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.

翻译：近年来，多机器人系统在电力巡检、自动驾驶车队等领域的广泛应用，使得多机器人技术成为机器人领域的研究热点。本文研究了未知环境下的多机器人协同探索问题，提出了一种基于多智能体强化学习的训练框架与决策策略。具体而言，我们提出了一种基于非对称拓扑表示的地图构建框架（ATR-Mapping），该方法结合了基于原始栅格地图和基于拓扑结构方法的优势，从原始栅格地图中提取结构信息，并与基于几何距离信息构建的拓扑图相结合进行决策。利用这种拓扑图表示，我们采用基于拓扑图匹配的决策网络为每个机器人分配相应的边界点，作为长期目标点进行决策。我们在Gazebo和Gibson仿真环境中对提出的算法进行了实景测试与应用。验证表明，与现有方法相比，所提方法在性能上实现了一定程度的提升。