Drones

26 pages, 37822 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Drone-Based VNIR–SWIR Hyperspectral Imaging for Environmental Monitoring of a Uranium Legacy Mine Site

by Victor Tolentino, Andres Ortega Lucero, Friederike Koerting, Ekaterina Savinova, Justus Constantin Hildebrand and Steven Micklethwaite

Drones 2025, 9(4), 313; https://doi.org/10.3390/drones9040313 - 17 Apr 2025

Viewed by 1190

Abstract

Growing awareness of the environmental cost of mining operations has led to increased research on monitoring and restoring legacy mine sites. Hyperspectral imaging (HSI) has emerged as a valuable tool in the mining life cycle, including post-mining environment. By detecting variations in crystal [...] Read more.

Growing awareness of the environmental cost of mining operations has led to increased research on monitoring and restoring legacy mine sites. Hyperspectral imaging (HSI) has emerged as a valuable tool in the mining life cycle, including post-mining environment. By detecting variations in crystal structure and physicochemical attributes on the surface of materials, HSI provides insights into site environmental and ecological conditions. Here, we explore the capabilities of drone-based HSI for mapping surface patterns related to contamination dispersal in a legacy uranium-rare earth element mine site. Hyperspectral data across the visible to near-infrared (VNIR) and short-wave infrared (SWIR) wavelength ranges (400–2500 nm) were collected over selected areas of the former Mary Kathleen mine site in Queensland, Australia. Analyses were performed using data-driven (Spectral Angle Mapper—SAM) and knowledge-based (Band Ratios—BRs) spectral processing techniques. SAM identifies contamination patterns and differentiates mineral compositions within visually similar areas. However, its accuracy is limited when mapping specific minerals, as most endmembers represent mineral groups or mixtures. BR highlights reactive surfaces and clay mixtures, reinforcing key patterns identified by SAM. The results indicate that drone-based HSI can capture and distinguish complex surface trends, demonstrating the technology’s potential to enhance the assessment and monitoring of environmental conditions at a mine site. Full article

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

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26 pages, 4680 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Impact of Drone Disturbances on Wildlife: A Review

by Saadia Afridi, Lucie Laporte-Devylder, Guy Maalouf, Jenna M. Kline, Samuel G. Penny, Kasper Hlebowicz, Dylan Cawthorne and Ulrik Pagh Schultz Lundquist

Drones 2025, 9(4), 311; https://doi.org/10.3390/drones9040311 - 16 Apr 2025

Viewed by 2115

Abstract

Drones are becoming increasingly valuable tools in wildlife studies due to their ability to access remote areas and offer high-resolution information with minimal human interference. Their application is, however, causing concern regarding wildlife disturbance. This review synthesizes the existing literature on how animals [...] Read more.

Drones are becoming increasingly valuable tools in wildlife studies due to their ability to access remote areas and offer high-resolution information with minimal human interference. Their application is, however, causing concern regarding wildlife disturbance. This review synthesizes the existing literature on how animals within terrestrial, aerial, and aquatic environments are impacted by drone disturbance in relation to operational variables, sensory stimulation, species-specific sensitivity, and physiological and behavioral responses. We found that drone altitude, speed, approach distance, and noise levels significantly influence wildlife responses, with some species exhibiting increased vigilance, flight responses, or physiological stress. Environmental context and visual cues are also involved in species detection of drones and disturbance thresholds. Although the short-term response to behavior change has been well documented, long-term consequences of repeated drone exposure remain poorly known. This paper identifies the necessity for continued research into drone–wildlife interactions, with an emphasis on the requirement to minimize disturbance by means of improved flight parameters and technology. Full article

(This article belongs to the Special Issue Drone-Based Wildlife Protection, Monitoring, and Conservation Management：2nd Edition)

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28 pages, 20307 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

AI-Driven UAV and IoT Traffic Optimization: Large Language Models for Congestion and Emission Reduction in Smart Cities

by Álvaro Moraga , J. de Curtò, I. de Zarzà and Carlos T. Calafate

Drones 2025, 9(4), 248; https://doi.org/10.3390/drones9040248 - 26 Mar 2025

Cited by 2 | Viewed by 2037

Abstract

Traffic congestion and carbon emissions remain pressing challenges in urban mobility. This study explores the integration of UAV (drone)-based monitoring systems and IoT sensors, modeled as induction loops, with Large Language Models (LLMs) to optimize traffic flow. Using the SUMO simulator, we conducted [...] Read more.

Traffic congestion and carbon emissions remain pressing challenges in urban mobility. This study explores the integration of UAV (drone)-based monitoring systems and IoT sensors, modeled as induction loops, with Large Language Models (LLMs) to optimize traffic flow. Using the SUMO simulator, we conducted experiments in three urban scenarios: Pacific Beach and Coronado in San Diego, and Argüelles in Madrid. A Gemini-2.0-Flash experimental LLM was interfaced with the simulation to dynamically adjust vehicle speeds based on real-time traffic conditions. Comparative results indicate that the AI-assisted approach significantly reduces congestion and CO₂ emissions compared to a baseline simulation without AI intervention. This research highlights the potential of UAV-enhanced IoT frameworks for adaptive, scalable traffic management, aligning with the future of drone-assisted urban mobility solutions. Full article

(This article belongs to the Special Issue Visual Language Models and Large Language Models for Unmanned Aerial Vehicles)

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46 pages, 4683 KiB

Open AccessFeature PaperEditor’s ChoiceReview

A Review of Last-Mile Delivery Optimization: Strategies, Technologies, Drone Integration, and Future Trends

by Abdullahi Sani Shuaibu, Ashraf Sharif Mahmoud and Tarek Rahil Sheltami

Drones 2025, 9(3), 158; https://doi.org/10.3390/drones9030158 - 21 Feb 2025

Cited by 8 | Viewed by 10293

Abstract

Last-mile delivery (LMD) is an important aspect of contemporary logistics that directly affects operational cost, efficiency, and customer satisfaction. In this paper, we provide a review of the optimization techniques of LMD, focusing on Artificial Intelligence (AI) driven decision-making, IoT-supported real-time monitoring, and [...] Read more.

Last-mile delivery (LMD) is an important aspect of contemporary logistics that directly affects operational cost, efficiency, and customer satisfaction. In this paper, we provide a review of the optimization techniques of LMD, focusing on Artificial Intelligence (AI) driven decision-making, IoT-supported real-time monitoring, and hybrid delivery networks. The combination of AI and IoT improves predictive analytics, dynamic routing, and fleet management, but scalability and regulatory issues are still major concerns. Hybrid frameworks that integrate drones or Unmanned Aerial Vehicles (UAVs), ground robots, and conventional vehicles reduce energy expenditure and increase delivery range, especially in urban contexts. Furthermore, sustainable logistics approaches, including electric vehicle fleets and shared delivery infrastructures, provide promise for minimizing environmental impact. However, economic viability, legal frameworks, and infrastructure readiness still influence the feasibility of large-scale adoption. This review offers a perspective on the changing patterns in LMD, calling for regulatory evolution, technological advancement, as well as interdisciplinary approaches toward cost-effective, durable, and environmentally friendly logistics systems. Full article

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59 pages, 45108 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Safety Systems for Emergency Landing of Civilian Unmanned Aerial Vehicles (UAVs)—A Comprehensive Review

by Mohsen Farajijalal, Hossein Eslamiat, Vikrant Avineni, Eric Hettel and Clark Lindsay

Drones 2025, 9(2), 141; https://doi.org/10.3390/drones9020141 - 14 Feb 2025

Cited by 2 | Viewed by 3048

Abstract

The expanding use of civilian unmanned aerial vehicles (UAVs) has brought forth a crucial need to address the safety risks they pose in the event of failure, especially when flying in populated areas. This paper reviews recent advancements in recovery systems designed for [...] Read more.

The expanding use of civilian unmanned aerial vehicles (UAVs) has brought forth a crucial need to address the safety risks they pose in the event of failure, especially when flying in populated areas. This paper reviews recent advancements in recovery systems designed for the emergency landing of civilian UAVs. It covers a wide range of recovery methods, categorizing them based on different recovery approaches and UAV types, including multirotor and fixed-wing. The study highlights the diversity of recovery strategies, ranging from parachute and airbag systems to software-based methods and hybrid solutions. It emphasizes the importance of considering UAV-specific characteristics and operational environments when selecting appropriate safety systems. Furthermore, by comparing various emergency landing systems, this study reveals that integrating multiple approaches based on the UAV type and mission requirements can achieve broader cover of emergency situations compared to using a single system for a specific scenario. Examples of UAVs that utilize emergency landing systems are also provided. For each recovery system, three key parameters of operating altitude, flight speed and added weight are presented. Researchers and UAV developers can utilize this information to identify a suitable emergency landing method tailored to their mission requirements and available UAVs. Based on the key trends and challenges found in the literature, this review concludes by proposing specific, actionable recommendations. These recommendations are directed towards researchers, UAV developers, and regulatory bodies, and focus on enhancing the safety of civilian UAV operations through the improvement of emergency landing systems. Full article

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

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22 pages, 2471 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Underwater Acoustic MAC Protocol for Multi-Objective Optimization Based on Multi-Agent Reinforcement Learning

by Jinfang Jiang, Yiling Dong, Guangjie Han and Gang Su

Drones 2025, 9(2), 123; https://doi.org/10.3390/drones9020123 - 7 Feb 2025

Cited by 1 | Viewed by 742

Abstract

In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, [...] Read more.

