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

Abstract: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.

Robotics (cs.RO)

arXiv:2609.26084 [cs.RO]

(or arXiv:2609.26084v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.26084 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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