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Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application

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A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 16 April 2026 | Viewed by 19075

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Special Issue Editors

Prof. Dr. Haokui Zhang

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Guest Editor

School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710129, China
Interests: remote sensing; image processing; visual language model
Special Issues, Collections and Topics in MDPI journals

Dr. Liang Wang

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Guest Editor

School of Cybersecurity, Northwestern Polytechnical University, Xi'an 710072, China
Interests: wireless communication; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Jingtao Sun

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Guest Editor

Department of Electrical and Computer Engineering, National University of Singapore, Singapre 117583, Singapre
Interests: 3D computer vision; multi-modal; UAV object tracking

Dr. Xizhe Xue

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Guest Editor

Department of Aerospace and Geodesy, School of Engineering and Design, Technical University of Munich, 85521 Munich, Germany
Interests: remote sensing; visual language model; UAV object tracking

Special Issue Information

Dear Colleagues,

Due to their efficiency, flexibility, and versatility, unmanned aerial vehicles (UAVs) are widely applied in various fields, including environmental monitoring, agriculture, urban planning, geological exploration, and security surveilance. In these applications, artificial intelligence technology has always provided crucial support.

In recent years, with breakthrough advancements in large language models (LLMs) such as ChatGPT, the development of artificial intelligence technology has entered a new phase. Unlike previous artificial intelligence models that used discriminative architectures, LLMs adopt a recursive generative architecture, in which they generate results by predicting the next token, offering greater flexibility. VLMs also integrate visual information into LLMs, enhancing their multimodal capabilities. These new technologies create new possibilities for key technologies and fields of UAV application. Very recently, researchers have explored the incorporation of LLMs into UAV applications, such as UAV image analysis and UAV communication systems, achieving promising performance metrics and demonstrating significant potential. However, at present, the application of these new technologies in the UAV field is still limited, and their full potential has yet to be realized. On the other hand, applying these new technologies to various aspects of UAVs still presents numerous challenges. For example, in the image domain, UAVs enable multiple modalities of images with different perspectives from common views. In the communication domain, UAVs with higher real-time performance and stronger anti-interference capabilities are required.

Moreover, as UAVs continue to evolve technologically, their impact on socio-economic development is becoming increasingly significant. Emerging research is now exploring the role of UAVs in economic and social contexts, for example, analyses of the UAV industry’s geographic distribution and identifying its influencing factors, as well as investigations into the innovation networks that drive UAV industry growth. These topics not only extend the technical frontiers of UAV research but also provide critical insights into how UAV applications contribute to regional development and industrial innovation.

To advance the application of new AI technologies in UAVs, this Special Issue aims to provide a platform for researchers to share their latest findings and engage in discussions on the opportunities, challenges, and solutions associated with integrating AI into UAV applications. In addition to technical advancements in image analysis, communication systems, and multimodal data processing, we encourage submissions that address the economic and social dimensions of UAV industries. By bridging technical innovation with socio-economic research, we hope that this Special Issue will also foster interdisciplinary collaboration.

This Special Issue welcomes high-quality submissions that provide the research community with the most recent advancements in artificial intelligence for UAV applications and interdisciplinary collaboration, including, but not limited to, the following topics:

UAV image super-resolution.
UAV image-based object detection/segmentation/classification.
UAV image-based multimodal fusion.
UAV object tracking.
Lightweight perception model for UAV platform.
VLM-based human–UAV interactions.
Adversarial attacks on and defenses for UAV time-series data.
Software engineering and software security for UAVs.
LLM-based UAV communications.
Economic- and social-dimension research into UAV industries.

We look forward to receiving your original research articles and reviews.

Prof. Dr. Haokui Zhang
Dr. Liang Wang
Dr. Jingtao Sun
Dr. Xizhe Xue
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

UAV platform
image processing
large language model
visual language model
UAV communication
UAVs in socio-economic development

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Published Papers (10 papers)

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Research

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19 pages, 806 KB

Open AccessArticle

DPAD: Distribution-Driven Perturbation-Adaptive Defense for UAV Time-Series Regression Under Hybrid Adversarial Attacks

by Bo Xu, Zhiqiang Liu, Zhongjun Dong, Kaiqi Huang, Xiaopeng Huang, Haolin Zhu, Jun Wei, Yong Li, Yangbai Zhang and Xiuping Li

Drones 2025, 9(12), 828; https://doi.org/10.3390/drones9120828 - 28 Nov 2025

Viewed by 272

Abstract

Time-series regression models are essential components in unmanned aerial vehicles (UAVs) for accurate trajectory and state prediction. Nevertheless, they are still vulnerable to hybrid adversarial attacks, which can lead to a compromised mission performance and cause huge economic loss. For this challenge, we propose the Distribution-driven Perturbation-Adaptive Defense (DPAD) framework. DPAD improves perturbation detection with Gaussian Mixture Model (GMM)-based feature augmentation that raises the accuracy of perturbation strength prediction, increasing from 0.685 to 0.943

