Drones

20 pages, 6382 KiB

Open AccessArticle

A Consensus Control Method for Unmanned Aerial Vehicle (UAV) Swarm Based on Molecular Dynamics

by Peng Xu, Xuefei Sun, Minggang Yu, Jintao Liu and Tingting Bai

Drones 2025, 9(6), 404; https://doi.org/10.3390/drones9060404 - 30 May 2025

In the field of unmanned aerial vehicle (UAV) swarm control, achieving efficient consensus is paramount. This paper proposes a molecular dynamics-based UAV swarm consensus control strategy. The strategy emulates the random motion of molecules in a vacuum, establishing a framework for UAV swarm [...] Read more.

In the field of unmanned aerial vehicle (UAV) swarm control, achieving efficient consensus is paramount. This paper proposes a molecular dynamics-based UAV swarm consensus control strategy. The strategy emulates the random motion of molecules in a vacuum, establishing a framework for UAV swarm behavior that aligns with principles from molecular dynamics. The framework is built upon the Vicsek model, a cornerstone in swarm dynamics, and employs the Lennard-Jones and Morse potential functions to model the attractive and repulsive forces between UAVs. Through simulation, this paper explores how different potential functions and swarm sizes affect consensus control, finding the Morse potential particularly advantageous. Theoretical analysis and experimental results demonstrate that these potential functions not only prevent UAV collisions but also facilitate the emergence of swarming behaviors, thereby enhancing the collaborative efficiency and stability of UAV swarms. This advancement significantly boosts the overall performance of swarm control, paving the way for the deployment of UAV swarms in complex environments. Full article

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23 pages, 2430 KiB

Open AccessArticle

Error-Constrained Fixed-Time Synchronized Trajectory Tracking Control for Unmanned Airships with Disturbances

by Jie Chen, Jiace Yuan and Ruohan Li

Drones 2025, 9(6), 403; https://doi.org/10.3390/drones9060403 - 29 May 2025

Abstract

This work focuses on fixed-time synchronized trajectory tracking control for unmanned airships subject to time-varying error constraints and unknown disturbances. First, to guarantee strict adherence to prescribed performance bounds, an error transformation function (ETF) is integrated into the control algorithm, which can ensure [...] Read more.

This work focuses on fixed-time synchronized trajectory tracking control for unmanned airships subject to time-varying error constraints and unknown disturbances. First, to guarantee strict adherence to prescribed performance bounds, an error transformation function (ETF) is integrated into the control algorithm, which can ensure all tracking errors remain within specified constraints throughout the convergence process. Then, a Norm-Normalized sign (NNS) function is incorporated to develop the control scheme, guaranteeing simultaneous convergence of all tracking error components. Additionally, a novel fixed-time synchronized disturbance observer (FTSDO) is constructed and implemented to achieve precise disturbance estimation while ensuring synchronous convergence of the estimation errors. Finally, the developed control strategy is analytically verified to guarantee fixed-time synchronized stability (FTSS). To assess its performance, multiple simulations are executed. The results clearly demonstrate the proposed control scheme enables the airship to track the prescribed trajectory precisely in fixed time, and the convergence of all tracking error components is achieved synchronously. Full article

(This article belongs to the Special Issue Design and Flight Control of Low-Speed Near-Space Unmanned Systems)

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22 pages, 32941 KiB

Open AccessArticle

Assessment of Building Vulnerability to Tsunami in Ancon Bay, Peru, Using High-Resolution Unmanned Aerial Vehicle Imagery and Numerical Simulation

by Carlos Davila, Angel Quesquen, Fernando Garcia, Brigitte Puchoc, Oscar Solis, Julian Palacios, Jorge Morales and Miguel Estrada

Drones 2025, 9(6), 402; https://doi.org/10.3390/drones9060402 - 29 May 2025

Abstract

Traditional tsunami vulnerability assessments often rely on empirical models and field surveys, which can be time-consuming and have limited accuracy. In this study, we propose a novel approach that integrates high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry with numerical simulation to improve vulnerability assessment [...] Read more.

