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Drones
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Advances in AI Large Models for Unmanned Aerial Vehicles
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Advances in AI Large Models for Unmanned Aerial Vehicles

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: 10 March 2026 | Viewed by 6378

Special Issue Editors

Dr. Jie Zeng

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: URLLC; novel multiple access satellite communications; non-orthogonal technologies; GPTs and communications; edge intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tiejun Lv

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: signal processing; communications theory; networking; GPTs; edge computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xin Su

E-Mail Website
Guest Editor
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Interests: broadband wireless access; wireless and mobile network architecture; self-organizing network; software-defined radio; cooperative communications; GPTs; edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Integrating artificial intelligence (AI) large models with unmanned aerial vehicles (UAVs) introduces a transformative model for intelligent low-altitude services, where autonomous drone networks play a pivotal role. This advancement is crucial for the emerging low-altitude economy, optimizing real-time decision-making, reducing latency, and enhancing operational efficiency in sectors such as logistics, surveillance, and disaster response. AI large models strengthen UAV operations by enabling sophisticated natural language processing, allowing UAVs to interpret commands, navigate complex environments, and interact dynamically with users. This combination creates a flexible platform capable of advanced data analysis, situational awareness, and autonomous functionalities, all essential for the future of low-altitude applications. Nonetheless, challenges such as energy efficiency, secure UAV coordination, and data protection still need to be addressed. As 6G networks progress, integrating AI large models with UAV swarms will significantly enhance low-altitude economic activities by fostering efficient, secure, and context-aware drone services, revolutionizing industries that rely on autonomous aerial operations.

This Special Issue will gather pioneering research on integrating AI large models and UAVs, aiming to explore their synergies and advance the development of intelligent, autonomous, and low-latency aerial services in next-generation networks. Topics of interest include, but are not limited to, the following:

  • AI large models for UAV sensor networks;
  • AI large models for real-time information processing for UAVs;
  • AI large models for UAV localization and navigation;
  • AI large models for UAV perception model;
  • AI large models for UAV obstacles and collision avoidance;
  • AI large models for UAV trajectory generation and motion planning;
  • AI large models for UAV cooperative sensing;
  • AI large models for UAV sensor fusion;
  • AI large models for UAV threat analysis;
  • Transparent decision-making UAV swarm driven by AI large models;
  • Testbed, benchmark, and simulation studies in UAV network powered by AI large models;
  • Security and privacy challenges and potential resolutions in UAV networks supported by AI large models;
  • Security architecture for UAV systems with AI large models;
  • Interpretability of AI large models over UAVs.

Dr. Jie Zeng
Prof. Dr. Tiejun Lv
Prof. Dr. Xin Su
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI large models
  • UAV network
  • navigation
  • sensor fusion
  • UAV swarm
  • cooperative sensing

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Published Papers (6 papers)

