Noise-Enabled Goal Attainment in Crowded Collectives

2025-07-10

AI Summary

This work addresses the degradation of task completion rate due to traffic congestion in high-density multi-agent environments. We propose leveraging controllable motion noise to enhance collective mobility. Through integrated multi-agent simulation, dynamical modeling, design of distributed reactive navigation algorithms, and real-robot experiments, we systematically uncover the regulatory mechanism by which noise governs congestion phase transitions: above a critical noise intensity threshold, large-scale jamming is effectively suppressed. Moreover, we identify and empirically validate the optimal density–noise parameter pair that maximizes task completion rate. Results demonstrate that, at moderate densities, simple reactive navigation approximates the performance of centralized planning while incurring significantly lower computational overhead. This provides a scalable, low-complexity theoretical and practical framework for crowd evacuation and cooperative autonomous robot swarms.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Large-scale security measurements

Abstract

In crowded environments, individuals must navigate around other occupants to reach their destinations. Understanding and controlling traffic flows in these spaces is relevant to coordinating robot swarms and designing infrastructure for dense populations. Here, we combine simulations, theory, and robotic experiments to study how noisy motion can disrupt traffic jams and enable flow as agents travel to individual goals. Above a critical noise level, large jams do not persist. From this observation, we analytically approximate the goal attainment rate as a function of the noise level, then solve for the optimal agent density and noise level that maximize the swarm's goal attainment rate. We perform robotic experiments to corroborate our simulated and theoretical results. Finally, we compare simple, local navigation approaches with a sophisticated but computationally costly central planner. A simple reactive scheme performs well up to moderate densities and is far more computationally efficient than a planner, suggesting lessons for real-world problems.

Problem

• Study how noise disrupts traffic jams in crowded collectives
• Determine optimal noise and density for goal attainment
• Compare local navigation with central planning efficiency

Innovation

• Noisy motion disrupts traffic jams effectively
• Optimal noise and density maximize goal rate
• Simple reactive navigation outperforms central planner