  Multi-robot systems are an efficient method to explore and map an unknown
environment. The simulataneous localization and mapping (SLAM) algorithm is
common for single robot systems, however multiple robots can share respective
map data in order to merge a larger global map. This thesis contributes to the
multi-robot mapping problem by considering cases in which robots have
communication range limitations. The architecture coordinates a team of robots
and the central server to explore an unknown environment by exploiting a
hierarchical choice structure. The coordination algorithms ensure that the
hierarchy of robots choose frontier points that provide maximum information
gain, while maintaining viable communication amongst themselves and the central
computer through an ad-hoc relay network. In addition, the robots employ a
backup choice algorithm in cases when no valid frontier points remain by
arranging the communication relay network as a fireline back to the source.
  This work contributes a scalable, efficient, and robust architecture towards
hybrid multi-robot mapping systems that take into account communication range
limitations. The architecture is tested in a simulation environment using
various maps.