Traditional tsunami vulnerability assessments often rely on empirical models and field surveys, which can be time-consuming and have limited accuracy. In this study, we propose a novel approach that integrates high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry with numerical simulation to improve vulnerability assessment efficacy in Ancon Bay, Lima, Peru, by using the Papathoma Tsunami Vulnerability Assessment (PTVA-4) model. For this purpose, a detailed 3D representation of the study area was generated using UAV-based oblique photogrammetry, enabling the extraction of building attributes. Additionally, a high-resolution numerical tsunami simulation was conducted using the TUNAMI-N2 model for a potential worst-case scenario that may affect the Central Peru subduction zone, incorporating topographic and land-use data obtained with UAV-based nadir photogrammetry. The results indicate that the northern region of Ancon Bay exhibits higher relative vulnerability levels due to greater inundation depths and more tsunami-prone building attributes. UAV-based assessments provide a rapid and detailed method for evaluating building vulnerability. These findings indicate that the proposed methodology is a valuable tool for supporting coastal risk planning and disaster preparedness in tsunami-prone areas. Full article

(This article belongs to the Special Issue Drones for Natural Hazards)

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31 pages, 8088 KiB

Open AccessArticle

Communication Infrastructure Design for Reliable UAV Operations in Air Mobility Corridors

by Igor Kabashkin, Duman Iskakov, Roman Topilskiy, Gulnar Tlepiyeva, Timur Sultanov and Zura Sansyzbayeva

Drones 2025, 9(6), 401; https://doi.org/10.3390/drones9060401 - 29 May 2025

Abstract

The integration of unmanned aerial vehicles (UAVs) into urban air mobility (UAM) systems necessitates reliable and uninterrupted communication infrastructure to ensure safety, control, and data continuity within designated air corridors. This paper proposes and evaluates four radio repeater deployment strategies to support robust [...] Read more.

The integration of unmanned aerial vehicles (UAVs) into urban air mobility (UAM) systems necessitates reliable and uninterrupted communication infrastructure to ensure safety, control, and data continuity within designated air corridors. This paper proposes and evaluates four radio repeater deployment strategies to support robust UAV communication in urban environments: Strategy 1 with non-overlapping radio coverage, Strategy 2 with fully overlapping coverage zones, Strategy 3 with alternating redundancy between repeater pairs, and Strategy 4 with full duplication of overlapping coverage. A continuous-time Markov modeling approach is employed to quantify communication availability under varying traffic loads and failure conditions. The strategies are assessed based on infrastructure requirements, reliability performance, and suitability for segmented and non-linear corridor geometries. The results show that increasing redundancy significantly improves reliability: for example, channel unavailability drops from 35% under Strategy 1 (no redundancy) to less than 0.5% under Strategy 4 (full duplication). Strategy 3 achieves a balanced performance, maintaining unavailability below 1% with approximately 50% fewer resources than Strategy 4. A case study in the Greenline district of Astana, Kazakhstan, illustrates the practical application of the framework, demonstrating how hybrid deployment strategies can address different operational and environmental demands. The results show that increasing redundancy significantly enhances availability, with Strategy 3 offering the most efficient balance between reliability and resource use. The proposed methodology provides a scalable foundation for designing resilient UAV communication systems to support future urban airspace operations. Full article

(This article belongs to the Section Innovative Urban Mobility)

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17 pages, 5469 KiB

Open AccessArticle

An Experiment on Multi-Angle Sun Glitter Remote Sensing of Water Surface Using Multi-UAV

by Chen Wang, Huaguo Zhang, Guanghong Liao, Wenting Cao, Juan Wang, Dongling Li and Xiulin Lou

Drones 2025, 9(6), 400; https://doi.org/10.3390/drones9060400 - 28 May 2025

Abstract

Unmanned aerial vehicle (UAV) remote sensing has become an important tool for modern remote sensing technology with its low cost and high flexibility. Sun glitter (SG) remote sensing based on satellite platforms shows great potential in the fields of marine dynamic environment and [...] Read more.

Unmanned aerial vehicle (UAV) remote sensing has become an important tool for modern remote sensing technology with its low cost and high flexibility. Sun glitter (SG) remote sensing based on satellite platforms shows great potential in the fields of marine dynamic environment and marine oil spill, but the analysis and application of SG images based on UAV need to be further studied. In this study, we conduct a multi-angle water surface SG remote sensing experiment using multi-UAV and collect images under different observation parameters. Then, we analyze and discuss the SG signal in the multi-angle images, especially the distribution and intensity of SG. In addition, a model for extracting SG signals from images based on region-based dark pixel retrieval is proposed in this study. Since the current Cox-Munk model is only applicable to statistical SG, the extracted SG images are reduced in resolution by mean filtering. Based on the multi-angle SG remote sensing model, the water surface roughness and equivalent refractive index are estimated. The estimated results are compared with measured and literature data. Additionally, the influence of different observation angle combinations on the inversion results is also discussed. The results of the study show that multi-angle SG remote sensing of water surface based on UAVs provides a new idea for the analysis and application of image signals, which has an important role to play. Full article

