and numerical solutions to enable streamlines-guided motion. Four operation modes—SNCF, TVNC, TVC, and SOLE—cover failures, cooperative/non-cooperative dynamics, and obstacle-rich environments, with validation via indoor Crazyflie experiments. The results demonstrate robust coordination by wrapping obstacles with streamlines and aligning agent motion along tangent directions, offering a scalable approach for safe multi-agent deployment in constrained spaces.

Abstract

Reliability is a critical aspect of multi-agent system coordination as it ensures that the system functions correctly and consistently. If one agent in the system fails or behaves unexpectedly, it can negatively impact the performance and effectiveness of the entire system. Therefore, it is important to design and implement multi-agent systems with a high level of reliability to ensure that they can operate safely and move smoothly in the presence of unforeseen agent failure or lack of communication with some agent teams moving in a shared motion space. This paper presents a novel fluid flow navigation model that, in an ideal fluid flow, divides agents into cooperative (non-singular) and noncooperative (singular) agents, with cooperative agents sliding along streamlines safely enclosing noncooperative agents in a shared motion space. A series of flight experiments utilizing crazyflie quadcopters will experimentally validate the suggested model.
Paper Structure (18 sections, 1 theorem, 30 equations, 9 figures, 1 table, 5 algorithms)

This paper contains 18 sections, 1 theorem, 30 equations, 9 figures, 1 table, 5 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contributions
  4. Organization
  5. Methodology
  6. Analytic Approach
  7. Numerical Approach
  8. Operation Modes
  9. SNCF Navigation
  10. TVNC Navigation
  11. TVC Navigation
  12. SOLE Navigation
  13. Experiments and Discussion
  14. Stationary Non-Concurrent Failures (SNCF) Experiment
  15. Time-Varying Non-Cooperative (TVNC) Experiment
  16. ...and 3 more sections

Key Result

Theorem 1

Let $\left(x_{i,0}, y_{i,0}\right)$ denote the position of agent $i \in \mathcal{V}_l$ in the $x-y$ plane and $\theta_l(t_0) = \theta_{l0}$ denote the bulk motion direction of agents in $\mathcal{V}_l$ at initial (reference) time $t_0$ for every $l\in \mathcal{M}$. Define $\phi_{il}(t_0) = \phi_{il, as the minimum separation distance between agents in the $\phi_l-\psi_l$ plane, and Assume that th

Figures (9)

  • Figure 1: Schematic of the desired path of agent $i\in \mathcal{V}_l$.
  • Figure 2: Two-dimensional fluid flow coordination in a $3$-dimensional motion space where obstacles are wrapped by vertical cylinders emadi2022physics.
  • Figure 3: The SMART lab floor is defined as the motion space $\mathcal{P}$ and divided into four navigable channels using Eq. \ref{['nj']}.
  • Figure 4: Transformation between $\mathcal{N}_j$ (the $j$-th navigable channel) and the $\mathcal{S}_j$.
  • Figure 5: Left: Motion space with streamlines shown by black and potential lines shown by red. Right: Planning space. The green curve in the motion space is an streamline used by an agent $i\in \mathcal{V}_l$ to avoid collision with obstacles (the projection of the agent $i$'s path is a horizontal line in the planning space).
  • ...and 4 more figures

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Remark 1
  • Definition 1