In underwater acoustic networks (UANs), communication between nodes is susceptible to long propagation delays, limited energy, and channel conflicts, and traditional multi-access control (MAC) protocols cannot easily cope with these challenges. To enhance network throughput and balance channel allocation fairness and energy efficiency, this paper proposes a multi-objective optimization MAC protocol (MOMA-MAC) based on multi-agent reinforcement learning. MOMA-MAC utilizes a delay reward mechanism combined with the Multi-agent Proximal Policy Optimization Algorithm (MAPPO) to design a dual reward mechanism, which enables agents to adaptively collaborate and compete to optimize the use of network resources. According to experimental results, MOMA-MAC performs noticeably better than traditional MAC protocols and deep reinforcement learning-based methods in terms of throughput, energy efficiency, and fairness in multi-agent scenarios, showing great potential for improving communication efficiency and energy utilization. Full article

(This article belongs to the Topic Advances in Wireless and Mobile Networking)

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30 pages, 11390 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning

by Xiukang Liu, Fufu Wang, Yu Liu and Long Li

Drones 2025, 9(2), 118; https://doi.org/10.3390/drones9020118 - 5 Feb 2025

Cited by 3 | Viewed by 1198

Abstract

In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning [...] Read more.

In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning issues that involve multiple machines. Here, we introduce an advanced mathematical model for cooperative path planning among multiple UAVs in urban logistics scenarios, employing the non-dominated sorting black-winged kite algorithm (NSBKA) to address this multi-objective optimization challenge. To evaluate the efficacy of NSBKA, it was benchmarked against other algorithms using the Zitzler, Deb, and Thiele (ZDT) test problems, Deb, Thiele, Laumanns, and Zitzler (DTLZ) test problems, and test functions from the conference on evolutionary computation 2009 (CEC2009) for three types of multi-objective problems. Comparative analyses and statistical results indicate that the proposed algorithm outperforms on all 22 test functions. To verify the capability of NSBKA in addressing the multi-UAV cooperative problem model, the algorithm is applied to solve the problem. Simulation experiments for three UAVs and five UAVs show that the proposed algorithm can obtain a more reasonable collaborative path solution set for UAVs. Moreover, path planning based on NSBKA is generally superior to other algorithms in terms of energy saving, safety, and computing efficiency during planning. This affirms the effectiveness of the meta-heuristic algorithm in dealing with multiple objective multi-UAV cooperation problems and further enhances the robustness and competitiveness of NSBKA. Full article

(This article belongs to the Special Issue Autonomous Flight of Drone: Control, Trajectory Optimization and Mission Planning: 2nd Edition)

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25 pages, 943 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Optimization of Bandwidth Allocation and UAV Placement in Active RIS-Assisted UAV Communication Networks with Wireless Backhaul

by Thi-Thuy-Minh Tran, Binh-Minh Vu and Oh-Soon Shin

Drones 2025, 9(2), 111; https://doi.org/10.3390/drones9020111 - 2 Feb 2025

Cited by 1 | Viewed by 1187

Abstract

In this paper, we present a novel design for unmanned aerial vehicle (UAV) communication networks with wireless backhaul, where an active reconfigurable intelligent surface (ARIS) is deployed to improve connections between a UAV and multiple users, while mitigating channel impairments in complex environments. [...] Read more.

In this paper, we present a novel design for unmanned aerial vehicle (UAV) communication networks with wireless backhaul, where an active reconfigurable intelligent surface (ARIS) is deployed to improve connections between a UAV and multiple users, while mitigating channel impairments in complex environments. The proposed design aims to maximize the achievable sum rate of all networks by jointly optimizing UAV placement; resource management strategies; transmit power allocation; and ARIS reflection coefficients, subject to backhaul constraints and power budget limitations in the ARIS system. The resulting optimization problem is highly non-convex, posing significant challenges. To tackle this, we decompose the problem into three interrelated sub-problems and apply inner approximation (IA) techniques to handle the non-convexities within each sub-problem. Moreover, a comprehensive alternating optimization framework is proposed to implement an iterative solution for the sub-problems. Simulation results demonstrate that the proposed algorithm achieves approximately 59% improvement in the average sum rate, substantially enhancing overall network reliability compared to existing benchmark schemes. Full article

(This article belongs to the Special Issue Drone Communication, Networking, and Trajectory Control in Urban Environments)

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42 pages, 40649 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Multi-Drone System Proof of Concept for Forestry Applications

by André G. Araújo, Carlos A. P. Pizzino, Micael S. Couceiro and Rui P. Rocha

Drones 2025, 9(2), 80; https://doi.org/10.3390/drones9020080 - 21 Jan 2025

Cited by 3 | Viewed by 2814

Abstract

This study presents a multi-drone proof of concept for efficient forest mapping and autonomous operation, framed within the context of the OPENSWARM EU Project. The approach leverages state-of-the-art open-source simultaneous localisation and mapping (SLAM) frameworks, like LiDAR (Light Detection And Ranging) Inertial Odometry [...] Read more.

This study presents a multi-drone proof of concept for efficient forest mapping and autonomous operation, framed within the context of the OPENSWARM EU Project. The approach leverages state-of-the-art open-source simultaneous localisation and mapping (SLAM) frameworks, like LiDAR (Light Detection And Ranging) Inertial Odometry via Smoothing and Mapping (LIO-SAM), and Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm (DCL-SLAM), seamlessly integrated within the MRS UAV System and Swarm Formation packages. This integration is achieved through a series of procedures compliant with Robot Operating System middleware (ROS), including an auto-tuning particle swarm optimisation method for enhanced flight control and stabilisation, which is crucial for autonomous operation in challenging environments. Field experiments conducted in a forest with multiple drones demonstrate the system’s ability to navigate complex terrains as a coordinated swarm, accurately and collaboratively mapping forest areas. Results highlight the potential of this proof of concept, contributing to the development of scalable autonomous solutions for forestry management. The findings emphasise the significance of integrating multiple open-source technologies to advance sustainable forestry practices using swarms of drones. Full article

(This article belongs to the Special Issue Feature Papers for Drones in Agriculture and Forestry Section: 2nd Edition)

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22 pages, 9199 KiB

Open AccessFeature PaperEditor’s ChoiceReview

UAV Detection with Passive Radar: Algorithms, Applications, and Challenges

by Zhibo Tang, He Ma, Youmin Qu and Xingpeng Mao

Drones 2025, 9(1), 76; https://doi.org/10.3390/drones9010076 - 20 Jan 2025

Cited by 1 | Viewed by 5227

Abstract

The unmanned aerial vehicle (UAV) industry has developed rapidly in recent years and is being applied in a wide range of fields. However, incidents involving unauthorized UAVs that threaten public safety have occurred frequently, highlighting the need for effective and accurate methods to [...] Read more.

The unmanned aerial vehicle (UAV) industry has developed rapidly in recent years and is being applied in a wide range of fields. However, incidents involving unauthorized UAVs that threaten public safety have occurred frequently, highlighting the need for effective and accurate methods to detect and respond to illegal UAVs. This has led to the emergence of various UAV detection technologies, among which passive radar stands out due to its unique advantages. This review aims to offer insights that can support further research and development in the field of UAV detection using passive radar. We begin by exploring the origins of passive radar and then provide a comprehensive overview of its progress from multiple angles, particularly focusing on its application in UAV detection. Finally, we provide a forward-looking discussion on the future development trends and challenges faced by passive radar in UAV detection. Full article

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19 pages, 6329 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Spray Deposition and Drift as Influenced by Wind Speed and Spray Nozzles from a Remotely Piloted Aerial Application System

by Daniel E. Martin, Jeffrey W. Perine, Shanique Grant, Farah Abi-Akar, Jerri Lynn Henry and Mohamed A. Latheef

Drones 2025, 9(1), 66; https://doi.org/10.3390/drones9010066 - 16 Jan 2025

Cited by 4 | Viewed by 1278

Abstract

The phenomenal growth of remotely piloted aerial application systems (RPAASs) in recent years has raised questions about their impact on the off-target movement of plant protection products. The spray droplet spectrum is one of the important determining factors that govern droplet trajectories and [...] Read more.

