  The formation and dynamics of swarms is wide spread in living systems, from
bacterial bio-films to schools of fish and flocks of birds. We study this
emergent collective behavior in a model of active Brownian particles with
visual-perception-induced steering and alignment interactions through
agent-based simulations. The dynamics, shape, and internal structure of the
emergent aggregates, clusters, and swarms of these intelligent active Brownian
particles (iABPs) is determined by the maneuverabilities $\Omega_v$ and
$\Omega_a$, quantifying the steering based on the visual signal and polar
alignment, respectively, the propulsion velocity, characterized by the
P{\'e}clet number $Pe$, the vision angle $\theta$, and the orientational noise.
Various non-equilibrium dynamical aggregates -- like motile worm-like swarms
and millings, and close-packed or dispersed clusters -- are obtained. Small
vision angles imply the formation of small clusters, while large vision angles
lead to more complex clusters. In particular, a strong polar-alignment
maneuverability $\Omega_a$ favors elongated worm-like swarms, which display
super-diffusive motion over a much longer time range than individual ABPs,
whereas a strong vision-based maneuverability $\Omega_v$ favors compact, nearly
immobile aggregates. Swarm trajectories show long persistent directed motion,
interrupted by sharp turns. Milling rings, where a worm-like swarm bites its
own tail, emerge for an intermediate regime of $Pe$ and vision angles. Our
results offer new insights into the behavior of animal swarms, and provide
design criteria for swarming microbots.