R^{2}

, and dynamically chooses a suitable defense sub-model or the original model for adaptive correction. The experiments on UAV_Delivery show that DPAD significantly enhances robustness by achieving about 80% reduction in prediction errors under hybrid attacks while maintaining high accuracy on clean samples with an inference speed of 2.744 ms per sample. The proposed framework can scale up an effective solution to defend UAV time-series regression models against complex adversarial scenarios. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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24 pages, 2374 KB

Open AccessArticle

NightTrack: Joint Night-Time Image Enhancement and Object Tracking for UAVs

by Xiaomin Huang, Yunpeng Bai, Jiaman Ma, Ying Li, Changjing Shang and Qiang Shen

Drones 2025, 9(12), 824; https://doi.org/10.3390/drones9120824 - 27 Nov 2025

Viewed by 332

Abstract

UAV-based visual object tracking has recently become a prominent research focus in computer vision. However, most existing trackers are primarily benchmarked under well-illuminated conditions, largely overlooking the challenges that may arise in night-time scenarios. Although attempts exist to restore image brightness via low-light image enhancement before feeding frames to a tracker, such two-stage pipelines often struggle to strike an effective balance between the competing objectives of enhancement and tracking. To address this limitation, this work proposes NightTrack, a unified framework that optimizes both low-light image enhancement and UAV object tracking. While boosting image visibility, NightTrack not only explicitly preserves but also reinforces the discriminative features required for robust tracking. To improve the discriminability of low-light representations, Pyramid Attention Modules (PAMs) are introduced to enhance multi-scale contextual cues. Moreover, by jointly estimating illumination and noise curves, NightTrack mitigates the potential adverse effects of low-light environments, leading to significant gains in precision and robustness. Experimental results on multiple night-time tracking benchmarks demonstrate that NightTrack outperforms state-of-the-art methods in night-time scenes, exhibiting strong promises for further development. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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39 pages, 1423 KB

Open AccessArticle

A Transformer-Based Self-Organizing UAV Swarm for Assisting an Emergency Communications System

by Isaac López-Villegas, Kevin Javier Medina-Gómez, Javier Izquierdo-Reyes, Daniel Colin-García, Hugo Gustavo González-Hernández and Rogelio Bustamante-Bello

Drones 2025, 9(11), 769; https://doi.org/10.3390/drones9110769 - 7 Nov 2025

Viewed by 1330

Abstract

Natural disasters often compromise telecommunications infrastructure, leading to unstable services or complete communication blackouts that hinder rescue operations and exacerbate victims’ distress. Rapidly deployable alternatives are, therefore, critical to sustaining reliable connectivity in affected regions. This work proposes a self-organizing multi-Unmanned Aerial Vehicle (UAV) swarm network capable of providing stand-alone and temporary coverage to both victims and emergency personnel in areas with compromised infrastructure through access points installed onboard UAVs. To address the challenges of partial observability in decentralized coordination, we introduce the Soft Transformer Recurrent Graph Network (STRGN), a novel encoder–decoder architecture inspired by the transformer model and extending the Soft Deep Recurrent Graph Network (SDRGN). By leveraging multi-head and cross-attention mechanisms, the STRGN captures higher-order spatiotemporal relationships, enabling UAVs to integrate information about neighbor proximity and ground user density when selecting actions. This facilitates adaptive positioning strategies that enhance coverage, fairness, and connectivity under dynamic conditions. Simulation results show that transformer-based approaches, including STRGN, the Soft Transformer Graph Network, and the Transformer Graph Network, consistently outperform SDRGN, and the Soft Deep Graph Network, and Deep Graph Network baselines by approximately

16 %

across core metrics, while also demonstrating improved scalability across diverse terrains and swarm sizes. These findings highlight STRGN’s potential as a resilient framework for UAV-assisted communications in disaster response. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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23 pages, 9193 KB

Open AccessArticle

An Algorithm for Planning Coverage of an Area with Obstacles with a Heterogeneous Group of Drones Using a Genetic Algorithm and Parameterized Polygon Decomposition

by Kirill Yakunin, Yan Kuchin, Elena Muhamedijeva, Adilkhan Symagulov and Ravil I. Mukhamediev

