Visual target navigation is a critical capability for autonomous robots operating in unknown environments, particularly in human-robot interaction scenarios. While classical and learning-based methods have shown promise, most existing approaches lack common-sense reasoning and are typically designed for single-robot settings, leading to reduced efficiency and robustness in complex environments. To address these limitations, we introduce Co-NavGPT, a novel framework that integrates a Vision Language Model (VLM) as a global planner to enable common-sense multi-robot visual target navigation. Co-NavGPT aggregates sub-maps from multiple robots with diverse viewpoints into a unified global map, encoding robot states and frontier regions. The VLM uses this information to assign frontiers across the robots, facilitating coordinated and efficient exploration. Experiments on the Habitat-Matterport 3D (HM3D) demonstrate that Co-NavGPT outperforms existing baselines in terms of success rate and navigation efficiency, without requiring task-specific training. Ablation studies further confirm the importance of semantic priors from the VLM. We also validate the framework in real-world scenarios using quadrupedal robots. Supplementary video and code are available at:this https URL.
View on arXiv@article{yu2025_2310.07937,
title={ Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models },
author={ Bangguo Yu and Qihao Yuan and Kailai Li and Hamidreza Kasaei and Ming Cao },
journal={arXiv preprint arXiv:2310.07937},
year={ 2025 }
}