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Large Language Model-Driven Autonomous UAV Systems: Technical Evolution, Core Architectures, and Critical Challenges

  • Hao Wang,   
  • Meijie Zhang *

Received: 11 May 2026 | Revised: 26 Jul 2026 | Accepted: 23 Aug 2026 | Published: 20 Sep 2026

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.

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DOI
10.53941/ic.2026.100011
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Volume 2, Issue 3
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Wang, H.; Zhang, M. Large Language Model-Driven Autonomous UAV Systems: Technical Evolution, Core Architectures, and Critical Challenges. Intelligence & Control 2026, 2 (3), 4. https://doi.org/10.53941/ic.2026.100011.
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