Drones

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

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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18 pages, 2484 KiB

Open AccessArticle

Empowering Smallholder Farmers with UAV-Based Early Cotton Disease Detection Using AI

by Halimjon Khujamatov, Shakhnoza Muksimova, Mirjamol Abdullaev, Jinsoo Cho, Cheolwon Lee and Heung-Seok Jeon

Drones 2025, 9(5), 385; https://doi.org/10.3390/drones9050385 - 21 May 2025

Abstract

Early detection of cotton diseases is critical for safeguarding crop yield and minimizing agrochemical usage. However, most state-of-the-art systems rely on multispectral or hyperspectral sensors, which are costly and inaccessible to smallholder farmers. This paper introduces CottoNet, a lightweight and efficient deep learning [...] Read more.

Early detection of cotton diseases is critical for safeguarding crop yield and minimizing agrochemical usage. However, most state-of-the-art systems rely on multispectral or hyperspectral sensors, which are costly and inaccessible to smallholder farmers. This paper introduces CottoNet, a lightweight and efficient deep learning framework for detecting early-stage cotton diseases using only RGB images captured by unmanned aerial vehicles (UAVs). The proposed model integrates an EfficientNetV2-S backbone with a Dual-Attention Feature Pyramid Network (DA-FPN) and a novel Early Symptom Emphasis Module (ESEM) to enhance sensitivity to subtle visual cues such as chlorosis, minor lesions, and texture irregularities. A custom-labeled dataset was collected from cotton fields in Uzbekistan to evaluate the model under realistic agricultural conditions. CottoNet achieved a mean average precision (mAP@50) of 89.7%, an F1 score of 88.2%, and an early detection accuracy (EDA) of 91.5%, outperforming existing lightweight models while maintaining real-time inference speed on embedded devices. The results demonstrate that CottoNet offers a scalable, accurate, and field-ready solution for precision agriculture in resource-limited settings. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture—2nd Edition)

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26 pages, 4132 KiB

Open AccessArticle

Hierarchical Reinforcement Learning with Automatic Curriculum Generation for Unmanned Combat Aerial Vehicle Tactical Decision-Making in Autonomous Air Combat

by Yang Li, Wenhan Dong, Pin Zhang, Hengang Zhai and Guangqi Li

Drones 2025, 9(5), 384; https://doi.org/10.3390/drones9050384 - 21 May 2025

Abstract

This study proposes an unmanned combat aerial vehicle (UCAV)-oriented hierarchical reinforcement learning framework to address the temporal abstraction challenge in autonomous within-visual-range air combat (WVRAC) for UCAVs. The incorporation of maximum-entropy objectives within the MEOL framework facilitates the optimization of both autonomous low-level [...] Read more.

This study proposes an unmanned combat aerial vehicle (UCAV)-oriented hierarchical reinforcement learning framework to address the temporal abstraction challenge in autonomous within-visual-range air combat (WVRAC) for UCAVs. The incorporation of maximum-entropy objectives within the MEOL framework facilitates the optimization of both autonomous low-level tactical discovery and high-level option selection. At the low level, three tactical policies (angle, snapshot, and energy tactics) are designed with reward functions informed by expert knowledge, while the high-level policy dynamically terminates current tactics and selects new ones through sparse reward learning, thus overcoming the limitations of fixed-duration tactical execution. Furthermore, a novel automatic curriculum generation mechanism based on Wasserstein Generative Adversarial Networks (WGANs) is introduced to enhance training efficiency and adaptability to diverse initial combat conditions. Extensive experiments conducted in UCAV air combat simulations have shown that MEOL not only achieves significantly better win rates than other policies when training against rule-based opponents, but also that MEOC achieves superior results in tests against tactical intra-option policies as well as other option learning policies. The framework facilitates dynamic termination and switching of tactics, thereby addressing the limitations of fixed-duration hierarchical methods. Ablation studies confirm the effectiveness of WGAN-based curricula in accelerating policy convergence. Additionally, the visual analysis of UCAVs’ flight logs validates the learned hierarchical decision-making process, showcasing the interplay between tactical selection and manoeuvring execution. This research provides novel methodologies combining hierarchical reinforcement learning with tactical domain knowledge for the autonomous decision-making of UCAVs in complex air combat scenarios. Full article

(This article belongs to the Collection Drones for Security and Defense Applications)

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36 pages, 5184 KiB

Open AccessReview

Aerial Drones for Geophysical Prospection in Mining: A Review

by Dimitris Perikleous, Katerina Margariti, Pantelis Velanas, Cristina Saez Blazquez and Diego Gonzalez-Aguilera

Drones 2025, 9(5), 383; https://doi.org/10.3390/drones9050383 - 21 May 2025

Abstract

This review explores the evolution and current state of aerial drones’ use in geophysical mining applications. Aerial drones have transformed many fields by offering high-resolution and cost-effective data acquisition. In geophysics, drones equipped with advanced sensors such as magnetometers, ground-penetrating radar, electromagnetic induction, [...] Read more.