(This article belongs to the Special Issue The Application of Image Processing and Signal Processing Techniques in Unmanned Aerial Vehicles)

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16 pages, 4008 KiB

Open AccessArticle

On the Flying Accuracy of Miniature Drones in Indoor Environments

by Nusin Akram, Ilker Kocabas and Orhan Dagdeviren

Drones 2025, 9(6), 399; https://doi.org/10.3390/drones9060399 - 28 May 2025

Abstract

Micro drones are becoming more popular in many areas, because they are small and fast enough to fly in tight and complex spaces. But they still have some significant problems. Their batteries drain fast, they cannot carry much weight, and their sensors and [...] Read more.

Micro drones are becoming more popular in many areas, because they are small and fast enough to fly in tight and complex spaces. But they still have some significant problems. Their batteries drain fast, they cannot carry much weight, and their sensors and computers are limited. These problems affect their flying performance and stability, which is very important for their missions. In this study, we evaluated the accuracy of mini drones in indoor environments. During hovering, the drones showed an average deviation of 77.9 cm, with a standard deviation of 26.4 cm, indicating moderate stability while stationary. In simple forward flights over 3 m, the average deviation increased to 92.6 cm, which showed slight drop in accuracy during movement. For more complex flight paths, such as L-shaped and square trajectories, the deviations increased to 141 cm and 245 cm, respectively. Full article

(This article belongs to the Special Issue Autonomous Drone Navigation in GPS-Denied Environments)

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14 pages, 7214 KiB

Open AccessArticle

Agroecological Alternatives for Substitution of Glyphosate in Orange Plantations (Citrus sinensis) Using GIS and UAVs

by María Guadalupe Galindo Mendoza, Abraham Cárdenas Tristán, Pedro Pérez Medina, Rita Schwentesius Rindermann, Tomás Rivas García, Carlos Contreras Servín and Oscar Reyes Cárdenas

Drones 2025, 9(6), 398; https://doi.org/10.3390/drones9060398 - 28 May 2025

Abstract

Field mapping is one of the most important aspects of precision agriculture, and community drones will be able to empower young rural entrepreneurs who will be the generational replacement of a new agrosocial paradigm. This research presents an agroecological participatory innovation methodology that [...] Read more.

Field mapping is one of the most important aspects of precision agriculture, and community drones will be able to empower young rural entrepreneurs who will be the generational replacement of a new agrosocial paradigm. This research presents an agroecological participatory innovation methodology that utilizes precision technology through geographic information systems and unmanned aerial vehicles to evaluate the integrated ecological management of weeds for glyphosate substitution in a transitional area of Citrus sinensis in San Luis Potosí, Mexico. Modeling methods and spatial analyses supported by intelligent georeference protocols were used to determine the number of weeds with tolerance and glyphosate resistance. Four control flights were conducted to monitor seven treatments. Glyphosate-resistant weeds were represented with the highest number of individuals and frequency in all experimental treatments. Although the treatment with maize stubble showed a slightly better result than the use of Mucuna pruriens mulch, which prevents the emergence of glyphosate resistant weeds before emergence, the second treatment is considered better in terms of the cost–benefit ratio, not only because of significantly lower cost but also because of the additional benefits it offers. Geospatial technologies will determine the nature of citrus and fruit tree agroecological treatments and highlight areas of the plot with binomial soil and plant nutrient deficiencies and pest and disease infestations, which will improve the timely application of bio-inputs through the development of accurate maps of agroecological transitions. Full article

(This article belongs to the Special Issue Advances in the Use of UAVs for Monitoring and Analysis of Forage and Agricultural Crops: Technologies, Methods and Applications)

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28 pages, 3529 KiB

Open AccessArticle

A Coverage-Based Cooperative Detection Method for CDUAV: Insights from Prediction Error Pipeline Modeling

by Jiong Li, Xianhai Feng, Yangchao He and Lei Shao

Drones 2025, 9(6), 397; https://doi.org/10.3390/drones9060397 - 27 May 2025

Abstract

To address the challenges of detection and acquisition caused by trajectory prediction errors during the midcourse–terminal guidance handover phase in cross-domain unmanned aerial vehicles (CDUAV), this study proposes a collaborative multi-interceptor detection coverage optimization method based on predictive error pipeline modeling. Firstly, we [...] Read more.

