颗粒环境中 stigmergic 运输的涌现
Emergence of Stigmergic Transport in Granular Environments
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中文总结 AI 辅助
该研究通过机器人实验与随机模型，揭示颗粒环境中环境记忆与几何拥挤的相互作用会涌现 stigmergic 运输，存在最优运输区域，阐明智能体可通过纯机械相互作用集体构建运输网络。
中文摘要 AI 辅助
我们证明，在可变形环境中，环境记忆与几何拥挤的相互作用会涌现出 stigmergic 路径形成。通过机器人随机行走器实验结合最小随机模型，我们展示了当堆积分数 φ 超过临界值 φ_c 时，会出现持续的自增强运输路径，此时环境记忆会提升行走器的移动性。当接近堵塞转变点 φ_J 时，尽管环境记忆仍然存在，但不断加剧的拥挤会逐渐抑制这种运输增强，由此产生的非单调行为揭示了远低于堵塞点的最优运输区域。更广泛地说，本研究阐明了智能体如何通过与可变形基底的纯机械相互作用，集体构建运输网络。
英文摘要
We show that stigmergic path formation emerges in a deformable environment through the interplay between environmental memory and geometrical crowding. Using experiments with robotic random walkers together with a minimal stochastic model, we demonstrate the onset of persistent self-reinforced transport pathways above a critical packing fraction $ϕ_c$, where environmental memory enhances walker mobility. As the jamming transition $ϕ_J$ is approached, increasing crowding progressively suppresses this transport enhancement despite the persistence of environmental memory. The resulting non-monotonic behavior reveals an optimal transport regime well below jamming. More generally, our work establishes how active agents can collectively build transport networks through purely mechanical interactions with a deformable substrate.
