无人机视觉语言导航模型综述:从感知理解到智能决策OA

The emergence of multimodal large language models(MLLMs)has laid the foundation for the vision-language-navigation paradigm,which integrates visual perception,natural language understanding,and navigation control within a unified strategic framework.This paradigm has been rapidly adopted in the UAV domain,attemp-ting to enable UAVs to understand natural language instructions,reason in three-dimensional environments,and make flight decisions.Compared with traditional modular navigation approaches,the end-to-end framework based on MLLMs can simultaneously process linguistic and visual signals,directly mapping perceptual information into control commands.However,a systematic review of UAV-VLN remains scarce.This paper presents a comprehen-sive review of recent advances in this area:from early modular solutions to reason-centric vision-language-action models.It elucidates how visual,linguistic,and control information are progressively integrated to enhance autono-mous navigation capabilities.It further summarizes existing datasets and evaluation protocols,including simulation tasks in both indoor and outdoor complex environments as well as real-world UAV flight trajectories,with evaluation metrics covering success rate,time cost,and semantic comprehension.Finally,it identifies key challenges,inclu-ding difficulties in cross-modal alignment,insufficient real-time responsiveness in dynamic environments,high an-notation costs,and poor decision-making robustness in complex scenarios.This paper outlines new pathways and future research directions for UAV autonomous navigation research.It highlights the potential of MLLMs in enhan-cing intelligent decision-making and interpretability of UAVs,and serves as a reference for research and practice in achieving safe and efficient autonomous flight of UAVs.