To address the challenges of detection and acquisition caused by trajectory prediction errors during the midcourse–terminal guidance handover phase in cross-domain unmanned aerial vehicles (CDUAV), this study proposes a collaborative multi-interceptor detection coverage optimization method based on predictive error pipeline modeling. Firstly, we employ nonlinear least squares to fit parameters for the motion model of CDUAV. By integrating error propagation theory, we derive a recursive expression for error pipelines under t-distribution and establish a parametric model for the target’s high-probability region (HPR). Next, we analyze target acquisition scenarios during guidance handover and reformulate the collaborative detection problem as a field-of-view (FOV) coverage optimization task on a two-dimensional detection plane. This framework incorporates the target HPR and the seeker detection FOV models, with an objective function defined for coverage optimization. Finally, inspired by wireless sensor network (WSN) coverage strategies, we implement the starfish optimization algorithm (SFOA) to enhance computational efficiency. Simulation results demonstrate that compared to Monte Carlo statistical methods, our parametric modeling approach reduces prediction error computation time from 15.82 s to 0.09 s while generating error pipeline envelopes with 99% confidence intervals, showing superior generalization capability. The proposed collaborative detection framework effectively resolves geometric coverage optimization challenges arising from mismatches between target HPR and FOV morphology, exhibiting rapid convergence and high computational efficiency. Full article

(This article belongs to the Special Issue Advanced Cross-Domain Unmanned Platform Command and Security Technology)

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28 pages, 3776 KiB

Open AccessArticle

Optimization Methods for Unmanned eVTOL Approach Sequencing Considering Flight Priority and Traffic Flow Imbalance

by Zhiqiang Wei, Xinlong Xiao, Xiangling Zhao and Jie Yuan

Drones 2025, 9(6), 396; https://doi.org/10.3390/drones9060396 - 25 May 2025

Abstract

Approach sequencing is important for multiple unmanned electric vertical take-off and landing (eVTOL) vehicles landing in vertiport. In this study, the additional intermediate transition ring (AIR) approach procedure in a balanced traffic flow scenario, the single ring movement-allowed (SRMA) approach procedure in an [...] Read more.

Approach sequencing is important for multiple unmanned electric vertical take-off and landing (eVTOL) vehicles landing in vertiport. In this study, the additional intermediate transition ring (AIR) approach procedure in a balanced traffic flow scenario, the single ring movement-allowed (SRMA) approach procedure in an imbalanced traffic flow scenario, and the additional ring and allowing of movement (ARAM) approach procedure in a mixed scenario are proposed and designed to improve the efficiency of approach sequencing. Furthermore, a priority loss classification method is proposed to consider the unmanned eVTOL flight priority difference. Finally, a multi-objective optimization model is constructed with the constraints of inflow, outflow, moment continuity, flow balance, and conflict avoidance. The objectives are minimizing the power consumption, total operation time, and priority loss. Comparison experiments are conducted, and the final results demonstrate that the ARAM approach procedure can reduce the average holding time by 8.4% and 7.6% less than the branch-queuing approach (BQA) and AIR in a balanced traffic flow scenario, respectively. The ARAM approach procedure can reduce the average holding time by 6.5% less than BQA in an imbalanced traffic flow scenario. Full article

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13 pages, 3371 KiB

Open AccessArticle

Marine Unmanned Surface Vehicle Measurements of Solar Irradiance Under Typhoon Conditions

by Ke Xu, Hongrong Shi, Hongbin Chen, Husi Letu, Jun Li, Wenying He, Xuehua Fan, Yaojiang Chen, Shuqing Ma and Xuefen Zhang

Drones 2025, 9(6), 395; https://doi.org/10.3390/drones9060395 - 25 May 2025

Abstract

Autonomous unmanned surface vehicles (USVs) offer transformative potential for collecting marine meteorological data under extreme weather conditions, yet their capability to provide reliable solar radiation measurements during typhoons remains underexplored. This study evaluates shortwave downward radiation (SWDR) data obtained by a solar-powered USV [...] Read more.

Autonomous unmanned surface vehicles (USVs) offer transformative potential for collecting marine meteorological data under extreme weather conditions, yet their capability to provide reliable solar radiation measurements during typhoons remains underexplored. This study evaluates shortwave downward radiation (SWDR) data obtained by a solar-powered USV (developed by IAP/CAS, Beijing, China) that successfully traversed Typhoon Sinlaku (2020), compared with Himawari-8 satellite products. The SUSV acquired 1 min resolution SWDR measurements near the typhoon center, while satellite data were collocated spatially and temporally for validation. Results demonstrate that the USV maintained uninterrupted operation and power supply despite extreme sea states, enabling continuous radiation monitoring. After averaging, high-frequency SWDR data exhibited minimal bias relative to Himawari-8 to mitigate wave-induced attitude effects, with a mean bias error (MBE) of 13.64 W m⁻² under cloudy typhoon conditions. The consistency between platforms confirms the SUSV’s capacity to deliver accurate in situ radiation data where traditional observations are scarce. This work establishes that autonomous SUSVs can critically supplement satellite validation and improve radiative transfer models in typhoon-affected oceans, addressing a key gap in severe weather oceanography. Full article

