  Robotic swarms are decentralized multi-robot systems whose members use local
information from proximal neighbors to execute simple reactive control laws
that result in emergent collective behaviors. In contrast, members of a general
multi-robot system may have access to global information, all- to-all
communication or sophisticated deliberative collabora- tion. Some algorithms in
the literature are applicable to robotic swarms. Others require the extra
complexity of general multi- robot systems. Given an application domain, a
system designer or supervisory operator must choose an appropriate system or
algorithm respectively that will enable them to achieve their goals while
satisfying mission constraints (e.g. bandwidth, energy, time limits). In this
paper, we compare representative swarm and general multi-robot algorithms in
two application domains - navigation and dynamic area coverage - with respect
to several metrics (e.g. completion time, distance trav- elled). Our objective
is to characterize each class of algorithms to inform offline system design
decisions by engineers or online algorithm selection decisions by supervisory
operators. Our contributions are (a) an empirical performance comparison of
representative swarm and general multi-robot algorithms in two application
domains, (b) a comparative analysis of the algorithms based on the theory of
information invariants, which provides a theoretical characterization supported
by our empirical results.
