在拥挤环境中引入随机运动可减少拥堵，提升群体到达目标的效率。
Noise-enabled goal attainment in crowded collectives
- 通过增加代理运动的随机性，改善集体导航性能。
- 超过临界噪声水平后，大规模拥堵不再持续，目标达成率显著提升。
- 简单反应式方案比复杂规划器更高效，适合实际应用。
在拥挤环境中，个体需绕行他人以抵达目的地。理解并控制此类空间中的交通流，对协调机器人集群和设计高密度人群基础设施具有重要意义。本文结合仿真、理论与实验，研究了在代理运动中引入随机性如何减少交通堵塞并加速到达指定目标。计算分析揭示了集体行为特征：当噪声水平超过临界值时，大范围拥堵不再持续。基于此，我们解析推导出群体目标达成率的近似公式，进而求解使目标达成数最大化的代理密度与噪声水平组合。机器人实验验证了仿真与理论结果的一致性。最后，我们比较了简单的局部导航策略与复杂但计算成本高昂的中央规划器，发现简单反应式方法在中等密度下表现优异，且计算效率远高于规划器，推动了对鲁棒、去中心化导航方法的进一步研究。本工作融合物理与工程思想，通过多模态验证，为涌现式交通研究开辟新方向。
原文摘要 · Abstract (English)
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.
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