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35 pages, 1570 KiB

Open AccessReview

A Review on the Construction, Modeling, and Consistency of Digital Twins for Advanced Air Mobility Applications

by Tianxiong Zhang, Dominik Grzelak, Wanqi Zhao, Md Ashraful Islam, Hartmut Fricke and Uwe Aßmann

Drones 2025, 9(6), 394; https://doi.org/10.3390/drones9060394 - 24 May 2025

Abstract

The advent of Advanced Air Mobility (AAM) presents a transformative solution to multi-modal transportation. However, coordinating missions, as well as monitoring and controlling multiple Unmanned Aerial Vehicles (multi-UAV systems), remains a significant challenge. The adoption of digital twin (DT) technology has the potential [...] Read more.

The advent of Advanced Air Mobility (AAM) presents a transformative solution to multi-modal transportation. However, coordinating missions, as well as monitoring and controlling multiple Unmanned Aerial Vehicles (multi-UAV systems), remains a significant challenge. The adoption of digital twin (DT) technology has the potential to provide a viable solution. This study synthesizes insights from 145 publications across the UAV, traditional aviation, and manufacturing domains, retrieved from major scientific databases such as Scopus, IEEE Xplore, and ScienceDirect. The aim is to comprehensively analyze advancements in cyber–physical systems (CPS) and DT technologies, with a particular focus on the key aspects of UAV-based DT construction, including framework architecture, geometric modeling, physical modeling, behavioral modeling, rule modeling, and cyber–physical consistent modeling approaches. Additionally, the application of DTs in AAM scenarios is analyzed, and key challenges are identified. Finally, we provide insights into research directions to enhance the robustness and applicability of future AAM-based DT. Full article

(This article belongs to the Section Innovative Urban Mobility)

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19 pages, 2680 KiB

Open AccessArticle

A Tunnel Crack Detection Method Based on an Unmanned Aerial Vehicle (UAV) Equipped with a High-Speed Camera and Crack Recognition Algorithm Using Improved Multi-Scale Retinex and Prewitt–Otsu

by Wei Sun, Xiaohu Liu and Zhiyong Lei

Drones 2025, 9(6), 393; https://doi.org/10.3390/drones9060393 - 24 May 2025

Abstract

In order to solve the problems of low efficiency and accuracy in the traditional detection of tunnel cracks, this paper proposes a tunnel crack detection method based on a UAV (unmanned aerial vehicle) equipped with a high-speed camera and a crack recognition algorithm [...] Read more.

In order to solve the problems of low efficiency and accuracy in the traditional detection of tunnel cracks, this paper proposes a tunnel crack detection method based on a UAV (unmanned aerial vehicle) equipped with a high-speed camera and a crack recognition algorithm using the improved multi-scale Retinex (MSR) algorithm and the Prewitt–Otsu algorithm, aiming to improve the accuracy and efficiency of detection. The tunnel crack detection method, based on a UAV equipped with a high-speed camera to acquire tunnel surface images, significantly improves the detection efficiency. The recognition method employs an improved multi-scale Retinex algorithm to process the acquired images, enhancing the details of the crack images and improving the contrast between cracks and the background. The enhanced images are input to the Prewitt–Otsu algorithm, which segments the crack image by combining Prewitt edge detection and Otsu thresholding. Finally, the pseudo-crack and isolated edges are removed by the minimum bounding rectangle principle. Using the UAV-collected tunnel surface images as targets, the tunnel crack recognition algorithm proposed in this paper is compared with other existing methods. The experimental results show that the method proposed in this paper improves the recognition ability of the small-texture features of the tunnel’s surface, and the overall crack recognition accuracy is higher than the existing methods. The proposed method not only enhances the efficiency of tunnel crack detection but also significantly improves the recognition accuracy, demonstrating substantial practical significance for tunnel maintenance and safety management. Full article

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57 pages, 24925 KiB

Open AccessReview

AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models

by Zheng Yang, Yuting Zhang, Jie Zeng, Yifan Yang, Yufei Jia, Hua Song, Tiejun Lv, Qian Sun and Jianping An

Drones 2025, 9(6), 392; https://doi.org/10.3390/drones9060392 - 23 May 2025

Abstract

As unmanned aerial vehicle (UAV) applications expand across logistics, agriculture, and emergency response, safety and security threats are becoming increasingly complex. Addressing these evolving threats, including physical safety and network security threats, requires continued advancement by integrating traditional artificial intelligence (AI) tools such [...] Read more.