The phenomenal growth of remotely piloted aerial application systems (RPAASs) in recent years has raised questions about their impact on the off-target movement of plant protection products. The spray droplet spectrum is one of the important determining factors that govern droplet trajectories and off-target movement of pesticide particles. A field study was conducted to compare in-swath and downwind spray deposition on ground samplers from a 20 L RPAAS platform, equipped with three different nozzles, which provided fine, medium, and extra-coarse droplet spectra. A fluorescent dye was used as a tracer to determine spray deposition. Airborne spray droplets were measured at 10 and 20 m downwind. Downwind deposition measured on ground samplers showed that the extra-coarse nozzle received significantly fewer deposits than the medium or the fine nozzle. Similarly, the airborne deposition for the extra-coarse nozzle was significantly less compared to either the fine or the medium nozzle. Linear mixed effects modeling confirmed these results and showed that wind speed served as a covariate by refining the deposition differences among nozzles. Results indicated that spray drift from RPAAS platforms may be mitigated by using appropriate nozzles that produce larger droplet spectra. These results will provide aerial applicators with a better understanding of the best management practices to mitigate drift. Full article

(This article belongs to the Special Issue Drones in Sustainable Agriculture)

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30 pages, 9579 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Unmanned Aircraft Systems (UASs): Current State, Emerging Technologies, and Future Trends

by Gennaro Ariante and Giuseppe Del Core

Drones 2025, 9(1), 59; https://doi.org/10.3390/drones9010059 - 15 Jan 2025

Cited by 5 | Viewed by 6554

Abstract

Unmanned aircraft, commonly referred to as drones, represent a valuable alternative for various operational tasks due to their versatility, flexibility, cost-effectiveness, and reusability. These features make them particularly advantageous in environments that are hazardous or inaccessible to humans. Recent developments have highlighted a [...] Read more.

Unmanned aircraft, commonly referred to as drones, represent a valuable alternative for various operational tasks due to their versatility, flexibility, cost-effectiveness, and reusability. These features make them particularly advantageous in environments that are hazardous or inaccessible to humans. Recent developments have highlighted a significant increase in the use of unmanned aircraft within metropolitan areas. This growth has necessitated the implementation of new regulations and guidelines to ensure the safe integration of UAS into urban environments. Consequently, the concept of UAM has emerged. UAM refers to an innovative air transportation paradigm designed for both passengers and cargo within urban settings, leveraging the capabilities of drones. This review manuscript explores the latest advancements for UAS, focusing on updated regulations, definitions, enabling technologies, and airspace classifications relevant to UAM operations. Additionally, it provides a comprehensive overview of unmanned aircraft systems, including their classifications, key features, and primary applications. Full article

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44 pages, 13137 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

The Future of Last-Mile Delivery: Lifecycle Environmental and Economic Impacts of Drone-Truck Parallel Systems

by Danwen Bao, Yu Yan, Yuhan Li and Jiajun Chu

Drones 2025, 9(1), 54; https://doi.org/10.3390/drones9010054 - 14 Jan 2025

Cited by 2 | Viewed by 4493

Abstract

With rapid advancements in unmanned aerial vehicle (UAV) technology, its integration into logistics operations has emerged as a promising solution for improving efficiency and sustainability. Among the emerging solutions, a collaborative delivery model involving drones and trucks addresses last-mile delivery challenges by leveraging [...] Read more.

With rapid advancements in unmanned aerial vehicle (UAV) technology, its integration into logistics operations has emerged as a promising solution for improving efficiency and sustainability. Among the emerging solutions, a collaborative delivery model involving drones and trucks addresses last-mile delivery challenges by leveraging the complementary strengths of both modes of transport. However, evaluating the environmental and economic impacts of this transportation mode requires a systematic framework to capture its unique characteristics and minimize environmental impacts and costs. This paper investigates the Parallel Drone Scheduling Traveling Salesman Problem (PDSTSP) to evaluate the environmental and economic sustainability of a collaborative drone-truck delivery system. Specifically, a mathematical model for this delivery system is developed to optimize joint delivery operations. Environmental impacts are assessed using a comprehensive Life Cycle Assessment (LCA), including emissions and operational noise, while a Life Cycle Cost Analysis (LCCA) quantifies economic performance across five cost dimensions. Sensitivity analysis explores factors such as delivery density, traffic congestion, and wind conditions. Results show that, compared to the electric vehicle fleet, the proposed model achieves an approximate 20% reduction in carbon emissions, while delivering a 20–30% cost reduction relative to the fuel truck fleet. Drones’ efficiency in short-distance deliveries alleviates trucks’ load, cutting environmental and operational costs. This study offers practical insights and recommendations for implementing drone-truck parallel delivery systems, particularly in high-demand density areas. Full article

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23 pages, 482 KiB

Open AccessFeature PaperEditor’s ChoiceSystematic Review

Systematic Literature Review Methodology for Drone Recharging Processes in Agriculture and Disaster Management

by Leonardo Grando, Juan Fernando Galindo Jaramillo, José Roberto Emiliano Leite and Edson Luiz Ursini

Drones 2025, 9(1), 40; https://doi.org/10.3390/drones9010040 - 8 Jan 2025

Cited by 4 | Viewed by 3322

Abstract

Unmanned Aerial Vehicles (UAVs), or drones, are becoming increasingly vital in agriculture and disaster management due to their autonomous monitoring, data collection, and service delivery capability. However, energy constraints often limit their potential, highlighting the need for efficient recharging and energy management solutions. [...] Read more.

Unmanned Aerial Vehicles (UAVs), or drones, are becoming increasingly vital in agriculture and disaster management due to their autonomous monitoring, data collection, and service delivery capability. However, energy constraints often limit their potential, highlighting the need for efficient recharging and energy management solutions. This systematic literature review (SLR) examines the current simulations of drone recharging technologies within precision agriculture and disaster relief. It highlights recent advancements, including various algorithms for path and mission planning, while identifying ongoing challenges, particularly the scarcity of studies on the recharging coordination that affects UAV operations in these fields. The review encompasses 36 high-quality studies from 2038 papers initially found in the literature. Despite significant progress in recharging technologies, achieving sustainable and continuous UAV operation remains challenging, especially in high-demand energy environments such as disaster zones and agricultural areas. We identify three research gaps—knowledge, methodological, and practical. There is a lack of drone recharging studies, as drones are energy-demanding devices. The studies show that the coordination process relies on communication, which can use more battery, and we also find a lack of real-world applications in the studies. Another finding is that the context of disaster is studied more than agricultural usage. Full article

(This article belongs to the Section Drones in Agriculture and Forestry)

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18 pages, 5874 KiB

Open AccessEditor’s ChoiceReview

Key Technology for Human-System Integration of Unmanned Aircraft Systems in Urban Air Transportation

by Chuanyan Feng, Jinwei Hou, Shuang Liu, Xiaoru Wanyan, Menglong Ding, Huadong Li, De Yan and Dawei Bie

Drones 2025, 9(1), 18; https://doi.org/10.3390/drones9010018 - 27 Dec 2024

Cited by 1 | Viewed by 1134

Abstract

Effective integration of human factors and systems engineering has become a technical challenge that constrains the full realization of human performance in unmanned aircraft systems (UAS) for urban air transportation. To address this challenge, breakthroughs are needed in key technologies related to human-system [...] Read more.

Effective integration of human factors and systems engineering has become a technical challenge that constrains the full realization of human performance in unmanned aircraft systems (UAS) for urban air transportation. To address this challenge, breakthroughs are needed in key technologies related to human-system integration (HSI) of UAS. Based on literature review and industry practices, unique HF challenges of UAS are identified, and two research issues, HSI analysis throughout UAS development lifecycle and HSI practice under UAS typical lifecycle stages, are summarized. To address these issues, a model-based human-system integration (MBHSI) design framework is proposed for the UAS development lifecycle, along with an HSI practice framework for UAS under typical human readiness levels. The HSI design and practice framework can provide references for HF design of UAS in urban air transportation. Full article

(This article belongs to the Special Issue Electric-Powered and Hybrid-Powered Unmanned Aerial Vehicle Technology and Applications of Low Altitude Aviation)

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24 pages, 3802 KiB

Open AccessEditor’s ChoiceArticle

Performance of Individual Tree Segmentation Algorithms in Forest Ecosystems Using UAV LiDAR Data

by Javier Marcello, María Spínola, Laia Albors, Ferran Marqués, Dionisio Rodríguez-Esparragón and Francisco Eugenio

Drones 2024, 8(12), 772; https://doi.org/10.3390/drones8120772 - 19 Dec 2024

Cited by 2 | Viewed by 3200

Abstract

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This [...] Read more.

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91% and an IoU of 83%. Full article

(This article belongs to the Section Drones in Agriculture and Forestry)

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22 pages, 10279 KiB

Open AccessEditor’s ChoiceArticle

Cybersecurity Challenges in UAV Systems: IEMI Attacks Targeting Inertial Measurement Units

by Issam Boukabou, Naima Kaabouch and Dulana Rupanetti

Drones 2024, 8(12), 738; https://doi.org/10.3390/drones8120738 - 8 Dec 2024

Viewed by 5386

Abstract

The rapid expansion in unmanned aerial vehicles (UAVs) across various sectors, such as surveillance, agriculture, disaster management, and infrastructure inspection, highlights the growing need for robust navigation systems. However, this growth also exposes critical vulnerabilities, particularly in UAV package delivery operations, where intentional [...] Read more.