This review explores the evolution and current state of aerial drones’ use in geophysical mining applications. Aerial drones have transformed many fields by offering high-resolution and cost-effective data acquisition. In geophysics, drones equipped with advanced sensors such as magnetometers, ground-penetrating radar, electromagnetic induction, and gamma-ray spectrometry have enabled more precise and rapid subsurface investigations, reducing operational costs and improving safety in mining exploration and monitoring. Over the last decade, advances in drone navigation, sensor integration, and data processing have improved the accuracy and applicability of geophysical surveys in mining. This review provides a historical overview and examines the latest developments in aerial drones, sensing technologies, data acquisition strategies, and processing methodologies. It analyses 59 studies spanning 66 drone-based geophysical applications and 63 geophysical method entries, published between 2005 and 2025. Multirotor drones are the most common, used in 72.73% of cases, followed by fixed-wing drones (12.12%), unmanned helicopters (9.09%), hybrid VTOL designs (3.03%), airships (1.52%), and one unspecified platform (1.52%). In terms of geophysical methods, magnetometry was the most frequently used technique, applied in thirty-nine studies, followed by gamma-ray spectrometry (eighteen studies), electromagnetic surveys (five studies), and ground-penetrating radar (one study). The findings show how drone-based geophysical techniques enhance resource exploration, safety, and sustainability in mining. Full article

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19 pages, 4634 KiB

Open AccessArticle

Adaptive Missile Avoidance Algorithm for UAV Based on Multi-Head Attention Mechanism and Dual Population Confrontation Game

by Cheng Zhang, Junhao Song, Chengyang Tao, Zitao Su, Zhiqiang Xu, Weijia Feng, Zhaoxiang Zhang and Yuelei Xu

Drones 2025, 9(5), 382; https://doi.org/10.3390/drones9050382 - 21 May 2025

Abstract

In recent years, UAVs have faced increasingly severe and diversified missile threats. To address the challenge that reinforcement learning-based missile evasion algorithms struggle to adapt to various unknown missile types, we introduce a risk-sensitive PPO algorithm and propose a training framework incorporating multi-head [...] Read more.

In recent years, UAVs have faced increasingly severe and diversified missile threats. To address the challenge that reinforcement learning-based missile evasion algorithms struggle to adapt to various unknown missile types, we introduce a risk-sensitive PPO algorithm and propose a training framework incorporating multi-head attention mechanisms and dual-population adversarial training. The multi-head attention mechanism enables the policy network to extract latent features such as missile guidance laws from state sequences, while the dual-population adversarial approach ensures policy diversity and robustness. Compared to conventional self-play methods and GRU-based evasion strategies, our method demonstrates superior training efficiency and generates evasion policies with better adaptability to different missile types. Full article

(This article belongs to the Collection Drones for Security and Defense Applications)

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19 pages, 3448 KiB

Open AccessArticle

Method for Multi-Target Wireless Charging for Oil Field Inspection Drones

by Yilong Wang, Li Ji and Ming Zhang

Drones 2025, 9(5), 381; https://doi.org/10.3390/drones9050381 - 20 May 2025

Abstract

Wireless power transfer (WPT) systems are critical for enabling safe and efficient charging of inspection drones in flammable oilfield environments, yet existing solutions struggle with multi-target compatibility and reactive power losses. This study proposes a novel frequency-regulated LCC-S topology that achieves both constant [...] Read more.

Wireless power transfer (WPT) systems are critical for enabling safe and efficient charging of inspection drones in flammable oilfield environments, yet existing solutions struggle with multi-target compatibility and reactive power losses. This study proposes a novel frequency-regulated LCC-S topology that achieves both constant current (CC) and constant voltage (CV) charging modes for heterogeneous drones using a single hardware configuration. By dynamically adjusting the operating frequency, the system minimizes the input impedance angle (θ < 10°) while maintaining load-independent CC and CV outputs, thereby reducing reactive power by 92% and ensuring spark-free operation in explosive atmospheres. Experimental validation with two distinct oilfield inspection drones demonstrates seamless mode transitions, zero-phase-angle (ZPA) resonance, and peak efficiencies of 92.57% and 91.12%, respectively. The universal design eliminates the need for complex alignment mechanisms, offering a scalable solution for multi-drone fleets in energy, agriculture, and disaster response applications. Full article

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16 pages, 1807 KiB

Open AccessArticle

Collision Detection and Recovery Control of Drones Using Onboard Inertial Measurement Unit

by Xisheng Huang, Guangjun Liu and Yugang Liu

Drones 2025, 9(5), 380; https://doi.org/10.3390/drones9050380 - 18 May 2025

Abstract

This paper presents a strategy for collision detection and recovery control of drones using an onboard Inertial Measurement Unit (IMU). The collision detection algorithm compares the expected response of the drone with the measurements from the IMU to identify and characterize collisions. The [...] Read more.

