B-FAME: An Adaptive Area Division Algorithm for Multi-Robot Exploration and Mapping

Abstract

Multi-robot systems enhance exploration efficiency but encounter challenges in coordination and collaboration. Effective area division is key to balancing workloads, reducing redundancy, and maintaining connectivity for reliable exploration. To address these challenges, this study introduces B-FAME, an adaptive area division algorithm designed for efficient multi-robot exploration and mapping. The algorithm integrates frontier clustering using Dijkstra-based distance matrices, Voronoi-based partitioning, area balancing while maintaining connectivity, and dynamic reallocation of exploration areas as new frontiers emerge. Simulation results illustrate the algorithm’s ability to achieve balanced workload distribution among robots, ensuring no overlap and optimized coverage. Furthermore, the dynamic reallocation stage successfully updates partitions during exploration, maintaining coordination without restarting the entire process.