Drones

22 pages, 466 KiB

Open AccessArticle

DEGNN: A Deep Learning-Based Method for Unmanned Aerial Vehicle Software Security Analysis

by Jiang Du, Qiang Wei, Yisen Wang and Xingyu Bai

Drones 2025, 9(2), 110; https://doi.org/10.3390/drones9020110 - 2 Feb 2025

With the increasing utilization of drones, the cyber security threats they face have become more prominent. Code reuse in the software development of drone systems has led to vulnerabilities in drones. The binary code similarity analysis method offers a way to analyze drone [...] Read more.

With the increasing utilization of drones, the cyber security threats they face have become more prominent. Code reuse in the software development of drone systems has led to vulnerabilities in drones. The binary code similarity analysis method offers a way to analyze drone firmware lacking source code. This paper proposes DEGNN, a novel graph neural network for binary code similarity analysis. It uses call-enhanced control graphs and attention mechanisms to generate dual embeddings of functions and predict similarity based on graph structures and node features. DEGNN is effective in cross-architecture tasks. Experimental results show that in the cross-architecture binary function search, DEGNN’s mean reciprocal rank and recall@1 surpass the state of the art by 12% and 28.6%, respectively. In the cross-architecture real-world vulnerability search, specifically targeting drone systems, it has a 33.3% performance improvement over the SOTA model, indicating its great potential in enhancing drone cyber security. Full article

(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV: 2nd Edition)

13 pages, 3458 KiB

Open AccessArticle

Smart Glove: A Cost-Effective and Intuitive Interface for Advanced Drone Control

by Cristian Randieri, Andrea Pollina, Adriano Puglisi and Christian Napoli

Drones 2025, 9(2), 109; https://doi.org/10.3390/drones9020109 - 1 Feb 2025

Abstract

Recent years have witnessed the development of human-unmanned aerial vehicle (UAV) interfaces to meet the growing demand for intuitive and efficient solutions in UAV piloting. In this paper, we propose a novel Smart Glove v 1.0 prototype for advanced drone gesture control, leveraging [...] Read more.

Recent years have witnessed the development of human-unmanned aerial vehicle (UAV) interfaces to meet the growing demand for intuitive and efficient solutions in UAV piloting. In this paper, we propose a novel Smart Glove v 1.0 prototype for advanced drone gesture control, leveraging key low-cost components such as Arduino Nano to process data, MPU6050 to detect hand movements, flexible sensors for easy throttle control, and the nRF24L01 module for wireless communication. The proposed research highlights the design methodology of reporting flight tests associated with simulation findings to demonstrate the characteristics of Smart Glove v1.0 in terms of intuitive, responsive, and hands-free piloting gesture interface. We aim to make the drone piloting experience more enjoyable and leverage ergonomics by adapting to the pilot’s preferred position. The overall research project points to a seedbed for future solutions, eventually extending its applications to medicine, space, and the metaverse. Full article

(This article belongs to the Special Issue Wearable Technologies for Driving Drones: Innovations and Applications)

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45 pages, 1062 KiB

Open AccessReview

UAV Communication in Space–Air–Ground Integrated Networks (SAGINs): Technologies, Applications, and Challenges

by Peiying Zhang, Shengpeng Chen, Xiangguo Zheng, Peiyan Li, Guilong Wang, Ruixin Wang, Jian Wang and Lizhuang Tan

Drones 2025, 9(2), 108; https://doi.org/10.3390/drones9020108 - 1 Feb 2025

Abstract

With the continuous advancement of 6G technology, SAGINs provide seamless coverage and efficient connectivity for future communications by integrating terrestrial, aerial, and satellite networks. Unmanned aerial vehicles (UAVs), owing to their high maneuverability and flexibility, have emerged as a critical component of the [...] Read more.