The rapid expansion in unmanned aerial vehicles (UAVs) across various sectors, such as surveillance, agriculture, disaster management, and infrastructure inspection, highlights the growing need for robust navigation systems. However, this growth also exposes critical vulnerabilities, particularly in UAV package delivery operations, where intentional electromagnetic interference (IEMI) poses significant security and safety threats. This paper addresses IEMI attacks targeting inertial measurement units (IMUs) in UAVs, focusing on their susceptibility to medium-power electromagnetic interference. Our approach combines a comprehensive literature review and QuickField simulation with experimental validation using a commercially available 6-degree-of-freedom (DOF) IMU sensor. We propose a hardware-based electromagnetic shielding solution using mu-metal to mitigate IEMI’s impact on sensor performance. The study combines experimental testing with simulations to evaluate the shielding effectiveness under controlled conditions. The results of the measurements showed that medium-power IEMI significantly distorted IMU sensor readings, but our proposed shielding method effectively reduces the impact, improving sensor reliability. We demonstrate the mechanisms by which medium-power IEMI disrupts sensor operation, offering insights for future research directions. These findings also highlight the importance of integrating hardware-based shielding solutions to safeguard UAV systems against electromagnetic threats. Full article

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28 pages, 19518 KiB

Open AccessEditor’s ChoiceReview

Urban Air Mobility Communications and Networking: Recent Advances, Techniques, and Challenges

by Muhammad Yeasir Arafat and Sungbum Pan

Drones 2024, 8(12), 702; https://doi.org/10.3390/drones8120702 - 24 Nov 2024

Cited by 7 | Viewed by 4235

Abstract

Over the past few years, our traditional ground-based transportation system has encountered various challenges, including overuse, traffic congestion, growing urban populations, high infrastructure costs, and disorganization. Unmanned aerial vehicles, commonly referred to as drones, have significantly impacted aerial communication in both the academic [...] Read more.

Over the past few years, our traditional ground-based transportation system has encountered various challenges, including overuse, traffic congestion, growing urban populations, high infrastructure costs, and disorganization. Unmanned aerial vehicles, commonly referred to as drones, have significantly impacted aerial communication in both the academic and industrial sectors. Therefore, researchers and scientists from the aviation and automotive industries have collaborated to create an innovative air transport system that solves traditional transport problems. In the coming years, urban air mobility (UAM) is expected to become an emerging air transportation system that enables on-demand air travel. UAM is also anticipated to offer more environmentally friendly, cost-effective, and faster modes of transportation than ground-based alternatives. Owing to the unique characteristics of personal air vehicles, ensuring reliable communication and maintaining proper safety and security, air traffic management, collision detection, path planning, and highly accurate localization and navigation have become increasingly complex. This article provides an extensive literature review of recent technologies to address the challenges UAM faces. First, we present UAM communication requirements in terms of coverage, data rate, latency, spectrum efficiency, networking, and computing capabilities. Subsequently, we identify the potential key technological enablers to meet these requirements and overcome their challenges. Finally, we discuss open research issues, challenges, and future research directions for UAM deployment. Full article

(This article belongs to the Section Innovative Urban Mobility)

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20 pages, 11387 KiB

Open AccessEditor’s ChoiceArticle

An Algorithm for Affordable Vision-Based GNSS-Denied Strapdown Celestial Navigation

by Samuel Teague and Javaan Chahl

Drones 2024, 8(11), 652; https://doi.org/10.3390/drones8110652 - 7 Nov 2024

Viewed by 54761

Abstract

Celestial navigation is rarely seen in modern Uncrewed Aerial Vehicles (UAVs). The size and weight of a stabilized imaging system, and the lack of precision, tend to be at odds with the operational requirements of the aircraft. Nonetheless, celestial navigation is one of [...] Read more.

Celestial navigation is rarely seen in modern Uncrewed Aerial Vehicles (UAVs). The size and weight of a stabilized imaging system, and the lack of precision, tend to be at odds with the operational requirements of the aircraft. Nonetheless, celestial navigation is one of the few non-emissive modalities that enables global navigation over the ocean at night in Global Navigation Satellite System (GNSS) denied environments. This study demonstrates a modular, low cost, lightweight strapdown celestial navigation solution that is utilized in conjunction with Ardupilot running on a Cube Orange to produce position estimates to within 4 km. By performing an orbit through a full rotation of compass heading and averaging the position output, we demonstrate that the biases present in a strapdown imaging system can be nullified to drastically improve the position estimate. Furthermore, an iterative method is presented which enables the geometric alignment of the camera with the Attitude and Heading Reference System (AHRS) in-flight without an external position input. The algorithm is tested using real flight data captured from a fixed wing aircraft. The results from this study offer promise for the application of low cost celestial navigation as a redundant navigation modality in affordable, lightweight drones. Full article

(This article belongs to the Special Issue Drones Navigation and Orientation)

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25 pages, 23247 KiB

Open AccessEditor’s ChoiceArticle

Infrared and Visible Camera Integration for Detection and Tracking of Small UAVs: Systematic Evaluation

by Ana Pereira, Stephen Warwick, Alexandra Moutinho and Afzal Suleman

Drones 2024, 8(11), 650; https://doi.org/10.3390/drones8110650 - 6 Nov 2024

Cited by 2 | Viewed by 2795

Abstract

Given the recent proliferation of Unmanned Aerial Systems (UASs) and the consequent importance of counter-UASs, this project aims to perform the detection and tracking of small non-cooperative UASs using Electro-optical (EO) and Infrared (IR) sensors. Two data integration techniques, at the decision and [...] Read more.

Given the recent proliferation of Unmanned Aerial Systems (UASs) and the consequent importance of counter-UASs, this project aims to perform the detection and tracking of small non-cooperative UASs using Electro-optical (EO) and Infrared (IR) sensors. Two data integration techniques, at the decision and pixel levels, are compared with the use of each sensor independently to evaluate the system robustness in different operational conditions. The data are submitted to a YOLOv7 detector merged with a ByteTrack tracker. For training and validation, additional efforts are made towards creating datasets of spatially and temporally aligned EO and IR annotated Unmanned Aerial Vehicle (UAV) frames and videos. These consist of the acquisition of real data captured from a workstation on the ground, followed by image calibration, image alignment, the application of bias-removal techniques, and data augmentation methods to artificially create images. The performance of the detector across datasets shows an average precision of 88.4%, recall of 85.4%, and mAP@0.5 of 88.5%. Tests conducted on the decision-level fusion architecture demonstrate notable gains in recall and precision, although at the expense of lower frame rates. Precision, recall, and frame rate are not improved by the pixel-level fusion design. Full article

(This article belongs to the Special Issue Intelligent Image Processing and Sensing for Drones, 2nd Edition)

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27 pages, 4611 KiB

Open AccessEditor’s ChoiceReview

Vision-Based Drone Detection in Complex Environments: A Survey

by Ziyi Liu, Pei An, You Yang, Shaohua Qiu, Qiong Liu and Xinghua Xu

Drones 2024, 8(11), 643; https://doi.org/10.3390/drones8110643 - 5 Nov 2024

Cited by 4 | Viewed by 6004

Abstract

The frequent illegal use of drones poses a serious threat to public security and property. Counter-drones are crucial tools. The prerequisite for an effective counter-drone is to detect drones accurately. With the rapid advancements in computer vision, vision-based drone detection methods have emerged [...] Read more.

The frequent illegal use of drones poses a serious threat to public security and property. Counter-drones are crucial tools. The prerequisite for an effective counter-drone is to detect drones accurately. With the rapid advancements in computer vision, vision-based drone detection methods have emerged as a hot topic of research. However, current reviews of vision-based drone detection are less focused on algorithmic summarization and analysis. For this reason, this survey aims to comprehensively review the latest methods for vision-based drone detection in complex environments, with the goal of providing a more thorough reference and guidance for related research. We first explore the imaging and drone characteristics in complex environments and summarize the main challenges of visual UAV detection. Then, we summarize the existing solutions for the main challenges. Finally, we systematically organize and introduce the commonly used datasets and evaluation metrics and conduct experiment comparisons based on the representative methods. We not only reveal the current development status of visual UAV detection but also analyze the deficiencies in current research. On this basis, we further look forward to future research directions and possible breakthroughs, with a view to providing useful insights for further research and development in related fields. Full article

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25 pages, 6840 KiB

Open AccessEditor’s ChoiceArticle

Air Route Design of Multi-Rotor UAVs for Urban Air Mobility

by Shan Li, Honghai Zhang, Zhuolun Li and Hao Liu

Drones 2024, 8(10), 601; https://doi.org/10.3390/drones8100601 - 18 Oct 2024

Cited by 1 | Viewed by 1869

Abstract

UAVs will present significant air traffic in the urban airspace in the future, which brings new challenges to urban air traffic management and control. This paper presents an air route design scheme for multi-rotor UAVs in urban airspace to enable UAV operations at [...] Read more.

