自驱动活性粒子的噪声追踪与图案形成
Noisy Pursuit and Pattern Formation of Self-Steering Active Particles
AI总结：
研究智能活性布朗粒子在噪声环境下追踪移动目标的性能，发现噪声对追踪具有双重作用，并提出基于圆形目标轨迹对追踪者进行分类的策略。
摘要：
我们考虑一个移动目标和一个活性追踪智能体，该智能体被建模为一个能够感知目标瞬时位置并相应调整其运动方向的智能活性布朗粒子。在二维空间中的解析和模拟研究表明，追踪性能取决于自驱动、主动重定向和随机噪声之间的相互作用。研究发现噪声具有两种相反的作用：(i) 它是干扰目标周围规则的准椭圆轨迹所必需的，(ii) 它通过增加追踪者的移动距离来减缓追踪过程。我们还提出了一种通过圆形目标轨迹根据运动能力对活性追踪者进行分类的策略。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