With the continuous advancement of 6G technology, SAGINs provide seamless coverage and efficient connectivity for future communications by integrating terrestrial, aerial, and satellite networks. Unmanned aerial vehicles (UAVs), owing to their high maneuverability and flexibility, have emerged as a critical component of the aerial layer in SAGINs. In this paper, we systematically review the key technologies, applications, and challenges of UAV-assisted SAGINs. First, the hierarchical architecture of SAGINs and their dynamic heterogeneous characteristics are elaborated on, and this is followed by an in-depth discussion of UAV communication. Subsequently, the core technologies of UAV-assisted SAGINs are comprehensively analyzed across five dimensions—routing protocols, security control, path planning, resource management, and UAV deployment—highlighting the progress and limitations of existing research. In terms of applications, UAV-assisted SAGINs demonstrate significant potential in disaster recovery, remote network coverage, smart cities, and agricultural monitoring. However, their practical deployment still faces challenges such as dynamic topology management, cross-layer protocol adaptation, energy-efficiency optimization, and security threats. Finally, we summarize the applications and challenges of UAV-assisted SAGINs and provide prospects for future research directions. Full article

(This article belongs to the Collection Feature Papers Collection of the World’s Top 2% Scientists in Drones)

28 pages, 14573 KiB

Open AccessArticle

Community Drones: A Concept Study on Shared Drone Services

by Peter Widhalm, Ulrike Ritzinger, Natalie Prüggler, Wolfgang Prüggler, Dariia Strelnikova, Gernot Paulus, Francesco d’Apolito and Felix Eicken

Drones 2025, 9(2), 107; https://doi.org/10.3390/drones9020107 - 31 Jan 2025

Abstract

The growing demand for civil drone services across diverse sectors necessitates more efficient and accessible operational models. This paper introduces the Community Drones model, a shared infrastructure framework designed to coordinate regional drone services through automated resource sharing. By consolidating tasks and resources, [...] Read more.

The growing demand for civil drone services across diverse sectors necessitates more efficient and accessible operational models. This paper introduces the Community Drones model, a shared infrastructure framework designed to coordinate regional drone services through automated resource sharing. By consolidating tasks and resources, this approach enhances drone utilization and reduces operational costs and user effort while ensuring high service quality, reliability, and flexibility. At the core of the model is an advanced mission planning system that automates flight scheduling, risk assessment, mission pooling, and resource allocation, taking into account critical factors such as weather conditions, regulatory safety requirements, and task priorities. We propose a system architecture and concept of operations, discuss the regulatory and technological frameworks, and introduce specialized mission planning algorithms tailored to the unique demands of shared drone environments. Simulation-based performance evaluations for a showcase region in Austria demonstrate the technical and economic viability of the proposed model. The results reveal that the Community Drones approach significantly reduces costs, with savings depending on individual and community utilization levels. These findings suggest that drone sharing holds substantial potential for making civil drone services more sustainable and economically attractive. Full article

(This article belongs to the Special Issue Advanced Autonomous Mobility Toward Low-Altitude Economy and Three-Dimensional Transportation Systems)

30 pages, 2257 KiB

Open AccessArticle

Hierarchical Online Air Combat Maneuver Decision Making and Control Based on Surrogate-Assisted Differential Evolution Algorithm

by Mulai Tan, Haocheng Sun, Dali Ding, Huan Zhou, Tong Han and Yuequn Luo

Drones 2025, 9(2), 106; https://doi.org/10.3390/drones9020106 - 31 Jan 2025

Abstract

One-to-one within-visual-range air combat of unmanned combat aerial vehicles (UCAVs) requires fast, continuous, and accurate decision-making to achieve air combat victory. In order to solve the current problems of insufficient real-time performance of traditional intelligent optimization algorithms for solving decision-making problems and the [...] Read more.

One-to-one within-visual-range air combat of unmanned combat aerial vehicles (UCAVs) requires fast, continuous, and accurate decision-making to achieve air combat victory. In order to solve the current problems of insufficient real-time performance of traditional intelligent optimization algorithms for solving decision-making problems and the mismatch between the planning trajectory and the actual flight trajectory caused by the difference between the decision-making model and the actual aircraft model, this paper proposes a hierarchical on-line air combat maneuvering decision-making and control framework. Considering the real-time constraints, the maneuver decision problem is transformed into an expensive optimization problem at the decision planning layer. The surrogate-assisted differential evolution algorithm is proposed on the basis of the original differential evolution algorithm, and the planning trajectory is obtained through the 5 degrees of freedom (DOF) model. In the control execution layer, the planning trajectory is tracked through the nonlinear dynamic inverse tracking control method to realize the high-precision control of the 6DOF model. The simulation is carried out under four different initial situation scenarios, including head-on neutral, dominant, parallel neutral, and disadvantaged situations. The Monte Carlo simulation results show that the Surrogate-assisted differential evolution algorithm (SADE) can achieve a win rate of over 53% in all four initial scenarios. The proposed maneuver decision and control framework in this article achieves smooth flight trajectories and stable aircraft control, with each decision average taking 0.08 s, effectively solving the real-time problem of intelligent optimization algorithms in maneuver decision problems. Full article