UAVs will present significant air traffic in the urban airspace in the future, which brings new challenges to urban air traffic management and control. This paper presents an air route design scheme for multi-rotor UAVs in urban airspace to enable UAV operations at orderly levels. The air routes include legs and intersections, which are the three-dimensional channels of UAV flight. Based on the concept of structured and layered urban airspace, the cylindrical pipeline flight leg is designed, and the operation concept, characteristic parameters and flight procedures of along-road and roundabout intersections are proposed. By defining UAV conflict risk and intersection service level metrics, the operation situation of UAVs is quantitatively evaluated. Taking an urban transportation scenario as a case, the proposed route design scheme is simulated in different scale UAV operating scenarios. The results show that the number of UAVs at the intersection is positively correlated with the conflict probability, the number of crossing routes is negatively correlated with the intersection passing rate, and the UAV arrival rate is positively correlated with the intersection average passing time. The along-road type intersection is suitable for the area with fewer crossing routes and sparse UAVs, while the roundabout type intersection is adapted for the area with more crossing routes and dense UAVs. This research provides a new idea for urban UAV air route design, which is helpful in promoting the standardized management of UAVs and accelerating the integration of UAVs into urban airspace. Full article

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16 pages, 12318 KiB

Open AccessEditor’s ChoiceArticle

Digital Traffic Lights: UAS Collision Avoidance Strategy for Advanced Air Mobility Services

by Zachary McCorkendale, Logan McCorkendale, Mathias Feriew Kidane and Kamesh Namuduri

Drones 2024, 8(10), 590; https://doi.org/10.3390/drones8100590 - 17 Oct 2024

Cited by 3 | Viewed by 1879

Abstract

With the advancing development of Advanced Air Mobility (AAM), there is a collaborative effort to increase safety in the airspace. AAM is an advancing field of aviation that aims to contribute to the safe transportation of goods and people using aerial vehicles. When [...] Read more.

With the advancing development of Advanced Air Mobility (AAM), there is a collaborative effort to increase safety in the airspace. AAM is an advancing field of aviation that aims to contribute to the safe transportation of goods and people using aerial vehicles. When aerial vehicles are operating in high-density locations such as urban areas, it can become crucial to incorporate collision avoidance systems. Currently, there are available pilot advisory systems such as Traffic Collision and Avoidance Systems (TCAS) providing assistance to manned aircraft, although there are currently no collision avoidance systems for autonomous flights. Standards Organizations such as the Institute of Electrical and Electronics Engineers (IEEE), Radio Technical Commission for Aeronautics (RTCA), and General Aviation Manufacturers Association (GAMA) are working to develop cooperative autonomous flights using UAS-to-UAS Communication in structured and unstructured airspaces. This paper presents a new approach for collision avoidance strategies within structured airspace known as “digital traffic lights”. The digital traffic lights are deployed over an area of land, controlling all UAVs that enter a potential collision zone and providing specific directions to mitigate a collision in the airspace. This strategy is proven through the results demonstrated through simulation in a Cesium Environment. With the deployment of the system, collision avoidance can be achieved for autonomous flights in all airspaces. Full article

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

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22 pages, 125192 KiB

Open AccessEditor’s ChoiceArticle

Under-Canopy Drone 3D Surveys for Wild Fruit Hotspot Mapping

by Paweł Trybała, Luca Morelli, Fabio Remondino, Levi Farrand and Micael S. Couceiro

Drones 2024, 8(10), 577; https://doi.org/10.3390/drones8100577 - 12 Oct 2024

Cited by 4 | Viewed by 2286

Abstract

Advances in mobile robotics and AI have significantly expanded their application across various domains and challenging conditions. In the past, this has been limited to safe, controlled, and highly structured settings, where simplifying assumptions and conditions allowed for the effective resolution of perception-based [...] Read more.

Advances in mobile robotics and AI have significantly expanded their application across various domains and challenging conditions. In the past, this has been limited to safe, controlled, and highly structured settings, where simplifying assumptions and conditions allowed for the effective resolution of perception-based tasks. Today, however, robotics and AI are moving into the wild, where human–robot collaboration and robust operation are essential. One of the most demanding scenarios involves deploying autonomous drones in GNSS-denied environments, such as dense forests. Despite the challenges, the potential to exploit natural resources in these settings underscores the importance of developing technologies that can operate in such conditions. In this study, we present a methodology that addresses the unique challenges of natural forest environments by integrating positioning methods, leveraging cameras, LiDARs, GNSS, and vision AI with drone technology for under-canopy wild berry mapping. To ensure practical utility for fruit harvesters, we generate intuitive heat maps of berry locations and provide users with a mobile app that supports interactive map visualization, real-time positioning, and path planning assistance. Our approach, tested in a Scandinavian forest, refines the identification of high-yield wild fruit locations using V-SLAM, demonstrating the feasibility and effectiveness of autonomous drones in these demanding applications. Full article

(This article belongs to the Section Drones in Agriculture and Forestry)

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16 pages, 1699 KiB

Open AccessEditor’s ChoiceArticle

Characteristics Analysis and Modeling of Integrated Sensing and Communication Channel for Unmanned Aerial Vehicle Communications

by Xinru Li, Yu Liu, Xinrong Zhang, Yi Zhang, Jie Huang and Ji Bian

Drones 2024, 8(10), 538; https://doi.org/10.3390/drones8100538 - 1 Oct 2024

Cited by 1 | Viewed by 1870

Abstract

As an important part of 6th generation (6G) communication, integrated sensing and communication (ISAC) for unmanned aerial vehicle (UAV) communication has attracted more and more attention. The UAV ISAC channel model considering the space-time evolution of joint and shared clusters is the basis [...] Read more.

As an important part of 6th generation (6G) communication, integrated sensing and communication (ISAC) for unmanned aerial vehicle (UAV) communication has attracted more and more attention. The UAV ISAC channel model considering the space-time evolution of joint and shared clusters is the basis of UAV ISAC system design and network evaluation. This paper introduces the UAV ISAC channel characteristics analysis and modeling method. In the UAV ISAC network, the channel consists of a communication channel and a sensing channel. A joint channel parameter is a combination of all (communication and sensing) multiple path component (MPC) parameter sets, while a shared path is the intersection of the communication path and sensing path that have some of the same MPC parameters. Based on the data collected from a ray-tracing (RT) UAV-to-ground scenario, the joint paths and shared paths of ISAC channels are clustered. Then, by introducing the occurrence and disappearance of clusters based on the birth–death (B–D) process, the space-time evolution of different clusters is described, and the influence of the addition of sensing clusters and the change in flight altitude on the B–D process is explored. Finally, the effects of the sensing cluster and flight altitude on the UAV ISAC channel characteristics, including the angle, time–varying characteristics, and sharing degree (SD), are analyzed. The related UAV ISAC channel characteristics analysis can provide reference for the future development of UAV ISAC systems. Full article

(This article belongs to the Special Issue Technologies and Applications of UAV Channel Models in Communications and Spectrum Awareness)

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22 pages, 4864 KiB

Open AccessEditor’s ChoiceArticle

High-Altitude-UAV-Relayed Satellite D2D Communications for 6G IoT Network

by Jie Wang, Tao Hong, Fei Qi, Lei Liu and Xieyao He

Drones 2024, 8(10), 532; https://doi.org/10.3390/drones8100532 - 29 Sep 2024

Cited by 2 | Viewed by 2881

Abstract

High-altitude UAVs (HA-UAVs) have emerged as vital components in 6G communication infrastructures, providing stable relays for telecommunications services above terrestrial and aerial disturbances. This paper explores the multifaceted roles of HA-UAVs in remote sensing, data relay, and telecommunication network enhancement. A Large Language [...] Read more.

High-altitude UAVs (HA-UAVs) have emerged as vital components in 6G communication infrastructures, providing stable relays for telecommunications services above terrestrial and aerial disturbances. This paper explores the multifaceted roles of HA-UAVs in remote sensing, data relay, and telecommunication network enhancement. A Large Language Model (LLM) framework is introduced that dynamically predicts optimal HA-UAV connectivity for IoT devices, enhancing network performance and adaptability. The study emphasizes HA-UAVs’ operational efficiency, broad coverage, and potential to transform global communications, particularly in remote and underserved areas. Our proposed satellite-HA-UAV-IoT architecture with LLM optimization demonstrated substantial improvements, including a 25% increase in network throughput (from 20 Mbps to 25 Mbps at a 20 km distance), a 40% reduction in latency (from 25 ms to 15 ms), and a 28% enhancement in energy efficiency (from 0.25 μJ/bit to 0.18 μJ/bit), significantly advancing the performance and adaptability of next-generation IoT networks. These advancements pave the way for unprecedented connectivity and set the stage for future communication technologies. Full article

(This article belongs to the Special Issue Advances in IoT and Wireless Networks of UAVs: State of the Art, Achievements and Perspectives)

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24 pages, 10240 KiB

Open AccessEditor’s ChoiceReview

A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods

by Bingshu Wang, Qiang Li, Qianchen Mao, Jinbao Wang, C. L. Philip Chen, Aihong Shangguan and Haosu Zhang

Drones 2024, 8(9), 518; https://doi.org/10.3390/drones8090518 - 23 Sep 2024

Cited by 16 | Viewed by 6356

Abstract

The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key [...] Read more.

