A Hierarchical Multi-Robot Mapping Architecture Subject to Communication Constraints

TL;DR

This paper presents a scalable and robust multi-robot mapping architecture that effectively manages communication constraints by hierarchical coordination and relay networks, demonstrated through simulation experiments.

Contribution

It introduces a hierarchical multi-robot mapping framework that accounts for communication range limitations and employs backup algorithms for robust exploration.

Findings

The architecture maintains effective communication and mapping performance under communication constraints.

Simulation results show improved scalability and robustness in unknown environments.

The system efficiently coordinates robots to maximize information gain while managing relay networks.

Abstract

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.