  As a long-term vision in the field of artificial intelligence, the core goal
of embodied intelligence is to improve the perception, understanding, and
interaction capabilities of agents and the environment. Vision-language
navigation (VLN), as a critical research path to achieve embodied intelligence,
focuses on exploring how agents use natural language to communicate effectively
with humans, receive and understand instructions, and ultimately rely on visual
information to achieve accurate navigation. VLN integrates artificial
intelligence, natural language processing, computer vision, and robotics. This
field faces technical challenges but shows potential for application such as
human-computer interaction. However, due to the complex process involved from
language understanding to action execution, VLN faces the problem of aligning
visual information and language instructions, improving generalization ability,
and many other challenges. This survey systematically reviews the research
progress of VLN and details the research direction of VLN with embodied
intelligence. After a detailed summary of its system architecture and research
based on methods and commonly used benchmark datasets, we comprehensively
analyze the problems and challenges faced by current research and explore the
future development direction of this field, aiming to provide a practical
reference for researchers.