The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key challenges of existing anti-UAV and delve into various detection and tracking methods. It is noteworthy that our study emphasizes the shift toward deep learning to enhance detection accuracy and tracking performance. Secondly, the survey organizes some public datasets, provides effective links, and discusses the characteristics and limitations of each dataset. Next, by analyzing current research trends, we have identified key areas of innovation, including the progress of deep learning techniques in real-time detection and tracking, multi-sensor fusion systems, and the automatic switching mechanisms that adapt to different conditions. Finally, this survey discusses the limitations and future research directions. This paper aims to deepen the understanding of innovations in anti-UAV detection and tracking methods. Hopefully our work can offer a valuable resource for researchers and practitioners involved in anti-UAV research. Full article

(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)

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24 pages, 14165 KiB

Open AccessEditor’s ChoiceArticle

Real-Time Fire Detection: Integrating Lightweight Deep Learning Models on Drones with Edge Computing

by Md Fahim Shahoriar Titu, Mahir Afser Pavel, Goh Kah Ong Michael, Hisham Babar, Umama Aman and Riasat Khan

Drones 2024, 8(9), 483; https://doi.org/10.3390/drones8090483 - 13 Sep 2024

Cited by 22 | Viewed by 6566

Abstract

Fire accidents are life-threatening catastrophes leading to losses of life, financial damage, climate change, and ecological destruction. Promptly and efficiently detecting and extinguishing fires is essential to reduce the loss of lives and damage. This study uses drone, edge computing, and artificial intelligence [...] Read more.

Fire accidents are life-threatening catastrophes leading to losses of life, financial damage, climate change, and ecological destruction. Promptly and efficiently detecting and extinguishing fires is essential to reduce the loss of lives and damage. This study uses drone, edge computing, and artificial intelligence (AI) techniques, presenting novel methods for real-time fire detection. This proposed work utilizes a comprehensive dataset of 7187 fire images and advanced deep learning models, e.g., Detection Transformer (DETR), Detectron2, You Only Look Once YOLOv8, and Autodistill-based knowledge distillation techniques to improve the model performance. The knowledge distillation approach has been implemented with the YOLOv8m (medium) as the teacher (base) model. The distilled (student) frameworks are developed employing the YOLOv8n (Nano) and DETR techniques. The YOLOv8n attains the best performance with 95.21% detection accuracy and 0.985 F1 score. A powerful hardware setup, including a Raspberry Pi 5 microcontroller, Pi camera module 3, and a DJI F450 custom-built drone, has been constructed. The distilled YOLOv8n model has been deployed in the proposed hardware setup for real-time fire identification. The YOLOv8n model achieves 89.23% accuracy and an approximate frame rate of 8 for the conducted live experiments. Integrating deep learning techniques with drone and edge devices demonstrates the proposed system’s effectiveness and potential for practical applications in fire hazard mitigation. Full article

(This article belongs to the Special Issue Drones for Wildfire and Prescribed Fire Science)

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34 pages, 3899 KiB

Open AccessEditor’s ChoiceReview

Drone-Assisted Multimodal Logistics: Trends and Research Issues

by Kyunga Kim, Songi Kim, Junsu Kim and Hosang Jung

Drones 2024, 8(9), 468; https://doi.org/10.3390/drones8090468 - 8 Sep 2024

Cited by 1 | Viewed by 6293

Abstract

This study explores the evolving trends and research issues in the field of drone-assisted multimodal logistics over the past two decades. By employing various text-mining techniques on related research publications, we identify the most frequently investigated topics and research issues within this domain. [...] Read more.

This study explores the evolving trends and research issues in the field of drone-assisted multimodal logistics over the past two decades. By employing various text-mining techniques on related research publications, we identify the most frequently investigated topics and research issues within this domain. Specifically, we utilize titles, abstracts, and keywords from the collected studies to perform both Latent Dirichlet Allocation techniques and Term Frequency-Inverse Document Frequency analysis, which help in identifying latent topics and the core research themes within the field. Our analysis focuses on three primary categories of drone-assisted logistics: drone–truck, drone–ship, and drone–robot systems. The study aims to uncover which latent topics have been predominantly emphasized in each category and to highlight the distinct differences in research focuses among them. Our findings reveal specific trends and gaps in the existing literature, providing a clear roadmap for future research directions in drone-assisted multimodal logistics. This targeted analysis not only enhances our understanding of the current state of the field but also identifies critical areas that require further investigation to advance the application of drones in logistics. Full article

(This article belongs to the Special Issue Advances of Drones in Logistics)

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22 pages, 6891 KiB

Open AccessEditor’s ChoiceArticle

Transponder: Support for Localizing Distressed People through a Flying Drone Network

by Antonello Calabrò and Eda Marchetti

Drones 2024, 8(9), 465; https://doi.org/10.3390/drones8090465 - 6 Sep 2024

Cited by 2 | Viewed by 1641

Abstract

Context: In Search and Rescue (SAR) operations, the speed and techniques used by rescuers and effective communication with the person in need of rescue are vital for successful operations. Recently, drones have become an essential tool in SAR, used by both military and [...] Read more.

Context: In Search and Rescue (SAR) operations, the speed and techniques used by rescuers and effective communication with the person in need of rescue are vital for successful operations. Recently, drones have become an essential tool in SAR, used by both military and civilian organizations to locate and aid missing persons. Objective: The paper introduces Transponder, a Wi-Fi-based solution designed to enhance SAR efforts by tracking, localizing, and providing first aid information to distressed individuals, even in challenging environments such as forests, mountains, and urban areas lacking GSM/UMTS coverage or that are difficult to reach with terrestrial rescue. Methods: Provide an innovative mechanism based on Wi-Fi beacon detection, LoRa communication, and the possible mobile application to leverage the SAR operation. Provide the preliminary implementation of the Transponder and perform its assessment in scenarios with dense vegetation. Results: The Transponder functionalities have been proven to enhance and expedite the detection of missing persons. Additionally, responses to several research questions regarding its performance and effectiveness are provided. Conclusions: Transponder is an innovative detection mechanism that combines ground-based analysis with on-board analysis, optimizing energy consumption and realizing an efficient solution for real-world scenarios. Full article

(This article belongs to the Topic Recent Advances and Technologies in Emergency Response, Security and Disaster Management Applications)

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51 pages, 4761 KiB

Open AccessEditor’s ChoiceReview

Polar AUV Challenges and Applications: A Review

by Shuangshuang Fan, Neil Bose and Zeming Liang

Drones 2024, 8(8), 413; https://doi.org/10.3390/drones8080413 - 22 Aug 2024

Cited by 6 | Viewed by 6198

Abstract

This study presents a comprehensive review of the development and progression of autonomous underwater vehicles (AUVs) in polar regions, aiming to synthesize past experiences and provide guidance for future advancements and applications. We extensively explore the history of notable polar AUV deployments worldwide, [...] Read more.

This study presents a comprehensive review of the development and progression of autonomous underwater vehicles (AUVs) in polar regions, aiming to synthesize past experiences and provide guidance for future advancements and applications. We extensively explore the history of notable polar AUV deployments worldwide, identifying and addressing the key technological challenges these vehicles face. These include advanced navigation techniques, strategic path planning, efficient obstacle avoidance, robust communication, stable energy supply, reliable launch and recovery, and thorough risk analysis. Furthermore, this study categorizes the typical capabilities and applications of AUVs in polar contexts, such as under-ice mapping and measurement, water sampling, ecological investigation, seafloor mapping, and surveillance networking. We also briefly highlight existing research gaps and potential future challenges in this evolving field. Full article

(This article belongs to the Special Issue Advances in Autonomous Underwater Drones)

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24 pages, 1530 KiB

Open AccessEditor’s ChoiceArticle

DFly: A Publicly Auditable and Privacy-Preserving UAS Traffic Management System on Blockchain

by Frederico Baptista, Marina Dehez-Clementi and Jonathan Detchart

Drones 2024, 8(8), 410; https://doi.org/10.3390/drones8080410 - 21 Aug 2024

Cited by 3 | Viewed by 1692

Abstract

The integration of Unmanned Aircraft Systems (UASs) into the current airspace poses significant challenges in terms of safety, security, and operability. As an example, in 2019, the European Union defined a set of rules to support the digitalization of UAS traffic management (UTM) [...] Read more.

