Large Language Model-Driven Autonomous UAV Systems: Technical Evolution, Core Architectures, and Critical Challenges
Abstract
The rapid development of large language models (LLMs) has expanded the capabilities of autonomous unmanned aerial vehicle (UAV) systems in naturallanguage instruction understanding, multimodal perception, and decision-making. This survey reviews the technical evolution, system architectures, and deployment challenges of LLM-driven UAVs across perception, planning, control, multi-agent coordination, and edge–cloud computing. Beyond cataloguing representative systems, we separate semantic-reasoning latency, control timing, power, task outcomes, hardware, and validation settings to avoid misleading cross-platform comparisons. We further analyze Sim2Real gaps, intermittent connectivity, hallucination-induced action risk, cyberattacks, and privacy leakage. In this survey, a semantically adaptive safety certificate denotes a runtime-verifiable CBF/MPC/STL constraint whose safe set or margin is parameterized by grounded task and scene semantics but enforced by a deterministic safety layer outside the generative model. Future directions emphasize hierarchical lightweight reasoning, communication-aware autonomy, formally bounded semantic adaptation, and airworthiness-oriented assurance.