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

26 pages, 1722 KiB

Open AccessArticle

Guidance Method with Collision Avoidance Using Guiding Vector Field for Multiple Unmanned Surface Vehicles

by Junbao Wei, Jianqiang Zhang, Haiyan Li, Jiawei Xia and Zhong Liu

Drones 2025, 9(2), 105; https://doi.org/10.3390/drones9020105 - 31 Jan 2025

Abstract

For the guidance problem of trajectory tracking in multiple unmanned surface vehicles (USVs), a trajectory tracking guidance method with collision avoidance based on a novel guiding vector field is proposed. Firstly, within the framework of the virtual leader–follower method for formation control, a [...] Read more.

For the guidance problem of trajectory tracking in multiple unmanned surface vehicles (USVs), a trajectory tracking guidance method with collision avoidance based on a novel guiding vector field is proposed. Firstly, within the framework of the virtual leader–follower method for formation control, a tracking error model for followers is developed based on the motion model of USVs. Secondly, considering the limitations of conventional trajectory tracking guidance methods in addressing various initial error conditions, a novel guiding vector field is developed for the design of the heading guidance law to enhance tracking performance. Then, a multi-USV collision avoidance strategy is proposed for formation navigation safety. The trigger conditions, actions and release conditions for collision avoidance are established in this strategy. USVs could avoid collision in time by following the commands outlined in the strategy, especially in complex situations where multiple USVs are simultaneously at risk of colliding with each other. And the theoretical proof is completed. Furthermore, the heading and velocity guidance laws are designed by combining the guidance vector field and the collision avoidance strategy. It is demonstrated that the tracking errors of the system are uniformly bounded based on Lyapunov stability theory. Finally, the effectiveness of the method is verified through simulation. Full article

(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems, 2nd Edition)

19 pages, 4069 KiB

Open AccessArticle

Hybrid DNN-Based Flight Power Estimation Framework for Unmanned Aerial Vehicles

by Minsu Kim, Minji Kim, Yukai Chen, Jaemin Kim and Donkyu Baek

Drones 2025, 9(2), 104; https://doi.org/10.3390/drones9020104 - 31 Jan 2025

Abstract

Unmanned Aerial Vehicles (UAVs) have been widely used in logistics and communication, though they were initially used for military purposes. However, because the motor must always be rotated, the flight range of an UAV is limited, which, in turn, restricts the scope of [...] Read more.

Unmanned Aerial Vehicles (UAVs) have been widely used in logistics and communication, though they were initially used for military purposes. However, because the motor must always be rotated, the flight range of an UAV is limited, which, in turn, restricts the scope of UAV applications. Of course, if UAV power consumption is predicted using AI, it is possible to effectively plan UAV operations by deriving optimal energy-efficient flight paths during the simulation phase. However, when using deep neural networks (DNNs) to build a UAV power consumption model, it is difficult to make accurate inferences based solely on flight velocity data. For precise predictions, random vibration acceleration data, as a result of thrust and resistance, are also required. Unfortunately, such information cannot be obtained during the simulation phase and can only be acquired through the actual flight environment. In this paper, we propose the first hybrid DNN-based power model that combines a DNN-based power consumption model and a data-driven random vibration acceleration model that derives UAV random vibration acceleration information based on flight velocity and environment. The proposed modeling framework was evaluated with flight experiments, demonstrating a 6.12% root mean squared percentage error (RMSPE), which is 39.45% more accurate when compared with a conventional DNN-only power model. In addition, we performed case studies to show that it is possible to find energy-efficient flight paths. Full article

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26 pages, 6834 KiB

Open AccessArticle

Stochastic Potential Game-Based Target Tracking and Encirclement Approach for Multiple Unmanned Aerial Vehicles System

by Kejie Yang, Ming Zhu, Xiao Guo, Yifei Zhang and Yuting Zhou

Drones 2025, 9(2), 103; https://doi.org/10.3390/drones9020103 - 30 Jan 2025

Abstract

Utilizing fully distributed intelligent control algorithms has enabled the gradual adoption of the multiple unmanned aerial vehicles system for executing Target Tracking and Encirclement missions in industrial and civil applications. Restricted by the evasion behavior of the target, current studies focus on constructing [...] Read more.