The integration of Unmanned Aircraft Systems (UASs) into the current airspace poses significant challenges in terms of safety, security, and operability. As an example, in 2019, the European Union defined a set of rules to support the digitalization of UAS traffic management (UTM) systems and services, namely the U-Space regulations. Current propositions opted for a centralized and private model, concentrated around governmental authorities (e.g., AlphaTango provides the Registration service and depends on the French government). In this paper, we advocate in favor of a more decentralized and transparent model in order to improve safety, security, operability among UTM stakeholders, and legal compliance. As such, we propose DFly, a publicly auditable and privacy-preserving UAS traffic management system on Blockchain, with two initial services: Registration and Flight Authorization. We demonstrate that the use of a blockchain guarantees the public auditability of the two services and corresponding service providers’ actions. In addition, it facilitates the comprehensive and distributed monitoring of airspace occupation and the integration of additional functionalities (e.g., the creation of a live UAS tracker). The combination with zero-knowledge proofs enables the deployment of an automated, distributed, transparent, and privacy-preserving Flight Authorization service, performed on-chain thanks to the blockchain logic. In addition to its construction, this paper details the instantiation of the proposed UTM system with the Ethereum Sepolia’s testnet and the Groth16 ZK-SNARK protocol. On-chain (gas cost) and off-chain (execution time) performance analyses confirm that the proposed solution is a viable and efficient alternative in the spirit of digitalization and offers additional security guarantees. Full article

(This article belongs to the Section Innovative Urban Mobility)

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25 pages, 11288 KiB

Open AccessEditor’s ChoiceArticle

Novel Twist Morphing Aileron and Winglet Design for UAS Control and Performance

by Mir Hossein Negahban, Musavir Bashir, Clovis Priolet and Ruxandra Mihaela Botez

Drones 2024, 8(8), 392; https://doi.org/10.3390/drones8080392 - 13 Aug 2024

Cited by 2 | Viewed by 2735

Abstract

This study introduces a novel “twist morphing aileron and winglet” design for the Unmanned Aircraft System UAS-S45. Improving rolling efficiency through twist morphing ailerons and reducing induced drag through twist morphing winglets are the two main objectives of this study. A novel wing [...] Read more.

This study introduces a novel “twist morphing aileron and winglet” design for the Unmanned Aircraft System UAS-S45. Improving rolling efficiency through twist morphing ailerons and reducing induced drag through twist morphing winglets are the two main objectives of this study. A novel wing design is introduced, and a high-fidelity gradient-based aerodynamic shape optimization is performed for twist morphing ailerons and twist morphing winglets, separately, with specified objective functions. The twist morphing aileron is then compared to the conventional hinged aileron configuration in terms of rolling efficiency and other aerodynamic properties, in particular aircraft maneuverability. The results for twist morphing ailerons show that the novel morphing design increases the aileron efficiency by 34% compared to the conventional design and reduces induced drag by 61%. Next, twist morphing winglets are studied regarding the induced drag in cruise and climb flight conditions. The results for twist morphing winglets indicate that the novel design reduces induced drag by 25.7% in cruise flight and up to 16.51% in climb; it also decreases the total drag by up to 7.5% and increases aerodynamic efficiency by up to 9%. Full article

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

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16 pages, 5933 KiB

Open AccessEditor’s ChoiceArticle

Wind Tunnel Investigation of the Icing of a Drone Rotor in Forward Flight

by Derek Harvey, Eric Villeneuve, Mathieu Béland and Maxime Lapalme

Drones 2024, 8(8), 380; https://doi.org/10.3390/drones8080380 - 7 Aug 2024

Cited by 2 | Viewed by 2301

Abstract

The Bell Textron APT70 is a UAV concept developed for last mile delivery and other usual applications. It performs vertical takeoff and transition into aircraft mode for forward flight. It includes four rotor each with four rotating blades. A test campaign has been [...] Read more.

The Bell Textron APT70 is a UAV concept developed for last mile delivery and other usual applications. It performs vertical takeoff and transition into aircraft mode for forward flight. It includes four rotor each with four rotating blades. A test campaign has been performed to study the effects of ice accretion on rotor performance through a parametric study of different parameters, namely MVD, LWC, rotor speed, and pitch angle. This paper presents the last experimentations of this campaign for the drone rotor operating in forward flight under simulated icing conditions in a refrigerated, closed-loop wind tunnel. Results demonstrated that the different parameters studied greatly impacted the collection efficiency of the blades and thus, the resulting ice accretion. Smaller droplets were more easily influenced by the streamlines around the rotating blades, resulting in less droplets impacting the surface and thus slower ice accumulations. Higher rotation speeds and pitch angles generated more energetic streamlines, which again transported more droplets around the airfoils instead of them impacting on the surface, which also led to slower accumulation. Slower ice accumulation resulted in slower thrust losses, since the loss in performances can be directly linked to the amount of ice accreted. This research has not only allowed the obtainment of very insightful results on the effect of each test parameter on the ice accumulation, but it has also conducted the development of a unique test bench for UAV propellers. The new circular test sections along with the new instrumentation installed in and around the tunnel will allow the laboratory to be able to generate icing on various type of UAV in forward flight under representative atmospheric conditions. Full article

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19 pages, 51994 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Impact of Clearing and Grazing on Fuel Management in a Mediterranean Oak Forest through Unmanned Aerial Vehicle Multispectral Data

by Luís Pádua, João P. Castro, José Castro, Joaquim J. Sousa and Marina Castro

Drones 2024, 8(8), 364; https://doi.org/10.3390/drones8080364 - 31 Jul 2024

Viewed by 1678

Abstract

Climate change has intensified the need for robust fire prevention strategies. Sustainable forest fuel management is crucial in mitigating the occurrence and rapid spread of forest fires. This study assessed the impact of vegetation clearing and/or grazing over a three-year period in the [...] Read more.

Climate change has intensified the need for robust fire prevention strategies. Sustainable forest fuel management is crucial in mitigating the occurrence and rapid spread of forest fires. This study assessed the impact of vegetation clearing and/or grazing over a three-year period in the herbaceous and shrub parts of a Mediterranean oak forest. Using high-resolution multispectral data from an unmanned aerial vehicle (UAV), four flight surveys were conducted from 2019 (pre- and post-clearing) to 2021. These data were used to evaluate different scenarios: combined vegetation clearing and grazing, the individual application of each method, and a control scenario that was neither cleared nor purposely grazed. The UAV data allowed for the detailed monitoring of vegetation dynamics, enabling the classification into arboreal, shrubs, herbaceous, and soil categories. Grazing pressure was estimated through GPS collars on the sheep flock. Additionally, a good correlation (r = 0.91) was observed between UAV-derived vegetation volume estimates and field measurements. These practices proved to be efficient in fuel management, with cleared and grazed areas showing a lower vegetation regrowth, followed by areas only subjected to vegetation clearing. On the other hand, areas not subjected to any of these treatments presented rapid vegetation growth. Full article

(This article belongs to the Section Drones in Agriculture and Forestry)

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25 pages, 1470 KiB

Open AccessEditor’s ChoiceReview

Advancement Challenges in UAV Swarm Formation Control: A Comprehensive Review

by Yajun Bu, Ye Yan and Yueneng Yang

Drones 2024, 8(7), 320; https://doi.org/10.3390/drones8070320 - 12 Jul 2024

Cited by 25 | Viewed by 11558

Abstract

This paper provides an in-depth analysis of the current research landscape in the field of UAV (Unmanned Aerial Vehicle) swarm formation control. This review examines both conventional control methods, including leader–follower, virtual structure, behavior-based, consensus-based, and artificial potential field, and advanced AI-based (Artificial [...] Read more.

This paper provides an in-depth analysis of the current research landscape in the field of UAV (Unmanned Aerial Vehicle) swarm formation control. This review examines both conventional control methods, including leader–follower, virtual structure, behavior-based, consensus-based, and artificial potential field, and advanced AI-based (Artificial Intelligence) methods, such as artificial neural networks and deep reinforcement learning. It highlights the distinct advantages and limitations of each approach, showcasing how conventional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization capabilities. This review underscores the critical need for innovative solutions and interdisciplinary approaches combining conventional and AI methods to overcome existing challenges and fully exploit the potential of UAV swarms in various applications. Full article

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20 pages, 1709 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Fed4UL: A Cloud–Edge–End Collaborative Federated Learning Framework for Addressing the Non-IID Data Issue in UAV Logistics

by Chong Zhang, Xiao Liu, Aiting Yao, Jun Bai, Chengzu Dong, Shantanu Pal and Frank Jiang

Drones 2024, 8(7), 312; https://doi.org/10.3390/drones8070312 - 10 Jul 2024

Cited by 3 | Viewed by 2761

Abstract

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the [...] Read more.

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the requirement for on-board processing power and minimises the data transfer time to enable real-time applications. Meanwhile, with federated learning (FL), UAVs can enhance their intelligent decision-making capabilities by learning from other UAVs without directly accessing their data. This facilitates rapid model iteration and improvement while safeguarding data privacy. However, in many UAV applications such as UAV logistics, different UAVs may perform different tasks and cover different areas, which can result in heterogeneous data and add to the problem of non-independent and identically distributed (Non-IID) data for model training. To address such a problem, we introduce a novel cloud–edge–end collaborative FL framework, which organises and combines local clients through clustering and aggregation. By employing the cosine similarity, we identified and integrated the most appropriate local model into the global model, which can effectively address the issue of Non-IID data in UAV logistics. The experimental results showed that our approach outperformed traditional FL algorithms on two real-world datasets, CIFAR-10 and MNIST. Full article

(This article belongs to the Special Issue Advances of Drones in Logistics)

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30 pages, 2317 KiB

Open AccessEditor’s ChoiceReview

Artificial Intelligence Applied to Drone Control: A State of the Art

by Daniel Caballero-Martin, Jose Manuel Lopez-Guede, Julian Estevez and Manuel Graña

Drones 2024, 8(7), 296; https://doi.org/10.3390/drones8070296 - 3 Jul 2024

Cited by 31 | Viewed by 34359

Abstract

The integration of Artificial Intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy [...] Read more.