As unmanned aerial vehicle (UAV) applications expand across logistics, agriculture, and emergency response, safety and security threats are becoming increasingly complex. Addressing these evolving threats, including physical safety and network security threats, requires continued advancement by integrating traditional artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL), which contribute to significantly enhancing UAV safety and security. Large language models (LLMs), a cutting-edge trend in the AI field, are associated with strong capabilities for learning and adapting across various environments. Their emergence reflects a broader trend toward intelligent systems that may eventually demonstrate behavior comparable to human-level reasoning. This paper summarizes the typical safety and security threats affecting UAVs, reviews the progress of traditional AI technologies, as described in the literature, and identifies strategies for reducing the impact of such threats. It also highlights the limitations of traditional AI technologies and summarizes the current application status of LLMs in UAV safety and security. Finally, this paper discusses the challenges and future research directions for improving UAV safety and security with LLMs. By leveraging their advanced capabilities, LLMs offer potential benefits in critical domains such as urban air traffic management, precision agriculture, and emergency response, fostering transformative progress toward adaptive, reliable, and secure UAV systems that address modern operational complexities. Full article

(This article belongs to the Special Issue AI for Cybersecurity in Unmanned Aerial Systems (UAS))

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27 pages, 3535 KiB

Open AccessArticle

Composite Learning-Based Inverse Optimal Fault-Tolerant Control for Hierarchy-Structured Unmanned Helicopters

by Qingyi Liu, Ke Zhang, Bin Jiang and Yushun Tan

Drones 2025, 9(6), 391; https://doi.org/10.3390/drones9060391 - 23 May 2025

Abstract

This article investigates the inverse optimal fault-tolerant formation-containment control problem for a group of unmanned helicopters, where the leaders form a desired formation pattern under the guidance of a virtual leader while the followers move toward the convex hull established by leaders. To [...] Read more.

This article investigates the inverse optimal fault-tolerant formation-containment control problem for a group of unmanned helicopters, where the leaders form a desired formation pattern under the guidance of a virtual leader while the followers move toward the convex hull established by leaders. To facilitate control design and stability analysis, each helicopter’s dynamics are separated into an outer-loop (position) and an inner-loop (attitude) subsystem by exploiting their multi-time-scale characteristics. Next, the serial-parallel estimation model, designed to account for prediction error, is developed. On this foundation, the composite updating law for network weights is derived. Using these intelligent approximations, a fault estimation observer is constructed. The estimated fault information is further incorporated into the inverse optimal fault-tolerant control framework that avoids tackling either the Hamilton–Jacobi–Bellman or Hamilton–Jacobi–Issacs equation. Finally, simulation results are presented to demonstrate the superior control performance and accuracy of the proposed method. Full article

(This article belongs to the Special Issue Advanced Intelligent Decision-Making and Flight Control of Unmanned Aerial Vehicles 2nd Edition)

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33 pages, 7523 KiB

Open AccessArticle

A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field

by Bo Ma, Yi Ji and Liyong Fang

Drones 2025, 9(6), 390; https://doi.org/10.3390/drones9060390 - 22 May 2025

Abstract

The traditional artificial potential field (APF) method exhibits limitations in its force distribution: excessive attraction when UAVs are far from the target may cause collisions with obstacles, while insufficient attraction near the goal often results in failure to reach the target. Furthermore, the [...] Read more.

The traditional artificial potential field (APF) method exhibits limitations in its force distribution: excessive attraction when UAVs are far from the target may cause collisions with obstacles, while insufficient attraction near the goal often results in failure to reach the target. Furthermore, the APF is highly susceptible to local minima, compromising the motion reliability in complex environments. To address these challenges, this paper presents a novel hybrid obstacle avoidance algorithm—deflected simulated annealing–adaptive artificial potential field (DSA-AAPF)—which combines an improved simulated annealing mechanism with an enhanced APF model. The proposed approach integrates a leader–follower distributed formation strategy with the APF framework, where the resultant force formulation is redefined to smooth the UAV trajectories. An adaptive attractive gain function is introduced to dynamically adjust the UAV velocity based on the environmental context, and a fast-converging controller ensures accurate and efficient convergence to the target. Moreover, a directional deflection mechanism is embedded within the simulated annealing process, enabling UAVs to escape the local minima caused by semi-enclosed obstacles through continuous rotational motion. The simulation results, covering the formation reconfiguration, complex obstacle avoidance, and entrapment escape, demonstrate the feasibility, robustness, and superiority of the proposed DSA-AAPF algorithm. Full article

