Vision-Language Navigation (VLN) aims to empower robots with the ability to perform long-horizon navigation in unfamiliar environments based on complex linguistic instructions. Its success critically hinges on establishing an efficient ``language-understanding -- visual-perception -- embodied-execution'' closed loop. Existing methods often suffer from perceptual distortion and decision drift in complex, long-distance tasks due to the cognitive overload of a single agent. Inspired by distributed cognition theory, this paper proposes MA-CoNav, a Multi-Agent Collaborative Navigation framework. This framework adopts a ``Master-Slave'' hierarchical agent collaboration architecture, decoupling and distributing the perception, planning, execution, and memory functions required for navigation tasks to specialized agents. Specifically, the Master Agent is responsible for global orchestration, while the Subordinate Agent group collaborates through a clear division of labor: an Observation Agent generates environment descriptions, a Planning Agent performs task decomposition and dynamic verification, an Execution Agent handles simultaneous mapping and action, and a Memory Agent manages structured experiences. Furthermore, the framework introduces a ``Local-Global'' dual-stage reflection mechanism to dynamically optimize the entire navigation pipeline. Empirical experiments were conducted using a real-world indoor dataset collected by a Limo Pro robot, with no scene-specific fine-tuning performed on the models throughout the process. The results demonstrate that MA-CoNav comprehensively outperforms existing mainstream VLN methods across multiple metrics.

翻译：视觉语言导航旨在赋予机器人基于复杂语言指令在陌生环境中执行长视野导航的能力。其成功关键取决于建立一个高效的“语言理解-视觉感知-具身执行”闭环。现有方法在复杂、长距离任务中常因单智能体的认知过载而遭受感知失真与决策漂移。受分布式认知理论启发，本文提出MA-CoNav，一种多智能体协同导航框架。该框架采用“主-从”分层智能体协作架构，将导航任务所需的感知、规划、执行与记忆功能解耦并分配给专业化智能体。具体而言，主智能体负责全局协调，而从属智能体组通过明确分工进行协作：观察智能体生成环境描述，规划智能体执行任务分解与动态验证，执行智能体处理同步建图与动作，记忆智能体管理结构化经验。此外，框架引入“局部-全局”双阶段反思机制，以动态优化整个导航流程。实证实验使用Limo Pro机器人采集的真实室内数据集进行，过程中未对模型进行任何场景特定的微调。结果表明，MA-CoNav在多项指标上全面优于现有主流VLN方法。