A Hierarchical Multi-Robot Mapping Architecture Subject to Communication Constraints
This work provides a scalable, efficient, and robust architecture for hybrid multi-robot mapping systems, which is important for scenarios where communication range is limited.
This paper addresses multi-robot mapping in environments with communication constraints, where robots coordinate with a central server to explore and build a global map. The proposed hierarchical architecture enables robots to choose frontier points for maximum information gain while maintaining communication through an ad-hoc relay network.
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.