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Multi-robot exploration is a field which tackles the challenge of exploring apreviously unknown environment with a number of robots. This is especiallyrelevant for search and rescue operations where time is essential. Currentstate of the art approaches are able to explore a given environment with alarge number of robots by assigning them to frontiers. However, this assignmentgenerally favors large frontiers and hence omits potentially valuablemedium-sized frontiers.  In this paper we showcase a novel multi-robot exploration algorithm, whichimproves and adapts the existing approaches. Through the addition ofinformation gain based ranking we improve the exploration time for closed urbanenvironments while maintaining similar exploration performance compared to thestate-of-the-art for open environments. Accompanying this paper, we furtherpublish our research code in order to lower the barrier to entry for furthermulti-robot exploration research. We evaluate the performance in threesimulated scenarios, two urban and one open scenario, where our algorithmoutperforms the state of the art by 5% overall.

coExplore: Combining multiple rankings for multi-robot exploration