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.

翻译：在拥挤环境中，个体必须绕过其他占据者以抵达目的地。理解并控制此类空间中的交通流，对于协调机器人集群及设计高密度人口基础设施具有重要意义。本文通过仿真、理论和实验，研究在智能体运动中添加随机性如何减少交通拥堵并帮助智能体更快抵达预设目标。计算模拟揭示了集体行为：当噪声水平超过临界值时，大规模拥堵将无法持续存在。基于这一观测，我们通过解析方法近似估计了集群的目标达成率，从而求解出使达成目标数量最大化的智能体密度与噪声水平。机器人实验验证了我们在仿真与理论结果中观察到的行为。最后，我们比较了简单的局部导航方法与复杂但计算成本高昂的中央规划器。实验表明，在中等密度以下，简单的反应式方案表现优异，且其计算效率远高于规划器，这为研究拥挤环境中鲁棒的去中心化导航方法提供了新动力。通过融合物理学与工程学思想，并综合运用仿真、理论与实验，本研究为涌现交通研究指明了新的方向。