This paper presents a strategy for collision detection and recovery control of drones using an onboard Inertial Measurement Unit (IMU). The collision detection algorithm compares the expected response of the drone with the measurements from the IMU to identify and characterize collisions. The recovery controller implements a gain scheduling approach, adjusting its parameters based on the characteristics of the collision and the drone’s attitude. Simulations were conducted to compare the proposed collision detection strategy with a popular detection method with fixed thresholds, and the simulation results showed that the proposed approach outperformed the existing method in terms of detection accuracy. Furthermore, the proposed collision detection and recovery control approaches were tested with physical experiments using a custom-built drone. The experimental results confirmed that the proposed collision detection algorithm was able to distinguish between actual collisions and aggressive flight maneuvers, and the recovery controller can recover the drone within 0.8 s. Full article

(This article belongs to the Special Issue Flight Control and Collision Avoidance of UAVs)

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24 pages, 9161 KiB

Open AccessArticle

An Efficient Pyramid Transformer Network for Cross-View Geo-Localization in Complex Terrains

by Chengjie Ju, Wangping Xu, Nanxing Chen and Enhui Zheng

Drones 2025, 9(5), 379; https://doi.org/10.3390/drones9050379 - 17 May 2025

Abstract

Unmanned aerial vehicle (UAV) self-localization in complex environments is critical when global navigation satellite systems (GNSSs) are unreliable. Existing datasets, often limited to low-altitude urban scenes, hinder generalization. This study introduces Multi-UAV, a novel dataset with 17.4 k high-resolution UAV–satellite image pairs from [...] Read more.

Unmanned aerial vehicle (UAV) self-localization in complex environments is critical when global navigation satellite systems (GNSSs) are unreliable. Existing datasets, often limited to low-altitude urban scenes, hinder generalization. This study introduces Multi-UAV, a novel dataset with 17.4 k high-resolution UAV–satellite image pairs from diverse terrains (urban, rural, mountainous, farmland, coastal) and altitudes across China, enhancing cross-view geolocalization research. We propose a lightweight value reduction pyramid transformer (VRPT) for efficient feature extraction and a residual feature pyramid network (RFPN) for multi-scale feature fusion. Using meter-level accuracy (MA@K) and relative distance score (RDS), VRPT achieves robust, high-precision localization across varied terrains, offering significant potential for resource-constrained UAV deployment. Full article

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15 pages, 1828 KiB

Open AccessArticle

Neural Network-Based Path Planning for Fixed-Wing UAVs with Constraints on Terminal Roll Angle

by Qian Xu, Fanchen Wu and Zheng Chen

Drones 2025, 9(5), 378; https://doi.org/10.3390/drones9050378 - 17 May 2025

Abstract

This paper presents a neural network-based path planning method for fixed-wing UAVs under terminal roll-angle constraints. The nonlinear optimal path planning problem is first formulated as an optimal control problem. The necessary conditions derived from Pontryagin’s Maximum Principle are then established to convert [...] Read more.

This paper presents a neural network-based path planning method for fixed-wing UAVs under terminal roll-angle constraints. The nonlinear optimal path planning problem is first formulated as an optimal control problem. The necessary conditions derived from Pontryagin’s Maximum Principle are then established to convert extremal trajectories as the solutions of a parameterized system. Additionally, a sufficient condition is presented to guarantee that the obtained solution is at least locally optimal. By simply propagating the parameterized system, a training dataset comprising at least locally optimal trajectories can be constructed. A neural network is then trained to generate the nonlinear optimal control command in real time. Finally, numerical examples demonstrate that the proposed method robustly ensures the generation of optimal trajectories in real time while satisfying the prescribed terminal roll-angle constraint. Full article

(This article belongs to the Special Issue AI-Assisted Control Strategies and Their Applications to the Stabilization, Guidance and Navigation of Drones)

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20 pages, 1057 KiB

Open AccessArticle

Heterogeneous Multi-Agent Deep Reinforcement Learning for Cluster-Based Spectrum Sharing in UAV Swarms

by Xiaomin Liao, Yulai Wang, Yang Han, You Li, Chushan Lin and Xuan Zhu

Drones 2025, 9(5), 377; https://doi.org/10.3390/drones9050377 - 17 May 2025

Abstract

Unmanned aerial vehicle (UAV) swarms are widely applied in various fields, including military and civilian domains. However, due to the scarcity of spectrum resources, UAV swarm clustering technology has emerged as an effective method for achieving spectrum sharing among UAV swarms. This paper [...] Read more.