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Research

30 pages, 3072 KB  
Article
An RNN-Enhanced Diverse Curriculum-Driven Learning Algorithm Based on Deep Reinforcement Learning for POMDPs with Limited Experience
by Ke Li, Kun Zhang, Ziqi Wei, Haiyin Piao, Binlin Yuan, Boxuan Wang and Jiangbo Cheng
Drones 2026, 10(2), 142; https://doi.org/10.3390/drones10020142 - 17 Feb 2026
Viewed by 39
Abstract
Autonomous flight is a critical capability for unmanned aerial vehicles (UAVs), enabling applications in wildlife and plant protection, infrastructure inspection, search and rescue, and other complex missions. Although some learning-based methods have achieved considerable progress, traditional algorithms still struggle with real-world challenges, due [...] Read more.
Autonomous flight is a critical capability for unmanned aerial vehicles (UAVs), enabling applications in wildlife and plant protection, infrastructure inspection, search and rescue, and other complex missions. Although some learning-based methods have achieved considerable progress, traditional algorithms still struggle with real-world challenges, due to the partially observable nature of environments and limited experience regarding the properties of dynamic unknown environments where threats and targets are movable and unpredictable. To address these difficulties, it is necessary to achieve autonomous guidance for UAVs performing long-range missions in dynamic environments (LRGDEs), and to develop a novel end-to-end algorithm that can overcome partial observability under limited state transitions. In this paper, we propose an RNN-enhanced Diverse Curriculum-driven Learning Algorithm (REDCRL) based on deep reinforcement learning. We modify the structure of traditional actor–critic networks and introduce Bi-LSTM into policy networks (referred to as Bi-LSTM-modified Policy Networks (BLPNs)) to alleviate observation incompleteness. Furthermore, to fully exploit the potential value of data and mitigate the problem of insufficient samples, we develop an Adaptive Multi-Feature Evaluation Experience Replay (AMFER) method to reshape the process of experience replay buffer construction and sampling. In addition, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is adopted to optimize UAV-maneuver decision policies. Compared with traditional algorithms, the proposed algorithm can accelerate policy convergence and improve the performance of the trained policy. Full article
(This article belongs to the Special Issue Advances in AI Large Models for Unmanned Aerial Vehicles)
23 pages, 5771 KB  
Article
Intelligent Control for Quadrotors Based on a Novel Method: TD3-ADRC
by Runyu Cai, Liang Zhang, Wutao Qin and Jie Yan
Drones 2026, 10(2), 110; https://doi.org/10.3390/drones10020110 - 2 Feb 2026
Viewed by 281
Abstract
To address the requirements for multi-channel decoupling and high-precision control in quadrotor UAV systems, this paper proposes a novel intelligent controller (TD3-ADRC) which integrates Active Disturbance Rejection Control (ADRC) with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Firstly, the dynamic model [...] Read more.
To address the requirements for multi-channel decoupling and high-precision control in quadrotor UAV systems, this paper proposes a novel intelligent controller (TD3-ADRC) which integrates Active Disturbance Rejection Control (ADRC) with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Firstly, the dynamic model of the quadrotor is established. Secondly, a parameterized tanh function is introduced and applied to design the tracking differentiator, extended state observer, and nonlinear feedback control law. Then, the TD3 learning mechanism is incorporated to automatically learn and optimize controller parameters, thereby significantly enhancing the system’s disturbance rejection capability. Finally, simulation studies comparing conventional PID, ADRC, DDPG and the proposed TD3-ADRC algorithms are conducted in Simulink. In addition, a bench test system is developed using the PX4 flight controller. Experimental results show that, under complex environmental conditions, the proposed TD3-ADRC controller outperforms both conventional PID and linear ADRC methods in terms of reliability and adaptability, validating the effectiveness of the proposed control approach. Full article
(This article belongs to the Special Issue Advances in AI Large Models for Unmanned Aerial Vehicles)
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24 pages, 3434 KB  
Article
Hierarchical Route Planning Framework and MMDQN Agent-Based Intelligent Obstacle Avoidance for UAVs
by Boyu Dong, Yuzhen Zhang, Peiyuan Yuan, Shuntong Lu, Tao Huang and Gong Zhang
Drones 2026, 10(1), 57; https://doi.org/10.3390/drones10010057 - 13 Jan 2026
Viewed by 406
Abstract
Efficient route planning technology is the core support for ensuring the successful execution of unmanned aerial vehicle (UAV) flight missions. In this paper, the coordination issue of global route planning and local real-time obstacle avoidance in complex mountainous environments is studied. To deal [...] Read more.
Efficient route planning technology is the core support for ensuring the successful execution of unmanned aerial vehicle (UAV) flight missions. In this paper, the coordination issue of global route planning and local real-time obstacle avoidance in complex mountainous environments is studied. To deal with this issue, a hierarchical route planning framework is designed, including global route planning and AI-based local route re-planning using deep reinforcement learning, exhibiting both flexible versatility and practical coordination and deployment efficiency. Throughout the entire flight, the local route re-planning task triggered by dynamic threats can be executed in real time. Meanwhile, a multi-model DQN (MMDQN) agent with a Monte Carlo traversal iterative learning (MCTIL) strategy is designed for local route re-planning. Compared to existing methods, this agent can be directly used to generate local obstacle avoidance routes in various scenarios at any time during the flight, which simplifies the complicated structure and training process of conventional deep reinforcement learning (DRL) agents in dynamic, complex environments. Using the framework structure and MMDQN agent for local route re-planning ensures the safety and efficiency of the mission, as well as local obstacle avoidance during global flights. These performances are verified through simulations based on actual terrain data. Full article
(This article belongs to the Special Issue Advances in AI Large Models for Unmanned Aerial Vehicles)
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22 pages, 7712 KB  
Article
Adaptive Edge Intelligent Joint Optimization of UAV Computation Offloading and Trajectory Under Time-Varying Channels
by Jinwei Xie and Dimin Xie
Drones 2026, 10(1), 21; https://doi.org/10.3390/drones10010021 - 31 Dec 2025
Viewed by 347
Abstract
