  In advanced human-robot interaction tasks, visual target navigation is
crucial for autonomous robots navigating unknown environments. While numerous
approaches have been developed in the past, most are designed for single-robot
operations, which often suffer from reduced efficiency and robustness due to
environmental complexities. Furthermore, learning policies for multi-robot
collaboration are resource-intensive. To address these challenges, we propose
Co-NavGPT, an innovative framework that integrates Large Language Models (LLMs)
as a global planner for multi-robot cooperative visual target navigation.
Co-NavGPT encodes the explored environment data into prompts, enhancing LLMs'
scene comprehension. It then assigns exploration frontiers to each robot for
efficient target search. Experimental results on Habitat-Matterport 3D (HM3D)
demonstrate that Co-NavGPT surpasses existing models in success rates and
efficiency without any learning process, demonstrating the vast potential of
LLMs in multi-robot collaboration domains. The supplementary video, prompts,
and code can be accessed via the following link:
https://sites.google.com/view/co-navgpt
