In recent years, the widespread application of multi-robot systems in areas such as power inspection, autonomous vehicle fleets has made multi-robot technology a research hotspot in the field of robotics. This paper investigates multi-robot cooperative exploration in unknown environments, proposing a training framework and decision strategy based on multi-agent reinforcement learning. Specifically we propose a Asymmetric Topological Representation based mapping framework (ATR-Mapping), combining the advantages of methods based on raw grid maps and methods based on topology, the structural information from the raw grid maps is extracted and combined with a topological graph constructed based on geometric distance information for decision-making. Leveraging this topological graph representation, we employs a decision network based on topological graph matching to assign corresponding boundary points to each robot as long-term target points for decision-making. We conducts testing and application of the proposed algorithms in real world scenarios using the Gazebo and Gibson simulation environments. It validates that the proposed method, when compared to existing methods, achieves a certain degree of performance improvement.

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