Drones 2025, 9(9), 658; https://doi.org/10.3390/drones9090658 - 18 Sep 2025

Cited by 1 | Viewed by 1002

Abstract

The paper presents an algorithm for planning agricultural field surveying routes in the presence of obstacles, designed to address precision agriculture tasks. Unlike classical methods, which are typically limited to straightforward zigzag (Zamboni) traversal and basic perimeter-based obstacle avoidance, the proposed algorithm accounts for heterogeneous unmanned aerial vehicles (UAVs) of varying types, ranges, costs, and speeds, along with a mobile ground platform that enables drone takeoff and landing at multiple points along the road. The key innovation lies in a two-stage optimization procedure: initially, a random set of field partitions into multiple sub-polygons with predefined area proportions (considering internal obstacles) is generated. Subsequently, the optimal partitioning is selected, and based on this, a genetic algorithm is applied to optimize flight parameters, including flight angle, entry points, composition, and sequence of drone launches, and the ground platform route. This approach achieves more localized coverage of individual field segments, with each segment serviced by an appropriate drone type, while also enabling flexible movement of the ground platform, thereby reducing unnecessary flights. This brings down the price of the coverage by 10–30% in some cases. The concluding section discusses future directions, including the incorporation of three-dimensional terrain considerations, dynamic factors (such as changing weather conditions and drone stoppages due to technical issues), and automated collision avoidance in intersecting route segments. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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42 pages, 13345 KB

Open AccessArticle

UAV Operations and Vertiport Capacity Evaluation with a Mixed-Reality Digital Twin for Future Urban Air Mobility Viability

by Junjie Zhao, Zhang Wen, Krishnakanth Mohanta, Stefan Subasu, Rodolphe Fremond, Yu Su, Ruechuda Kallaka and Antonios Tsourdos

Drones 2025, 9(9), 621; https://doi.org/10.3390/drones9090621 - 3 Sep 2025

Cited by 2 | Viewed by 2003

Abstract

This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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24 pages, 3172 KB

Open AccessArticle

A DDPG-LSTM Framework for Optimizing UAV-Enabled Integrated Sensing and Communication

by Xuan-Toan Dang, Joon-Soo Eom, Binh-Minh Vu and Oh-Soon Shin

Drones 2025, 9(8), 548; https://doi.org/10.3390/drones9080548 - 1 Aug 2025

Viewed by 1636

Abstract

This paper proposes a novel dual-functional radar-communication (DFRC) framework that integrates unmanned aerial vehicle (UAV) communications into an integrated sensing and communication (ISAC) system, termed the ISAC-UAV architecture. In this system, the UAV’s mobility is leveraged to simultaneously serve multiple single-antenna uplink users (UEs) and perform radar-based sensing tasks. A key challenge stems from the target position uncertainty due to movement, which impairs matched filtering and beamforming, thereby degrading both uplink reception and sensing performance. Moreover, UAV energy consumption associated with mobility must be considered to ensure energy-efficient operation. We aim to jointly maximize radar sensing accuracy and minimize UAV movement energy over multiple time steps, while maintaining reliable uplink communications. To address this multi-objective optimization, we propose a deep reinforcement learning (DRL) framework based on a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) network. By leveraging historical target trajectory data, the model improves prediction of target positions, enhancing sensing accuracy. The proposed DRL-based approach enables joint optimization of UAV trajectory and uplink power control over time. Extensive simulations validate that our method significantly improves communication quality and sensing performance, while ensuring energy-efficient UAV operation. Comparative results further confirm the model’s adaptability and robustness in dynamic environments, outperforming existing UAV trajectory planning and resource allocation benchmarks. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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18 pages, 10604 KB

Open AccessFeature PaperArticle

Fast Detection of Plants in Soybean Fields Using UAVs, YOLOv8x Framework, and Image Segmentation

by Ravil I. Mukhamediev, Valentin Smurygin, Adilkhan Symagulov, Yan Kuchin, Yelena Popova, Farida Abdoldina, Laila Tabynbayeva, Viktors Gopejenko and Alexey Oxenenko

Drones 2025, 9(8), 547; https://doi.org/10.3390/drones9080547 - 1 Aug 2025

Cited by 3 | Viewed by 1446

Abstract

The accuracy of classification and localization of plants on images obtained from the board of an unmanned aerial vehicle (UAV) is of great importance when implementing precision farming technologies. It allows for the effective application of variable rate technologies, which not only saves chemicals but also reduces the environmental load on cultivated fields. Machine learning algorithms are widely used for plant classification. Research on the application of the YOLO algorithm is conducted for simultaneous identification, localization, and classification of plants. However, the quality of the algorithm significantly depends on the training set. The aim of this study is not only the detection of a cultivated plant (soybean) but also weeds growing in the field. The dataset developed in the course of the research allows for solving this issue by detecting not only soybean but also seven weed species common in the fields of Kazakhstan. The article describes an approach to the preparation of a training set of images for soybean fields using preliminary thresholding and bound box (Bbox) segmentation of marked images, which allows for improving the quality of plant classification and localization. The conducted research and computational experiments determined that Bbox segmentation shows the best results. The quality of classification and localization with the application of Bbox segmentation significantly increased (f1 score increased from 0.64 to 0.959, mAP50 from 0.72 to 0.979); for a cultivated plant (soybean), the best classification results known to date were achieved with the application of YOLOv8x on images obtained from the UAV, with an f1 score = 0.984. At the same time, the plant detection rate increased by 13 times compared to the model proposed earlier in the literature. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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26 pages, 5914 KB

