  Visual navigation tasks are critical for household service robots. As these
tasks become increasingly complex, effective communication and collaboration
among multiple robots become imperative to ensure successful completion. In
recent years, large language models (LLMs) have exhibited remarkable
comprehension and planning abilities in the context of embodied agents.
However, their application in household scenarios, specifically in the use of
multiple agents collaborating to complete complex navigation tasks through
communication, remains unexplored. Therefore, this paper proposes a framework
for decentralized multi-agent navigation, leveraging LLM-enabled communication
and collaboration. By designing the communication-triggered dynamic leadership
organization structure, we achieve faster team consensus with fewer
communication instances, leading to better navigation effectiveness and
collaborative exploration efficiency. With the proposed novel communication
scheme, our framework promises to be conflict-free and robust in multi-object
navigation tasks, even when there is a surge in team size.