Utilizing fully distributed intelligent control algorithms has enabled the gradual adoption of the multiple unmanned aerial vehicles system for executing Target Tracking and Encirclement missions in industrial and civil applications. Restricted by the evasion behavior of the target, current studies focus on constructing zero-sum game settings, and existing strategy solvers that accommodate continuous state-action spaces have exhibited only modest performance. To tackle the challenges mentioned above, we devise a Stochastic Potential Game framework to model the mission scenario while considering the environment’s limited observability. Furthermore, a multi-agent reinforcement learning method is proposed to estimate the near Nash Equilibrium strategy in the above game scenario, which utilizes time-serial relative kinematic information and obstacle observation. In addition, considering collision avoidance and cooperative tracking, several techniques, such as novel reward functions and recurrent network structures, are presented to optimize the training process. The results of numerical simulations demonstrate that the proposed method exhibits superior search capability for Nash strategies. Moreover, through dynamic virtual experiments conducted with speed and attitude controllers, it has been shown that well-trained actors can effectively act as practical navigators for the real-time swarm control. Full article

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

22 pages, 5791 KiB

Open AccessArticle

Vibration Analysis Using Multi-Layer Perceptron Neural Networks for Rotor Imbalance Detection in Quadrotor UAV

by Ba Tarfi Salem Abdullah Salem, Mohd Na’im Abdullah, Faizal Mustapha, Nur Shahirah Atifah Kanirai and Mazli Mustapha

Drones 2025, 9(2), 102; https://doi.org/10.3390/drones9020102 - 30 Jan 2025

Abstract

Rotor imbalance in quadrotor UAVs poses a critical challenge, compromising flight stability, increasing maintenance demands, and reducing overall operational efficiency. Traditional vibration analysis methods, such as Fast Fourier Transform (FFT) and wavelet analysis, often struggle with non-stationary signals and real-time data processing, limiting [...] Read more.

Rotor imbalance in quadrotor UAVs poses a critical challenge, compromising flight stability, increasing maintenance demands, and reducing overall operational efficiency. Traditional vibration analysis methods, such as Fast Fourier Transform (FFT) and wavelet analysis, often struggle with non-stationary signals and real-time data processing, limiting their effectiveness under dynamic UAV operating conditions. To address these challenges, this study develops a machine learning-based vibration analysis system using a Multi-Layer Perceptron (MLP) neural network for real-time rotor imbalance detection. The system integrates Micro-Electro-Mechanical Systems (MEMS) sensors for vibration data acquisition, preprocessing techniques for noise reduction and feature extraction, and an optimized MLP architecture tailored to high-dimensional vibration data. Experimental validation was conducted under controlled flight scenarios, collecting a comprehensive dataset of 800 samples representing both balanced and imbalanced rotor conditions. The optimized MLP model, featuring five hidden layers, achieved a Root Mean Squared Error (RMSE) of 0.1414 and a correlation coefficient (R²) of 0.9224 on the test dataset, demonstrating high accuracy and reliability. This study highlights the potential of MLP-based diagnostics to enhance UAV reliability, safety, and operational efficiency, providing a scalable and effective solution for rotor imbalance detection in dynamic environments. The findings offer significant implications for improving UAV performance in addition to minimizing downtime in various industrial and commercial applications. Full article

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29 pages, 3517 KiB

Open AccessArticle

Assessing Lightweight Folding UAV Reliability Through a Photogrammetric Case Study: Extracting Urban Village’s Buildings Using Object-Based Image Analysis (OBIA) Method

by Junyu Kuang, Yingbiao Chen, Zhenxiang Ling, Xianxin Meng, Wentao Chen and Zihao Zheng

Drones 2025, 9(2), 101; https://doi.org/10.3390/drones9020101 - 29 Jan 2025

Abstract

With the rapid advancement of drone technology, modern drones have achieved high levels of functional integration, alongside structural improvements that include lightweight, compact designs with foldable features, greatly enhancing their flexibility and applicability in photogrammetric applications. Nevertheless, limited research currently explores data collected [...] Read more.

