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UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility

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arxiv 2501.02341 v2 pith:FCWTR5TI submitted 2025-01-04 cs.RO cs.AI

Yonglin Tian , Fei Lin , Yiduo Li , Tengchao Zhang , Qiyao Zhang , Xuan Fu , Jun Huang , Xingyuan Dai
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Yutong Wang Chunwei Tian Bai Li Yisheng Lv Levente Kovács Fei-Yue Wang
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classification cs.ROcs.AI
keywords uavsllmsagenticintelligenceavailablecapabilitieslow-altitudemeet
verification ladder T0 review T1 audit T2 compute T3 formal
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Low-altitude mobility, exemplified by unmanned aerial vehicles (UAVs), has introduced transformative advancements across various domains, like transportation, logistics, and agriculture. Leveraging flexible perspectives and rapid maneuverability, UAVs extend traditional systems' perception and action capabilities, garnering widespread attention from academia and industry. However, current UAV operations primarily depend on human control, with only limited autonomy in simple scenarios, and lack the intelligence and adaptability needed for more complex environments and tasks. The emergence of large language models (LLMs) demonstrates remarkable problem-solving and generalization capabilities, offering a promising pathway for advancing UAV intelligence. This paper explores the integration of LLMs and UAVs, beginning with an overview of UAV systems' fundamental components and functionalities, followed by an overview of the state-of-the-art in LLM technology. Subsequently, it systematically highlights the multimodal data resources available for UAVs, which provide critical support for training and evaluation. Furthermore, it categorizes and analyzes key tasks and application scenarios where UAVs and LLMs converge. Finally, a reference roadmap towards agentic UAVs is proposed, aiming to enable UAVs to achieve agentic intelligence through autonomous perception, memory, reasoning, and tool utilization. Related resources are available at https://github.com/Hub-Tian/UAVs_Meet_LLMs.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Where, What, Why: Towards Explainable Driver Attention Prediction

    cs.CV 2025-06 conditional novelty 7.0 of 10

    W3DA adds semantic and causal labels to four driver gaze datasets, and the LLada model predicts attention maps, attended semantics, and reasons in one end-to-end system.

  2. AERMANI-VLM: Structured Prompting and Reasoning for Aerial Manipulation with Vision Language Models

    cs.RO 2025-11 conditional novelty 6.0 of 10

    Structured prompting plus a discrete skill library lets a frozen VLM direct aerial manipulation, reaching 87.5% simulated and 80% hardware success in pick-and-place tasks.

  3. When Large Language Models Meet UAV Projects: An Empirical Study from Developers' Perspective

    cs.SE 2025-09 conditional novelty 6.0 of 10

    The first empirical taxonomy of LLM tasks in UAVs, with an academia-industry comparison and survey, shows LLMs are used mainly for planning and interaction, not direct control.

  4. RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control

    cs.CR 2026-07 conditional novelty 5.0 of 10

    An architecture that mediates LLM computer-use agents for UAV control by compiling agent decisions into validated, time-bounded, evidence-logged skill invocations, with a prototype on OpenClaw/PX4/OP-TEE.

  5. AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoring

    cs.AI 2025-10 reject novelty 5.0 of 10

    AIC-VDS uses trainable attention to shrink sensor data prompts for an LLM, and simulations show lower packet loss than two baselines in multi-UAV monitoring.

  6. SkyVLN: Vision-and-Language Navigation and NMPC Control for UAVs in Urban Environments

    cs.RO 2025-07 conditional novelty 5.0 of 10

    An LLM-and-NMPC drone navigation framework that reports 42.4% success on unseen AVDN test data, versus 16.6% for NavGPT, using spatial verbalization and a path memory graph.

  7. Taking Flight with Dialogue: Enabling Natural Language Control for PX4-based Drone Agent

    cs.RO 2025-06 conditional novelty 5.0 of 10

    An open-source ROS2/PX4 framework using locally hosted LLMs and VLMs enables natural language drone commands, with the best simulated mission success rate at 40%.

  8. Mathematical Reasoning for Unmanned Aerial Vehicles: A RAG-Based Approach for Complex Arithmetic Reasoning

    cs.AI 2025-06 conditional novelty 4.0 of 10

    RAG improved one LLM's exact-match accuracy on a small UAV math benchmark, but most reported gains compare different models rather than the same model with and without retrieval.

  9. UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs

    cs.RO 2025-06 conditional novelty 3.0 of 10

    A narrative survey defines 'agentic UAVs' as drones with perception, cognition, control, and communication layers and catalogs applications and challenges across eight domains.

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