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15 pages, 2232 KiB

Open AccessArticle

Deep Learning-Based Acoustic Recognition of UAVs in Complex Environments

by Zhongru Liu, Kuangang Fan, Yuhang Chen, Lizhi Xiong, Jingzhen Ye, Aigen Fan and Hengheng Zhang

Drones 2025, 9(6), 389; https://doi.org/10.3390/drones9060389 - 22 May 2025

Abstract

In recent years, UAV technology has developed rapidly and has been widely applied across various fields. However, as the adoption of civilian UAVs continues to grow, there has been a corresponding rise in the number of black flights by UAVs, which may cause [...] Read more.

In recent years, UAV technology has developed rapidly and has been widely applied across various fields. However, as the adoption of civilian UAVs continues to grow, there has been a corresponding rise in the number of black flights by UAVs, which may cause criminal activities and privacy and security issues, so it has become necessary to recognize UAVs in the airspace in order to deal with potential threats. This study recognizes UAVs based on the acoustic signals of UAV flights. Since there are various acoustic interferences in the real environment, more efficient acoustic recognition techniques are needed to meet the recognition needs in complex environments. Aiming at the recognition difficulties caused by the overlap of UAV sound and the background noise spectrum in low signal-to-noise ratio environments, this study proposes an improved lightweight ResNet10_CBAM deep learning model. The optimal performance of MFCC in low SNR environments is verified by comparing three feature extraction methods, Spectrogram, Fbank, and MFCC. The enhanced ResNet10_CBAM model, with fewer layers and integrated channel and spatial attention mechanisms, significantly improved feature extraction in low SNR conditions while reducing model parameters. The experimental results show that the model improves the average accuracy by 14.52%, 17.53%, and 20.71% compared with ResNet18 under the low SNR conditions of −20 dB, −25 dB, and −30 dB, respectively, and the F1 score reaches 94.30%. The study verifies the effectiveness of lightweight design and attention mechanisms in complex acoustic environments. Full article

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19 pages, 2850 KiB

Open AccessArticle

Industry (UAPASTF) Response to Pesticide Regulators’ “State of the Knowledge” Review of Drone Use for Pesticide Application: Best Practices for Safe and Effective Application of Pesticides

by Hector Portillo, Roberto Barbosa, Matt Beckwith, Tyler Gullen, Rebecca Haynie, Sarah Hovinga, Banugopan Kesavaraju, Edward Lang, Pamela Livingston, Neill Newton, Mark Oostlander, Greg Watson and Rajeev Sinha

Drones 2025, 9(6), 388; https://doi.org/10.3390/drones9060388 - 22 May 2025

Abstract

The Organization of Economic Cooperation and Development Working Party on Pesticides (OECD WPP) Drone/UAV Subgroup published a “state of the knowledge” report on pesticide application using unmanned aerial vehicles (UAV) in 2021. One of the recommendations made in this report was to “develop [...] Read more.

The Organization of Economic Cooperation and Development Working Party on Pesticides (OECD WPP) Drone/UAV Subgroup published a “state of the knowledge” report on pesticide application using unmanned aerial vehicles (UAV) in 2021. One of the recommendations made in this report was to “develop and publish a user-friendly summary of best practices (including the essential nature of calibration), pitfalls and a trouble shooting guide (both for generating trials data and applying pesticides in practice)”. In response to recommendations in that report, the pesticide registrant industry in the United States formed the global Unmanned Aerial Pesticide Application System Task Force (UAPASTF). This report outlines the overview of the “Best Management Practices” (BMP) guidance that was developed by the UAPASTF. UAV-based spraying of crop protection products is relatively new for most of the regions globally. Therefore, this guidance document is intended to serve as an excellent resource for growers, researchers (both academics and industry) and other relevant stakeholders to carry out UAV-based spray application in an efficient and safe manner. Full article

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21 pages, 5420 KiB

Open AccessArticle

Effective Strategies for Mitigating the “Bowl” Effect and Optimising Accuracy: A Case Study of UAV Photogrammetry in Corridor Projects

by Sara Ait-Lamallam, Rim Lamrani, Wijdane Mastari and Mehdi Kechna

Drones 2025, 9(6), 387; https://doi.org/10.3390/drones9060387 - 22 May 2025

Abstract

UAV-Enabled Corridor Photogrammetry is applied to survey linear transport infrastructure projects’ sites. The corridor flight missions cause a misalignment of the point cloud called the “bowl” effect. The purpose of this study is to offer a methodology based on statistical compensation methods to [...] Read more.

