Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot systems. Previous methods primarily focused on single-robot centralized planning strategies, which severely limited exploration efficiency. Recent research has considered decentralized planning strategies for multiple robots, assigning separate planning models to each robot, but these approaches often overlook communication costs. In this work, we propose Multimodal Chain-of-Thought Co-Navigation (MCoCoNav), a modular approach that utilizes multimodal Chain-of-Thought to plan collaborative semantic navigation for multiple robots. MCoCoNav combines visual perception with Vision Language Models (VLMs) to evaluate exploration value through probabilistic scoring, thus reducing time costs and achieving stable outputs. Additionally, a global semantic map is used as a communication bridge, minimizing communication overhead while integrating observational results. Guided by scores that reflect exploration trends, robots utilize this map to assess whether to explore new frontier points or revisit history nodes. Experiments on HM3D_v0.2 and MP3D demonstrate the effectiveness of our approach. Our code is available at https://github.com/FrankZxShen/MCoCoNav.git.

Efficient data transmission and reasonable task allocation are important to improve multi-robot exploration efficiency. However, most communication data types typically contain redundant information and thus require massive communication volume. Moreover, exploration-oriented task allocation is far from trivial and becomes even more challenging for resource-limited unmanned aerial vehicles (UAVs). In this paper, we propose a fast and communication-efficient multi-UAV exploration method for exploring large environments. We first design a multi-robot dynamic topological graph (MR-DTG) consisting of nodes representing the explored and exploring regions and edges connecting nodes. Supported by MR-DTG, our method achieves efficient communication by only transferring the necessary information required by exploration planning. To further improve the exploration efficiency, a hierarchical multi-UAV exploration method is devised using MR-DTG. Specifically, the \emph{graph Voronoi partition} is used to allocate MR-DTG's nodes to the closest UAVs, considering the actual motion cost, thus achieving reasonable task allocation. To our knowledge, this is the first work to address multi-UAV exploration using \emph{graph Voronoi partition}. The proposed method is compared with a state-of-the-art method in simulations. The results show that the proposed method is able to reduce the exploration time and communication volume by up to 38.3\% and 95.5\%, respectively. Finally, the effectiveness of our method is validated in the real-world experiment with 6 UAVs. We will release the source code to benefit the community.