A Graph Partitioning Approach for Fast Exploration with Multi-Robot Coordination

A multi-robot exploration approach is suggested in this paper that works on the premise that the topo-metric map of the indoor environment is known a priori. Genetic Algorithms (GAs) are used for spatial partitioning of the topo-metric graph of the environment. Each spatial partition, which represents the sub-graph, is apportioned to a unique robot by using the Hungarian method for task assignment in conjunction with Bully Algorithm for leader election. In the case of robot(s) failure, graph re-partitioning and single item auctions are used for re-assigning the remaining task(s) of the failed robot(s) to other robots. The proposed approach performs better than a recent state-of-the-art strategy that employs Delaunay triangulation and multi-prim algorithm for multi-robot exploration. Empirical results obtained in simulation by varying the number of robots in two different and complex environments prove the efficacy of the proposed approach.

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