With the rapid advancement of drone technology, modern drones have achieved high levels of functional integration, alongside structural improvements that include lightweight, compact designs with foldable features, greatly enhancing their flexibility and applicability in photogrammetric applications. Nevertheless, limited research currently explores data collected by such compact UAVs, and whether they can balance a small form factor with high data quality remains uncertain. To address this challenge, this study acquired the remote sensing data of a peri-urban area using the DJI Mavic 3 Enterprise and applied Object-Based Image Analysis (OBIA) to extract high-density buildings. It was found that this drone offers high portability, a low operational threshold, and minimal regulatory constraints in practical applications, while its captured imagery provides rich textural details that clearly depict the complex surface features in urban villages. To assess the accuracy of the extraction results, the visual comparison between the segmentation outputs and airborne LiDAR point clouds captured by the DJI M300 RTK was performed, and classification performance was evaluated based on confusion matrix metrics. The results indicate that the boundaries of the segmented objects align well with the building edges in the LiDAR point cloud. The classification accuracy of the three selected algorithms exceeded 80%, with the KNN classifier achieving an accuracy of 91% and a Kappa coefficient of 0.87, which robustly demonstrate the reliability of the UAV data and validate the feasibility of the proposed approach in complex cases. As a practical case reference, this study is expected to promote the wider application of lightweight UAVs across various fields. Full article

(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)

22 pages, 13006 KiB

Open AccessArticle

LCSC-UAVNet: A High-Precision and Lightweight Model for Small-Object Identification and Detection in Maritime UAV Perspective

by Yanjuan Wang, Jiayue Liu, Jun Zhao, Zhibin Li, Yuxian Yan, Xiaohong Yan, Fengqiang Xu and Fengqi Li

Drones 2025, 9(2), 100; https://doi.org/10.3390/drones9020100 - 29 Jan 2025

Abstract

Unmanned Aerial Vehicle (UAV) object detection is crucial in various fields, such as maritime rescue and disaster investigation. However, due to small objects and the limitations of UAVs’ hardware and computing power, detection accuracy and computational overhead are the bottleneck issues of UAV [...] Read more.

Unmanned Aerial Vehicle (UAV) object detection is crucial in various fields, such as maritime rescue and disaster investigation. However, due to small objects and the limitations of UAVs’ hardware and computing power, detection accuracy and computational overhead are the bottleneck issues of UAV object detection. To address these issues, a novel convolutional neural network (CNN) model, LCSC-UAVNet, is proposed, which substantially enhances the detection accuracy and saves computing resources. To address the issues of low parameter utilization and insufficient detail capture, we designed the Lightweight Shared Difference Convolution Detection Head (LSDCH). It combines shared convolution layers with various differential convolution to enhance the detail capture ability for small objects. Secondly, a lightweight CScConv module was designed and integrated to enhance detection speed while reducing the number of parameters and computational cost. Additionally, a lightweight Contextual Global Module (CGM) was designed to extract global contextual information from the sea surface and features of small objects in maritime environments, thus reducing the false negative rate for small objects. Lastly, we employed the WIoUv2 loss function to address the sample imbalance issue of the datasets, enhancing the detection capability. To evaluate the performance of the proposed algorithm, experiments were performed across three commonly used datasets: SeaDroneSee, AFO, and MOBdrone. Compared with the state-of-the-art algorithms, the proposed model showcases improvements in mAP, recall, efficiency, where the mAP increased by over 10%. Furthermore, it utilizes only 5.6 M parameters and 16.3 G floating-point operations, outperforming state-of-the-art models such as YOLOv10 and RT-DETR. Full article

19 pages, 4941 KiB

Open AccessArticle

Sensitivity Analysis of Unmanned Aerial Vehicle Composite Wing Structural Model Regarding Material Properties and Laminate Configuration

by Artur Kierzkowski, Jakub Wróbel, Maciej Milewski and Angelos Filippatos

Drones 2025, 9(2), 99; https://doi.org/10.3390/drones9020099 - 28 Jan 2025

Abstract

This study optimizes the structural design of a composite wing shell by minimizing mass and maximizing the first natural frequency. The analysis focuses on the effects of polyvinyl chloride (PVC) foam thickness and the fiber orientation angle of the inner carbon layers, with [...] Read more.

