This invention provides a UAV visual language navigation method based on predictive semantic occupancy representation, comprising: acquiring multimodal visual observations and six-degree-of-freedom
pose data; extracting semantic features using a large visual
language model and back-projecting them to construct a local three-dimensional semantic occupancy representation; performing target existence likelihood
inference on the observation boundary and unexplored airspace based on the semantic prior of the
large model to generate predictive semantic occupancy representations;
parsing natural language commands to extract target semantic description vectors, aligning them with the predictive representations across modalities, and calculating semantic response scores; extracting candidate frontiers based on semantic response scores and
obstacle avoidance constraints, calculating comprehensive exploration utility to select local navigation target points; performing local
trajectory planning that satisfies kinematic constraints, driving the UAV to fly along a collision-free smooth trajectory, and updating the environmental representation online in a
closed loop based on real physical observations until the mission is completed.