A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
Aermani-vlm: Structured prompting and reasoning for aerial manipu- lation with vision language models
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This survey organizes aerial vision-language navigation methods into five architectural categories, critically reviews evaluation infrastructure, and synthesizes seven open problems for LLM/VLM integration.
A universal LLM-to-drone interface is implemented via the Model Context Protocol (MCP) and Mavlink, demonstrated with real UAV flight control and simulated flights using live map data.
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