Noise-enabled goal attainment in crowded collectives

TL;DR

This paper demonstrates that adding stochasticity to agent motion in crowded environments can reduce traffic jams and improve goal attainment, supported by simulations, theory, and robotic experiments.

Contribution

It introduces a computational and analytical framework showing how noise influences traffic flow and goal achievement in crowded collectives, with practical robotic validation.

Findings

Above a critical noise level, large jams do not persist.

Optimal noise and density maximize goal attainment.

Simple reactive navigation performs well at moderate densities.

Abstract

In crowded environments, individuals must navigate around other occupants to reach their destinations. Understanding and controlling traffic flows in these spaces is relevant for coordinating robot swarms and designing infrastructure for dense populations. Here, we use simulations, theory, and experiments to study how adding stochasticity to agent motion can reduce traffic jams and help agents travel more quickly to prescribed goals. A computational approach reveals the collective behavior. Above a critical noise level, large jams do not persist. From this observation, we analytically approximate the swarm's goal attainment rate, which allows us to solve for the agent density and noise level that maximize the goals reached. Robotic experiments corroborate the behaviors observed in 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, motivating further research into robust, decentralized navigation methods for crowded environments. By integrating ideas from physics and engineering using simulations, theory, and experiments, our work identifies new directions for emergent traffic research.