Active navigation relies on effectively extracting information from the surrounding environment, and often features the tracking of gradients of a relevant signal - such as the concentration of molecules. Microfluidic networks of closed pathways pose the challenge of determining the shortest exit pathway, which involves the proper local decision-making at each bifurcating junction. Here, we focus on the basic decision faced at a T-junction by a microscopic particle, which orients among possible paths via its sensing of a diffusible substance’s concentration. Specifically, we study the navigation of colloidal particles by diffusiophoresis, and highlight the crucial role of junctions in determining the statistics of the paths chosen by the particles. We demonstrate the harsh limits faced by a microscopic agent attempting to follow the shortest path, even for agents that reliably track local concentration gradients. We further use numerical experiments to search for navigation strategies that achieve a better selection of optimal paths, including biomimetic strategies such as run and tumble or Markovian chemotactic migration. We show that the latter perform similarly to the colloidal particles, whilst an alternatively engineered “run and whirl” strategy allows a point particle to effectively emulate larger particles and navigate with higher efficiency.

中文翻译：

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梯度传感微观主体在T型节点处的决策

主动导航依赖于从周围环境中有效提取信息，并且通常具有跟踪相关信号的梯度（例如分子浓度）的功能。封闭路径的微流体网络带来了确定最短出口路径的挑战，这需要在每个分叉路口进行适当的局部决策。在这里，我们着眼于微观粒子在T型结处面临的基本决策，该微观粒子通过感知可扩散物质的浓度而在可能的路径中定向。具体来说，我们研究了通过扩散电泳的胶体粒子导航，并强调了结在确定粒子选择的路径的统计数据中的关键作用。我们证明了微观代理试图遵循最短路径所面临的严苛限制，即使对于能够可靠跟踪局部浓度梯度的试剂也是如此。我们进一步使用数值实验来搜索导航策略，以更好地选择最佳路径，包括仿生策略，例如奔跑和滚滚或马尔可夫趋化迁移。我们表明，后者的性能与胶体颗粒相似，而另类设计的“运行和旋转”策略则可以使点颗粒有效地模拟较大的颗粒并以更高的效率导航。