Unmanned aerial vehicle (UAV) swarms are widely applied in various fields, including military and civilian domains. However, due to the scarcity of spectrum resources, UAV swarm clustering technology has emerged as an effective method for achieving spectrum sharing among UAV swarms. This paper introduces a distributed heterogeneous multi-agent deep reinforcement learning algorithm, named HMDRL-UC, which is specifically designed to address the cluster-based spectrum sharing problem in heterogeneous UAV swarms. Heterogeneous UAV swarms consist of two types of UAVs: cluster head (CH) and cluster member (CM). Each UAV is equipped with an intelligent agent to execute the deep reinforcement learning (DRL) algorithm. Correspondingly, the HMDRL-UC consists of two parts: multi-agent proximal policy optimization for cluster head (MAPPO-H) and independent proximal policy optimization for cluster member (IPPO-M). The MAPPO-H enables the CHs to decide cluster selection and moving position, while CMs utilize IPPO-M to cluster autonomously under the condition of certain partial channel distribution information (CDI). Adequate experimental evidence has confirmed that the HMDRL-UC algorithm proposed in this paper is not only capable of managing dynamic drone swarm scenarios in the presence of partial CDI, but also has a clear advantage over the other existing three algorithms in terms of average throughput, intra-cluster communication delay, and minimum signal-to-noise ratio (SNR). Full article

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46 pages, 9673 KiB

Open AccessReview

Advances in UAV Path Planning: A Comprehensive Review of Methods, Challenges, and Future Directions

by Wenlong Meng, Xuegang Zhang, Lvzhuoyu Zhou, Hangyu Guo and Xin Hu

Drones 2025, 9(5), 376; https://doi.org/10.3390/drones9050376 - 16 May 2025

Abstract

Unmanned aerial vehicles (UAVs) have revolutionized fields such as monitoring, cargo delivery, precision farming, and emergency response, demonstrating remarkable flexibility and operational effectiveness. A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, [...] Read more.

Unmanned aerial vehicles (UAVs) have revolutionized fields such as monitoring, cargo delivery, precision farming, and emergency response, demonstrating remarkable flexibility and operational effectiveness. A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, safety, and power consumption. This article presents an extensive overview of methodologies for UAV route planning, including deterministic models, stochastic sampling techniques, biologically inspired methods, and integrated algorithmic frameworks. The discussion extends to their performance in various operational contexts, including stationary, moving, and three-dimensional settings. Innovative methods utilizing artificial intelligence, particularly machine learning and neural networks, are emphasized for their promise in facilitating adaptive responses to intricate, evolving environments. Furthermore, strategies focused on reducing energy usage and enabling coordinated operations among multiple drones are analyzed, addressing issues such as prolonged operation, distribution of assignments, and navigation around obstacles. Although notable advancements have been achieved, challenges like high computational demands and the need for immediate responsiveness persist. By consolidating the latest progress, this survey provides meaningful perspectives and guidance for the ongoing evolution of UAV route planning solutions. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition)

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24 pages, 11354 KiB

Open AccessArticle

A Mean-Field-Game-Integrated MPC-QP Framework for Collision-Free Multi-Vehicle Control

by Liancheng Zheng, Xuemei Wang, Feng Li, Zebing Mao, Zhen Tian, Yanhong Peng, Fujiang Yuan and Chunhong Yuan

Drones 2025, 9(5), 375; https://doi.org/10.3390/drones9050375 - 15 May 2025

Abstract

In recent years, rapid progress in autonomous driving has been achieved through advances in sensing, control, and earning. However, as the complexity of traffic scenarios increases, ensuring safe interaction among vehicles remains a formidable challenge. Recent works combining artificial potential fields (APFs) with [...] Read more.

