Noisy pursuit and pattern formation of self-steering active particles

Abstract We consider a moving target and an active pursing agent, modeled as intelligent Brownian particle capable of sensing the instantaneous location adjusting its direction motion accordingly. An analytical simulation study in two spatial dimensions reveals that pursuit performance depends on interplay between self-propulsion, reorientation, limited maneuverability, random noise. Noise is found to have opposing effects: (i) it necessary disturb regular, quasi-elliptical orbits around target, (ii) slows down by increasing traveled distance pursuer. For stationary we predict universal scaling behavior mean pursuer–target first-passage time function Pe 2 /Ω, where Péclet number characterizes activity Ω maneuverability. Importantly, variable /Ω implicitly level thermal or A similar for but modified velocity ratio α = u 0 / v pursuer velocities , respectively. also propose strategy sort pursuers according their motility circular trajectories.