UAV-Enabled Corridor Photogrammetry is applied to survey linear transport infrastructure projects’ sites. The corridor flight missions cause a misalignment of the point cloud called the “bowl” effect. The purpose of this study is to offer a methodology based on statistical compensation methods to mitigate this effect and to improve the accuracy and density of the generated point cloud. The aerial images’ post-processing was carried out by varying the aerotriangulation methods. Subsequently, the accuracy improvement was completed by integrating the coordinates of the ground control points (GCPs) through different spatial distributions. Finally, Mean and RANSAC compensations were proposed to address the errors induced by the “bowl” effect on the coordinates of the images’ perspective centres (PCs). The findings indicate that the optimised aerotriangulation using Post-Processed Kinematic (PPK) data significantly contribute to reducing the “bowl” effect. Moreover, the GCP pyramidal spatial distribution allows accuracy improvement to a centimetre level. The Mean compensation method yields optimal outcomes in accuracy. It also helps to optimise on-site survey time and computing resources. RANSAC compensation optimises the accuracy and allows the retrieval of a 5-times-denser point cloud. Furthermore, the results give better accuracy compared to some current approaches. Full article

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20 pages, 5649 KiB

Open AccessArticle

Edge-Deployed Band-Split Rotary Position Encoding Transformer for Ultra-Low-Signal-to-Noise-Ratio Unmanned Aerial Vehicle Speech Enhancement

by Feifan Liu, Muying Li, Luming Guo, Hao Guo, Jie Cao, Wei Zhao and Jun Wang

Drones 2025, 9(6), 386; https://doi.org/10.3390/drones9060386 - 22 May 2025

Abstract

Addressing the significant challenge of speech enhancement in ultra-low-Signal-to-Noise-Ratio (SNR) scenarios for Unmanned Aerial Vehicle (UAV) voice communication, particularly under edge deployment constraints, this study proposes the Edge-Deployed Band-Split Rotary Position Encoding Transformer (Edge-BS-RoFormer), a novel, lightweight band-split rotary position encoding transformer. While [...] Read more.

Addressing the significant challenge of speech enhancement in ultra-low-Signal-to-Noise-Ratio (SNR) scenarios for Unmanned Aerial Vehicle (UAV) voice communication, particularly under edge deployment constraints, this study proposes the Edge-Deployed Band-Split Rotary Position Encoding Transformer (Edge-BS-RoFormer), a novel, lightweight band-split rotary position encoding transformer. While existing deep learning methods face limitations in dynamic UAV noise suppression under such constraints, including insufficient harmonic modeling and high computational complexity, the proposed Edge-BS-RoFormer distinctively synergizes a band-split strategy for fine-grained spectral processing, a dual-dimension Rotary Position Encoding (

RoPE

) mechanism for superior joint time–frequency modeling, and

FlashAttention

to optimize computational efficiency, pivotal for its lightweight nature and robust ultra-low-SNR performance. Experiments on our self-constructed DroneNoise-LibriMix (DN-LM) dataset demonstrate Edge-BS-RoFormer’s superiority. Under a −15 dB SNR, it achieves Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) improvements of 2.2 dB over Deep Complex U-Net (DCUNet), 25.0 dB over the Dual-Path Transformer Network (DPTNet), and 2.3 dB over HTDemucs. Correspondingly, the Perceptual Evaluation of Speech Quality (PESQ) is enhanced by 0.11, 0.18, and 0.15, respectively. Crucially, its efficacy for edge deployment is substantiated by a minimal model storage of 8.534 MB, 11.617 GFLOPs (an 89.6% reduction vs. DCUNet), a runtime memory footprint of under 500MB, a Real-Time Factor (RTF) of 0.325 (latency: 330.830 ms), and a power consumption of 6.536 W on an NVIDIA Jetson AGX Xavier, fulfilling real-time processing demands. This study delivers a validated lightweight solution, exemplified by its minimal computational overhead and real-time edge inference capability, for effective speech enhancement in complex UAV acoustic scenarios, including dynamic noise conditions. Furthermore, the open-sourced dataset and model contribute to advancing research and establishing standardized evaluation frameworks in this domain. Full article

(This article belongs to the Section Drone Communications)

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