Open AccessArticle

BiDGCNLLM: A Graph–Language Model for Drone State Forecasting and Separation in Urban Air Mobility Using Digital Twin-Augmented Remote ID Data

by Zhang Wen, Junjie Zhao, An Zhang, Wenhao Bi, Boyu Kuang, Yu Su and Ruixin Wang

Drones 2025, 9(7), 508; https://doi.org/10.3390/drones9070508 - 19 Jul 2025

Cited by 1 | Viewed by 1647

Abstract

Accurate prediction of drone motion within structured urban air corridors is essential for ensuring safe and efficient operations in Urban Air Mobility (UAM) systems. Although real-world Remote Identification (Remote ID) regulations require drones to broadcast critical flight information such as velocity, access to large-scale, high-quality broadcast data remains limited. To address this, this study leverages a Digital Twin (DT) framework to augment Remote ID spatio-temporal broadcasts, emulating the sensing environment of dense urban airspace. Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. The model forecasts near-future speed variations in surrounding drones, supporting proactive conflict avoidance in constrained air corridors. Results from the AirSUMO co-simulation platform and a DT replica of the Cranfield University campus show that BiDGCNLLM outperforms state-of-the-art time series models in short-term velocity prediction. Compared to Transformer-LSTM, BiDGCNLLM marginally improves the

R^{2}

by 11.59%. This study introduces the integration of LLMs into dynamic graph-based drone prediction. It shows the potential of Remote ID broadcasts to enable scalable, real-time airspace safety solutions in UAM. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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Review

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30 pages, 34352 KB

Open AccessReview

Infrared and Visible Image Fusion Techniques for UAVs: A Comprehensive Review

by Junjie Li, Cunzheng Fan, Congyang Ou and Haokui Zhang

Drones 2025, 9(12), 811; https://doi.org/10.3390/drones9120811 - 21 Nov 2025

Viewed by 1269

Abstract

Infrared–visible (IR–VIS) image fusion is becoming central to unmanned aerial vehicle (UAV) perception, enabling robust operation across day–night cycles, backlighting, haze or smoke, and large viewpoint or scale changes. However, for practical applications some challenges still remain: visible images are illumination-sensitive; infrared imagery suffers thermal crossover and weak texture; motion and parallax cause cross-modal misalignment; UAV scenes contain many small or fast targets; and onboard platforms face strict latency, power, and bandwidth budgets. Given these UAV-specific challenges and constraints, we provide a UAV-centric synthesis of IR–VIS fusion. We: (i) propose a taxonomy linking data compatibility, fusion mechanisms, and task adaptivity; (ii) critically review learning-based methods—including autoencoders, CNNs, GANs, Transformers, and emerging paradigms; (iii) compare explicit/implicit registration strategies and general-purpose fusion frameworks; and (iv) consolidate datasets and evaluation metrics to reveal UAV-specific gaps. We further identify open challenges in benchmarking, metrics, lightweight design, and integration with downstream detection, segmentation, and tracking, offering guidance for real-world deployment. A continuously updated bibliography and resources are provided and discussed in the main text. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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26 pages, 1616 KB

Open AccessReview

Unmanned Aerial Vehicles in Last-Mile Parcel Delivery: A State-of-the-Art Review

by Almodather Mohamed and Moataz Mohamed

Drones 2025, 9(6), 413; https://doi.org/10.3390/drones9060413 - 6 Jun 2025

Cited by 8 | Viewed by 6555

Abstract

Unmanned Aerial Vehicles (UAVs) are being increasingly implemented in parcel delivery applications. The scientific progress in this field is progressing exponentially. However, there is a notable gap in synthesizing recent research progress in UAV applications for last-mile delivery. This review study addresses this gap and conducts an in-depth review of UAV research for last-mile delivery across seven domains: environmental performance, economic impacts, social impacts, policy and regulations, routing and scheduling, charging infrastructure, and energy consumption. The review indicates that UAVs promise to reduce last-mile delivery emissions by 71% and costs by 96.5% compared to truck delivery. Saturated knowledge analysis is conducted across the seven domains to identify potential research gaps. Additionally, this review identifies key knowledge gaps, including variability in environmental and cost data, limitations associated with 2D modelling, and a lack of experimental validation. Future research interventions aimed at advancing UAV adoption in last-mile delivery applications are discussed. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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