  We study multi-agent coverage algorithms for autonomous monitoring and patrol
in urban environments. We consider scenarios in which a team of flying agents
uses downward facing cameras (or similar sensors) to observe the environment
outside of buildings at street-level. Buildings are considered obstacles that
impede movement, and cameras are assumed to be ineffective above a maximum
altitude. We study multi-agent urban coverage problems related to this
scenario, including: (1) static multi-agent urban coverage, in which agents are
expected to observe the environment from static locations, and (2) dynamic
multi-agent urban coverage where agents move continuously through the
environment. We experimentally evaluate six different multi-agent coverage
methods, including: three types of ergodic coverage (that avoid buildings in
different ways), lawn-mower sweep, voronoi region based control, and a naive
grid method. We evaluate all algorithms with respect to four performance
metrics (percent coverage, revist count, revist time, and the integral of area
viewed over time), across four types of urban environments [low density, high
density] x [short buildings, tall buildings], and for team sizes ranging from 2
to 25 agents. We believe this is the first extensive comparison of these
methods in an urban setting. Our results highlight how the relative performance
of static and dynamic methods changes based on the ratio of team size to search
area, as well the relative effects that different characteristics of urban
environments (tall, short, dense, sparse, mixed) have on each algorithm.