This study optimizes the structural design of a composite wing shell by minimizing mass and maximizing the first natural frequency. The analysis focuses on the effects of polyvinyl chloride (PVC) foam thickness and the fiber orientation angle of the inner carbon layers, with the outer layers fixed at ±45° for torsional rigidity. A Multi-Objective Genetic Algorithm (MOGA), well suited for complex engineering problems, was employed alongside Design of Experiments to develop a precise response surface model, achieving predictive errors of 0% for mass and 2.99% for frequency. The optimal configuration—90° and 0° fiber orientations for the upper and lower layers and a foam thickness of 1.05 mm—yielded a mass of 412 g and a frequency of 122.95 Hz. These findings demonstrate the efficacy of MOGA in achieving innovative lightweight aerospace designs, striking a balance between material efficiency and structural performance. Full article

(This article belongs to the Section Drone Design and Development)

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21 pages, 1981 KiB

Open AccessArticle

Efficient Coverage Path Planning for a Drone in an Urban Environment

by Joanne Sabag, Barak Pinkovich, Ehud Rivlin and Hector Rotstein

Drones 2025, 9(2), 98; https://doi.org/10.3390/drones9020098 - 27 Jan 2025

Abstract

Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future [...] Read more.

Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future drones should be able to solve the last-mile challenge and land safely in urban areas. This paper addresses the path planning task for an autonomous drone searching for a landing place in an urban environment. Our algorithm uses a novel multi-resolution probabilistic approach in which visual information is collected by the drone at decreasing altitudes. As part of the exploration task, we present the Global Path Planning (GPP) problem, which uses probabilistic information and the camera’s field of view to plan safe trajectories that will maximize the search success by covering areas with high potential for proper landing while avoiding no-fly zones and complying with time constraints. The GPP problem is formulated as a minimization problem and then is shown to be NP-hard. As a baseline, we develop an approximation algorithm based on an exhaustive search, and then we devise a more complex yet efficient heuristic algorithm to solve the problem. Finally, we evaluate the algorithms’ performance using simulation experiments. Simulation results obtained from various scenarios show that the proposed heuristic algorithm significantly reduces computation time while keeping coverage performance close to the baseline. To the best of our knowledge, this is the first work referring to a multi-resolution approach to such search missions; further, in particular, the GPP problem has not been addressed previously. Full article

22 pages, 25824 KiB

Open AccessArticle

NoctuDroneNet: Real-Time Semantic Segmentation of Nighttime UAV Imagery in Complex Environments

by Ruokun Qu, Jintao Tan, Yelu Liu, Chenglong Li and Hui Jiang

Drones 2025, 9(2), 97; https://doi.org/10.3390/drones9020097 - 27 Jan 2025

Abstract

Nighttime semantic segmentation represents a challenging frontier in computer vision, made particularly difficult by severe low-light conditions, pronounced noise, and complex illumination patterns. These challenges intensify when dealing with Unmanned Aerial Vehicle (UAV) imagery, where varying camera angles and altitudes compound the difficulty. [...] Read more.

Nighttime semantic segmentation represents a challenging frontier in computer vision, made particularly difficult by severe low-light conditions, pronounced noise, and complex illumination patterns. These challenges intensify when dealing with Unmanned Aerial Vehicle (UAV) imagery, where varying camera angles and altitudes compound the difficulty. In this paper, we introduce NoctuDroneNet (Nocturnal UAV Drone Network, hereinafter referred to as NoctuDroneNet), a real-time segmentation model tailored specifically for nighttime UAV scenarios. Our approach integrates convolution-based global reasoning with training-only semantic alignment modules to effectively handle diverse and extreme nighttime conditions. We construct a new dataset, NUI-Night, focusing on low-illumination UAV scenes to rigorously evaluate performance under conditions rarely represented in standard benchmarks. Beyond NUI-Night, we assess NoctuDroneNet on the Varied Drone Dataset (VDD), a normal-illumination UAV dataset, demonstrating the model’s robustness and adaptability to varying flight domains despite the lack of large-scale low-light UAV benchmarks. Furthermore, evaluations on the Night-City dataset confirm its scalability and applicability to complex nighttime urban environments. NoctuDroneNet achieves state-of-the-art performance on NUI-Night, surpassing strong real-time baselines in both segmentation accuracy and speed. Qualitative analyses highlight its resilience to under-/over-exposure and small-object detection, underscoring its potential for real-world applications like UAV emergency landings under minimal illumination. Full article

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45 pages, 20140 KiB

Open AccessArticle

Development and Experimental Validation of a Sense-and-Avoid System for a Mini-UAV

by Marco Fiorio, Roberto Galatolo and Gianpietro Di Rito

Drones 2025, 9(2), 96; https://doi.org/10.3390/drones9020096 - 26 Jan 2025

Abstract

This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research [...] Read more.