In recent years, rapid progress in autonomous driving has been achieved through advances in sensing, control, and earning. However, as the complexity of traffic scenarios increases, ensuring safe interaction among vehicles remains a formidable challenge. Recent works combining artificial potential fields (APFs) with game-theoretic methods have shown promise in modeling vehicle interactions and avoiding collisions. However, these approaches often suffer from overly conservative decisions or fail to capture the nonlinear dynamics of real-world driving. To address these imitations, we propose a novel framework that integrates mean field game (MFG) theory with model predictive control (MPC) and quadratic programming (QP). Our approach everages the aggregate behavior of surrounding vehicles to predict interactive effects and embeds these predictions into an MPC-QP scheme for real-time control. Simulation results in complex driving scenarios demonstrate that our method achieves multiple autonomous driving tasks while ensuring collision-free operation. Furthermore, the proposed framework outperforms popular game-based benchmarks in terms of achieving driving tasks and producing fewer collisions. Full article

(This article belongs to the Section Innovative Urban Mobility)

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18 pages, 13241 KiB

Open AccessArticle

Experimental Investigation of Aerodynamic Interaction in Non-Parallel Tandem Dual-Rotor Systems for Tiltrotor UAV

by He Zhu, Yuhao Du, Hong Nie, Zhiyang Xin and Xi Geng

Drones 2025, 9(5), 374; https://doi.org/10.3390/drones9050374 - 15 May 2025

Abstract

The distributed electric tilt-rotor Unmanned Aerial Vehicle (UAV) combines the vertical take-off and landing (VTOL) capability of helicopters with the high-speed cruise performance of fixed-wing aircraft, offering a transformative solution for Urban Air Mobility (UAM). However, aerodynamic interference between rotors is a new [...] Read more.

The distributed electric tilt-rotor Unmanned Aerial Vehicle (UAV) combines the vertical take-off and landing (VTOL) capability of helicopters with the high-speed cruise performance of fixed-wing aircraft, offering a transformative solution for Urban Air Mobility (UAM). However, aerodynamic interference between rotors is a new challenge to improving their flight efficiency, especially the dynamic interactions during the transition phase of non-parallel tandem dual-rotor systems, which require in-depth investigation. This study focuses on the aerodynamic performance evolution of the tilt-rotor system during asynchronous transition processes, with an emphasis on quantifying the influence of rotor tilt angles. A customized experimental platform was developed to investigate a counter-rotating dual-rotor model with fixed axial separation. Key performance metrics, including thrust, torque, and power, were systematically measured at various tilt angles (0–90°) and rotational speeds (1500–3500 RPM). The aerodynamic coupling mechanisms between the front and rear rotor disks were analyzed. The experimental results indicate that the relative tilt angle of the dual rotors significantly affects aerodynamic interference between the rotors. In the forward tilt mode, the thrust of the aft rotor recovers when the tilt angle reaches 45°, while in the aft tilt mode, it requires a tilt angle of 75°. By optimizing the tilt configuration, the aerodynamic performance loss of the aft rotor due to rotor-to-rotor aerodynamic interference can be effectively mitigated. This study provides important insights for the aerodynamic performance optimization and transition control strategies of the distributed electric tilt-rotor UAV. Full article

(This article belongs to the Special Issue Dynamics Modeling and Conceptual Design of UAVs)

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22 pages, 20558 KiB

Open AccessArticle

Long-Duration UAV Localization Across Day and Night by Fusing Dual-Vision Geo-Registration with Inertial Measurements

by Xuehui Xing, Xiaofeng He, Ke Liu, Zhizhong Chen, Guofeng Song, Qikai Hao, Lilian Zhang and Jun Mao

Drones 2025, 9(5), 373; https://doi.org/10.3390/drones9050373 - 15 May 2025

Abstract

Remote sensing visual-light spectral (VIS) maps provide stable and rich features for geo-localization. However, it still remains a challenge to make use of VIS map features as localization references at night. To construct a cross-day-and-night localization system for long-duration UAVs, this study proposes [...] Read more.

Remote sensing visual-light spectral (VIS) maps provide stable and rich features for geo-localization. However, it still remains a challenge to make use of VIS map features as localization references at night. To construct a cross-day-and-night localization system for long-duration UAVs, this study proposes a visual–inertial integrated localization system, where the visual component can register both RGB and infrared camera images in one unified VIS map. To deal with the large differences between visible and thermal images, we inspected various visual features and utilized a pre-trained network for cross-domain feature extraction and matching. To obtain an accurate position from visual geo-localization, we demonstrate a localization error compensation algorithm with considerations about the camera attitude, flight height, and terrain height. Finally, the inertial and dual-vision information is fused with a State Transformation Extended Kalman Filter (ST-EKF) to generate long-term, drift-free localization performance. Finally, we conducted actual long-duration flight experiments with altitudes ranging from 700 to 2400 m and flight distances longer than 344.6 km. The experimental results demonstrate that the proposed method’s localization error is less than 50 m in its RMSE. Full article

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