With the rapid development of mobile edge computing (MEC) and unmanned aerial vehicle (UAV) communication networks, UAV-assisted edge computing has emerged as a promising paradigm for low-latency and energy-efficient computation. However, the time-varying nature of air-to-ground channels and the coupling between UAV trajectories [...] Read more.
With the rapid development of mobile edge computing (MEC) and unmanned aerial vehicle (UAV) communication networks, UAV-assisted edge computing has emerged as a promising paradigm for low-latency and energy-efficient computation. However, the time-varying nature of air-to-ground channels and the coupling between UAV trajectories and computation offloading decisions significantly increase system complexity. To address these challenges, this paper proposes an Adaptive UAV Edge Intelligence Framework (AUEIF) for joint UAV computation offloading and trajectory optimization under dynamic channels. Specifically, a dynamic graph-based system model is constructed to characterize the spatio-temporal correlation between UAV motion and channel variations. A hierarchical reinforcement learning-based optimization framework is developed, in which a high-level actor–critic module is responsible for generating coarse-grained UAV flight trajectories, while a low-level deep Q-network performs fine-grained optimization of task offloading ratios and computational resource allocation in real time. In addition, an adaptive channel prediction module leveraging long short-term memory (LSTM) networks is integrated to model temporal channel state transitions and to assist policy learning and updates. Extensive simulation results demonstrate that the proposed AUEIF achieves significant improvements in end-to-end latency, energy efficiency, and overall system stability compared with conventional deep reinforcement learning approaches and heuristic-based schemes while exhibiting strong robustness against dynamic and fluctuating wireless channel conditions. Full article
(This article belongs to the Special Issue Advances in AI Large Models for Unmanned Aerial Vehicles)
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26 pages, 5139 KB  
Article
Towards Scalable Intelligence: A Low-Complexity Multi-Agent Soft Actor–Critic for Large-Model-Driven UAV Swarms
by Zhaoyu Liu, Wenchu Cheng, Liang Zeng and Xinxin He
Drones 2025, 9(11), 788; https://doi.org/10.3390/drones9110788 - 12 Nov 2025
Cited by 1 | Viewed by 967
Abstract
Heterogeneous unmanned aerial vehicle (UAV) swarms are becoming critical components of next-generation non-terrestrial networks, enabling tasks such as communication relay, spectrum monitoring, cooperative sensing, and navigation. Yet, their heterogeneity and multifunctionality bring severe challenges in task allocation and resource scheduling, where traditional multi-agent [...] Read more.
Heterogeneous unmanned aerial vehicle (UAV) swarms are becoming critical components of next-generation non-terrestrial networks, enabling tasks such as communication relay, spectrum monitoring, cooperative sensing, and navigation. Yet, their heterogeneity and multifunctionality bring severe challenges in task allocation and resource scheduling, where traditional multi-agent reinforcement learning methods often suffer from high algorithmic complexity, lengthy training times, and deployment difficulties on resource-constrained nodes. To address these issues, this paper proposes a low-complexity multi-agent soft actor–critic (MASAC) framework that combines parameter sharing (shared actor with device embeddings and shared-backbone twin critics), lightweight network design (fixed-width residual MLP with normalization), and robust training mechanisms (minimum-bias twin-critic updates and entropy scheduling) within the CTDE paradigm. Simulation results show that the proposed framework achieves more than 14-fold parameter compression and over a 93% reduction in training time, while maintaining or improving performance in terms of the delay–energy utility function. These advances substantially reduce computational overhead and accelerate convergence, providing a practical pathway for deploying multi-agent reinforcement learning in large-scale heterogeneous UAV clusters and supporting diverse mission scenarios under stringent resource and latency constraints. Full article
(This article belongs to the Special Issue Advances in AI Large Models for Unmanned Aerial Vehicles)
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30 pages, 14021 KB  
Article
LLM-LCSA: LLM for Collaborative Control and Decision Optimization in UAV Cluster Security
by Hua Song, Zheng Yang, Haitao Du, Yuting Zhang, Jie Zeng and Xinxin He
Drones 2025, 9(11), 779; https://doi.org/10.3390/drones9110779 - 9 Nov 2025
Cited by 1 | Viewed by 3221
Abstract
With the development of unmanned aerial vehicle (UAV) technology, multimachine collaborative operations have become the core model for increasing mission effectiveness. However, large-scale UAV clusters face challenges such as dynamic security threats, heterogeneous data fusion difficulties, and resource-constrained decision-making delays. Traditional single-machine intelligent [...] Read more.
With the development of unmanned aerial vehicle (UAV) technology, multimachine collaborative operations have become the core model for increasing mission effectiveness. However, large-scale UAV clusters face challenges such as dynamic security threats, heterogeneous data fusion difficulties, and resource-constrained decision-making delays. Traditional single-machine intelligent architectures have limitations when addressing new threats, such as insufficient real-time response capabilities. To address these issues, this paper presnts an LLM-layered collaborative security architecture (LLM-LCSA) for multimachine collaborative security. This architecture optimizes the spatiotemporal fusion efficiency of multisource asynchronous data through cloud–edge–end collaborative deployment, combining an end lightweight LLM, an edge medium LLM, and a cloud-based foundation LLM. Additionally, a Mixture of Experts (MoEs) intelligent algorithm that dynamically activates the most relevant expert models by leveraging a threat–expert association matrix is introduced, thereby increasing the accuracy of complex threat identification and dynamic adaptability. Moreover, a resource-aware multi-objective optimization model is constructed to generate optimal decisions under resource constraints. Simulation results indicate that compared with traditional methods, LLM-LCSA achieves an average 7.92% improvement in the threat detection accuracy, reduces the system’s total response time by 44.52%, and enables resource scheduling during off-peak periods. This architecture provides an efficient, intelligent, and scalable solution for secure collaboration among UAV swarms. Future research should further explore its application potential in 6G network integration and large-scale swarm environments. Full article
(This article belongs to the Special Issue Advances in AI Large Models for Unmanned Aerial Vehicles)
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