This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The system leverages advanced computer vision algorithms and an extended Kalman filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive maneuver computation. A novel hardware-in-the-loop (HIL) simulation environment was developed and used for validation, enabling the evaluation of closed-loop real-time interaction between the “Sense” and “Avoid” subsystems. Extensive numerical simulations and a flight test campaign demonstrate the system’s effectiveness in real-time detection and the avoidance of non-cooperative obstacles, ensuring compliance with UAV aero mechanical and safety constraints in terms of minimum separation requirements. The novelty of this research lies in (1) the design of an innovative and efficient visual processing pipeline tailored for SWaP-constrained mini-UAVs, (2) the formulation an EKF-based data fusion strategy integrating optical data with a custom-built Doppler radar, and (3) the development of a unique HIL simulation environment with realistic scenery generation for comprehensive system evaluation. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, significantly contributing to the safety of flight in non-segregated airspaces Full article

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36 pages, 3892 KiB

Open AccessArticle

Mutual Cooperation System for Task Execution Between Ground Robots and Drones Using Behavior Tree-Based Action Planning and Dynamic Occupancy Grid Mapping

by Hiroaki Kobori and Kosuke Sekiyama

Drones 2025, 9(2), 95; https://doi.org/10.3390/drones9020095 - 26 Jan 2025

Abstract

This study presents a cooperative system where drones and ground robots share information to efficiently complete tasks in environments that challenge the capabilities of a single robot. Drones focus on exploring high-interest areas for ground robots, generating occupancy grid maps and identifying high-risk [...] Read more.

This study presents a cooperative system where drones and ground robots share information to efficiently complete tasks in environments that challenge the capabilities of a single robot. Drones focus on exploring high-interest areas for ground robots, generating occupancy grid maps and identifying high-risk routes. Ground robots use this information to evaluate and adapt routes as needed. Flexible action planning through behavior trees enables the robots to respond dynamically to environmental changes, facilitating spontaneous and adaptable cooperation. Experiments with real robots confirmed the system’s performance and adaptability to various settings. Specifically, when high-risk areas were identified from drone provided information, ground robots generated alternative routes to bypass these zones, demonstrating the system’s capacity to navigate complex paths while minimizing risks. This establishes a basis for scaling to larger environments. The proposed system is expected to improve the safety and efficiency of robot operations by enabling multiple robots to accomplish complex tasks collaboratively-tasks that would be difficult or time consuming for an individual robot. The findings demonstrate the potential for multi-robot cooperation to enhance task execution in challenging environments and provide a framework for future research on effective role sharing and information exchange in autonomous systems. Full article

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17 pages, 4887 KiB

Open AccessArticle

Towards Mobile Wind Measurements Using Joust Configured Ultrasonic Anemometer for Applications in Gas Flux Quantification

by Derek Hollenbeck, Colin Edgar, Eugenie Euskirchen and Kristen Manies

Drones 2025, 9(2), 94; https://doi.org/10.3390/drones9020094 - 26 Jan 2025

Abstract

Small uncrewed aerial systems (sUASs) can be used to quantify emissions of greenhouse and other gases, providing flexibility in quantifying these emissions from a multitude of sources, including oil and gas infrastructure, volcano plumes, wildfire emissions, and natural sources. However, sUAS-based emission estimates [...] Read more.

Small uncrewed aerial systems (sUASs) can be used to quantify emissions of greenhouse and other gases, providing flexibility in quantifying these emissions from a multitude of sources, including oil and gas infrastructure, volcano plumes, wildfire emissions, and natural sources. However, sUAS-based emission estimates are sensitive to the accuracy of wind speed and direction measurements. In this study, we examined how filtering and correcting sUAS-based wind measurements affects data accuracy by comparing data from a miniature ultrasonic anemometer mounted on a sUAS in a joust configuration to highly accurate wind data taken from a nearby eddy covariance flux tower (aka the Tower). These corrections had a small effect on wind speed error, but reduced wind direction errors from 50° to >120° to 20–30°. A concurrent experiment examining the amount of error due to the sUAS and the Tower not being co-located showed that the impact of this separation was 0.16–0.21

