Vision-Language Models as copilots for Autonomous UAV Navigation: Analysis of Latency and Reliability in Degraded Environments

The integration of Vision-Language Models (VLMs) in autonomous Unmanned Aerial Vehicles (UAVs) offers unprecedented semantic reasoning capabilities. However, real-time closed-loop navigation requires not only low inference latency but also obedience to structured flight commands. This paper proposes a hybrid FSM-VLM control architecture for UAVs in GPS-free environments. The system combines a deterministic Finite State Machine (FSM) for low-level physical control with an asynchronous VLM copilot for high-level semantic pathfinding. We evaluate three models with different parameter scales in a Software-In-The-Loop (SITL) simulation. The framework isolates and measures syntax errors at the format level versus semantic hallucinations at the logic level in a normal and degraded scenario. This study demonstrates that parameter scaling, and not pure latency, remains the primary bottleneck for the safe and compatible integration of VLM into autonomous flights.