Noisy Pursuit and Pattern Formation of Self-Steering Active Particles
arXiv:2203.07153 · doi:10.1088/1367-2630/ac924f
Abstract
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.
4 figures
References in corpus (10)
- Novel type of phase transition in a system of self-driven particles
- The hydrodynamics of swimming microorganisms
- Motility-Induced Phase Separation
- Physics of Microswimmers - Single Particle Motion and Collective Behavior
- Meso-scale turbulence in living fluids
- Large-scales patterns in a minimal cognitive flocking model: incidental leaders, nematic patterns, and aggregates
- A Lattice Boltzmann Model for Squirmers
- Optimising low-Reynolds-number predation via optimal control and reinforcement learning
- A single predator charging a herd of prey: effects of self volume and predator-prey decision-making
- Hunting active Brownian particles: Learning optimal behavior