LLM‐guided semantic‐aware trajectory optimization for UAV path planning using environmental context

Summary Autonomous unmanned aerial vehicle path planning in urban environments requires both geometric safety and semantic awareness. Conventional planners based on fixed costs or predefined risk models struggle to reflect mission‐dependent semantic priorities. We present a large language model (LLM)‐guided semantic‐aware planning framework in which the LLM serves as a bounded high‐level decision module. OpenStreetMap data are transformed into class‐wise semantic potential fields, and a route‐relative context and natural‐language mission description are used to infer validated semantic modulation coefficients. A deterministic B‐spline optimizer generates smooth paths by jointly considering multiple factors. Comparisons with existing models demonstrate a competitive tradeoff between path efficiency and semantic safety. Mission‐conditioned experiments reveal that different mission descriptions produce distinct semantic policies and path geometries under identical environmental conditions. Reliability analysis indicates that the deterministic downstream planner maintains stable safety‐related performance under context perturbations and stochastic LLM outputs.