{ms}^{- 1}

, a small influence on wind speed errors. Lower wind speed errors were correlated with lower turbulence intensity and higher relative wind speeds. There were also some loose trends in diminished wind direction errors at higher relative wind speeds. Therefore, to improve the quality of sUAS-based wind measurements, our study suggested that flight planning consider optimizing conditions that can lower turbulence intensity and maximize relative wind speeds as well as include post-flight corrections. Full article

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21 pages, 707 KiB

Open AccessArticle

Integrative Path Planning for Multi-Rotor Logistics UAVs Considering UAV Dynamics, Energy Efficiency, and Obstacle Avoidance

by Kunpeng Wu, Juncong Lan, Shaofeng Lu, Chaoxian Wu, Bingjian Liu and Zenghao Lu

Drones 2025, 9(2), 93; https://doi.org/10.3390/drones9020093 - 25 Jan 2025

Abstract

Due to their high flexibility, low cost, and energy-saving advantages, applying Unmanned Aerial Vehicles (UAVs) in logistics is a promising field to achieve better social and economic benefits. Since UAVs’ energy storage capacity is generally low, it is essential to reduce energy costs [...] Read more.

Due to their high flexibility, low cost, and energy-saving advantages, applying Unmanned Aerial Vehicles (UAVs) in logistics is a promising field to achieve better social and economic benefits. Since UAVs’ energy storage capacity is generally low, it is essential to reduce energy costs to improve their system’s energy efficiency. In this paper, we proposed a novel trajectory planning framework to achieve the optimal trajectory with the minimum amount of energy consumption under the constraints of obstacles in a static environment. Based on UAV dynamics, we first derived the required power functions of multi-rotor UAVs in vertical and horizontal flight. To generate a feasible trajectory, we first adopted the A* algorithm to find a path and developed a safe flight corridor for the UAV to fly across by expanding the waypoints against the environment, and then proposed a time-discretization method to formulate the trajectory generation problem and solve it by the convex optimization algorithm. The optimization results in a static environment with obstacles demonstrated that the proposed method could efficiently and effectively obtain the optimal trajectory with the minimum amount of energy consumption under different allowed mission times and payloads. The framework would promote a variety of logistics UAV applications relevant to trajectory planning. Full article

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

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17 pages, 20814 KiB

Open AccessArticle

Vision-Based Gesture-Driven Drone Control in a Metaverse-Inspired 3D Simulation Environment

by Yaseen, Oh-Jin Kwon, Jaeho Kim, Jinhee Lee and Faiz Ullah

Drones 2025, 9(2), 92; https://doi.org/10.3390/drones9020092 - 24 Jan 2025

Abstract

Unlike traditional remote control systems for controlling unmanned aerial vehicles (UAVs) and drones, active research is being carried out in the domain of vision-based hand gesture recognition systems for drone control. However, contrary to static and sensor based hand gesture recognition, recognizing dynamic [...] Read more.

Unlike traditional remote control systems for controlling unmanned aerial vehicles (UAVs) and drones, active research is being carried out in the domain of vision-based hand gesture recognition systems for drone control. However, contrary to static and sensor based hand gesture recognition, recognizing dynamic hand gestures is challenging due to the complex nature of multi-dimensional hand gesture data, present in 2D images. In a real-time application scenario, performance and safety is crucial. Therefore we propose a hybrid lightweight dynamic hand gesture recognition system and a 3D simulator based drone control environment for live simulation. We used transfer learning-based computer vision techniques to detect dynamic hand gestures in real-time. The gestures are recognized, based on which predetermine commands are selected and sent to a drone simulation environment that operates on a different computer via socket connectivity. Without conventional input devices, hand gesture detection integrated with the virtual environment offers a user-friendly and immersive way to control drone motions, improving user interaction. Through a variety of test situations, the efficacy of this technique is illustrated, highlighting its potential uses in remote-control systems, gaming, and training. The system is tested and evaluated in real-time, outperforming state-of-the-art methods. The code utilized in this study are publicly accessible. Further details can be found in the “Data Availability Statement”. Full article

(This article belongs to the Special Issue Mobile Fog and Edge Computing in Drone Swarms)

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