论文标题
自我探测活跃粒子的嘈杂追求和图案形成
Noisy Pursuit and Pattern Formation of Self-Steering Active Particles
论文作者
论文摘要
我们考虑一个移动的目标和一个活跃的捕获剂，被模型为一种智能活跃的棕色粒子，能够感测瞬时目标位置并相应地调整其运动方向。两个空间维度的分析和仿真研究表明，追求性能取决于自我推测，主动重新定位和随机噪声之间的相互作用。发现噪声具有两个相反的效果：（i）有必要打扰目标周围的常规，准胸椎轨迹，并且（ii）通过增加追求者的行进距离来减慢追求。我们还提出了一种策略，根据圆形目标轨迹对积极的追随者的运动性进行分类。
We consider a moving target and an active pursing agent, modeled as an intelligent active Brownian particle capable of sensing the instantaneous target location and adjust its direction of motion accordingly. An analytical and simulation study in two spatial dimensions reveals that pursuit performance depends on the interplay between self-propulsion, active reorientation, and random noise. Noise is found to have two opposing effects: (i) it is necessary to disturb regular, quasi-elliptical trajectories around the target, and (ii) slows down pursuit by increasing the traveled distance of the pursuer. We also propose a strategy to sort active pursuers according to their motility by circular target trajectories.