The integration of Artificial Intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy between drones and AI has led to notable progress in the autonomy of drones, which have become capable of completing complex missions without direct human supervision. This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making. The paper provides detailed examples of the latest developments and applications. Ethical and regulatory challenges are also considered for the future evolution of this field of research, because drones with AI have the potential to greatly change our socioeconomic landscape. Full article

(This article belongs to the Special Issue Perception, Decision-Making and Control of Intelligent Unmanned System)

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17 pages, 1074 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Emerging Research Topics in Drone Healthcare Delivery

by Hamish A. Campbell, Vanya Bosiocic, Aliesha Hvala, Mark Brady, Mariana A. Campbell, Kade Skelton and Osmar J. Luiz

Drones 2024, 8(6), 258; https://doi.org/10.3390/drones8060258 - 12 Jun 2024

Cited by 6 | Viewed by 4842

Abstract

The application of drones to assist with healthcare delivery has grown rapidly over the last decade. This industry is supported by a growing research field, and we have undertaken a systematic review of the published literature. Web-based searches returned 290 relevant manuscripts published [...] Read more.

The application of drones to assist with healthcare delivery has grown rapidly over the last decade. This industry is supported by a growing research field, and we have undertaken a systematic review of the published literature. Web-based searches returned 290 relevant manuscripts published between 2010 and 2024. We applied Topic Modelling to this corpus of literature, which examines word association and connectedness within the research papers. The modelling identified two emerging research themes with little connection between them: those who used drones to deliver time-critical medical items and those who used drones to deliver non-time-critical medical items. The former was in response to medical emergencies, while the latter was for enhancing resilience in the healthcare supply chain. The topics within these research themes exhibited notable differences. The delivery of time-critical medical items theme comprised the topics of ‘Emergency Response’, ‘Defibrillator and Organ Delivery’, and ‘Search and Rescue’, whilst non-time-critical delivery researched the topics of ‘Supply Chain Optimisation’ and ‘Cost-Effectiveness’, ‘Overcoming Remoteness’, and ‘Pandemic Response’. Research on ‘Engineering and Design Considerations’ and ‘Ethical and Social Considerations’ cut across both research themes. We undertook further analysis to assess research topic alignment and identify knowledge gaps. We found that efforts are needed to establish a more standardised terminology for better alignment across the two emerging research themes. Future studies should focus on evaluating the impact of drone delivery on patient health using systematic methods. Additionally, exploring the economic viability of drone-based health services and addressing regulatory barriers are crucial for efficient and effective drone deployment in healthcare delivery systems. Full article

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18 pages, 7743 KiB

Open AccessEditor’s ChoiceArticle

Design of a UAV Trajectory Prediction System Based on Multi-Flight Modes

by Zhuoyong Shi, Jiandong Zhang, Guoqing Shi, Longmeng Ji, Dinghan Wang and Yong Wu

Drones 2024, 8(6), 255; https://doi.org/10.3390/drones8060255 - 10 Jun 2024

Cited by 8 | Viewed by 2478

Abstract

With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm [...] Read more.

With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm of UAV flight state recognition and trajectory prediction. Presenting an innovative approach focused on improving the precision of unmanned aerial vehicle (UAV) path forecasting via the identification of flight states, this study demonstrates its efficacy through the implementation of two prediction models. Firstly, UAV flight data acquisition was realized in this paper by the use of multi-sensors. Finally, two models for UAV trajectory prediction were designed based on machine learning methods and classical mathematical prediction methods, respectively, and the results before and after flight pattern recognition are compared. The experimental results show that the prediction error of the UAV trajectory prediction method based on multiple flight modes is smaller than the traditional trajectory prediction method in different flight stages. 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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23 pages, 9478 KiB

Open AccessEditor’s ChoiceArticle

Research on Multi-UAV Obstacle Avoidance with Optimal Consensus Control and Improved APF

by Pengfei Zhang, Yin He, Zhongliu Wang, Shujie Li and Qinyang Liang

Drones 2024, 8(6), 248; https://doi.org/10.3390/drones8060248 - 6 Jun 2024

Cited by 9 | Viewed by 2521

Abstract

To address collision challenges between multi-UAVs (unmanned aerial vehicles) during obstacle avoidance, a novel formation control method is proposed. Leveraging the concept of APF (artificial potential field), the proposed approach integrates UAV maneuver constraints with a consensus formation control algorithm, optimizing UAV velocities [...] Read more.

To address collision challenges between multi-UAVs (unmanned aerial vehicles) during obstacle avoidance, a novel formation control method is proposed. Leveraging the concept of APF (artificial potential field), the proposed approach integrates UAV maneuver constraints with a consensus formation control algorithm, optimizing UAV velocities through the particle swarm optimization (PSO) algorithm. The optimal consensus control algorithm is then employed to achieve the optimal convergence rate of the UAV formation. To mitigate the limitations of traditional APF, a collinear force deflection angle is introduced, along with an obstacle avoidance method aimed at preventing UAVs from being trapped in locally optimal solutions. Additionally, an obstacle avoidance algorithm based on virtual force fields between UAVs is designed. Comparative analysis against the basic algorithm demonstrates the effectiveness of the designed optimal consensus algorithm in improving formation convergence performance. Moreover, the improved APF resolves local optimal solution issues, enabling UAVs to effectively navigate around obstacles. Simulation results validate the efficacy of this method in achieving multi-UAV formation control while effectively avoiding obstacles. Full article

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19 pages, 6305 KiB

Open AccessEditor’s ChoiceArticle

Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV

by Xiang Liu, Weizhi Zhong, Xin Wang, Hongtao Duan, Zhenxiong Fan, Haowen Jin, Yang Huang and Zhipeng Lin

Drones 2024, 8(5), 199; https://doi.org/10.3390/drones8050199 - 15 May 2024

Cited by 6 | Viewed by 2964

Abstract

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the [...] Read more.

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the 3D space environment and integrating factors such as UAV mission completion time and connectivity, we develop an objective function for path optimization and utilize the advanced dueling double deep Q network (D3QN) to optimize it. Additionally, we introduce the prioritized experience replay (PER) mechanism to enhance learning efficiency and expedite convergence. In order to further aid in trajectory planning, our method incorporates a simultaneous navigation and radio mapping (SNARM) framework that generates simulated 3D radio maps and simulates flight processes by utilizing measurement signals from the UAV during flight, thereby reducing actual flight costs. The simulation results demonstrate that the proposed approach effectively enable UAVs to avoid weak coverage regions in space, thereby reducing the weighted sum of flight time and expected interruption time. Full article

(This article belongs to the Special Issue Technologies and Applications of UAV Channel Models in Communications and Spectrum Awareness)

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42 pages, 3596 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Strategies for Optimized UAV Surveillance in Various Tasks and Scenarios: A Review

by Zixuan Fang and Andrey V. Savkin

Drones 2024, 8(5), 193; https://doi.org/10.3390/drones8050193 - 12 May 2024

Cited by 30 | Viewed by 7152

Abstract

This review paper provides insights into optimization strategies for Unmanned Aerial Vehicles (UAVs) in a variety of surveillance tasks and scenarios. From basic path planning to complex mission execution, we comprehensively evaluate the multifaceted role of UAVs in critical areas such as infrastructure [...] Read more.

This review paper provides insights into optimization strategies for Unmanned Aerial Vehicles (UAVs) in a variety of surveillance tasks and scenarios. From basic path planning to complex mission execution, we comprehensively evaluate the multifaceted role of UAVs in critical areas such as infrastructure inspection, security surveillance, environmental monitoring, archaeological research, mining applications, etc. The paper analyzes in detail the effectiveness of UAVs in specific tasks, including power line and bridge inspections, search and rescue operations, police activities, and environmental monitoring. The focus is on the integration of advanced navigation algorithms and artificial intelligence technologies with UAV surveillance and the challenges of operating in complex environments. Looking ahead, this paper predicts trends in cooperative UAV surveillance networks and explores the potential of UAVs in more challenging scenarios. This review not only provides researchers with a comprehensive analysis of the current state of the art, but also highlights future research directions, aiming to engage and inspire readers to further explore the potential of UAVs in surveillance missions. Full article

(This article belongs to the Special Issue Conceptual Design, Modeling, and Control Strategies of Drones 3rd Edition)

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24 pages, 1497 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

by D S Nithya, Giuseppe Quaranta, Vincenzo Muscarello and Man Liang

Drones 2024, 8(4), 147; https://doi.org/10.3390/drones8040147 - 9 Apr 2024

Cited by 9 | Viewed by 5861

Abstract

Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low [...] Read more.

Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low altitudes where microscale turbulent wind flow conditions are prevalent. This introduces flight testing, certification, and operational complexities. To tackle these issues, the UAM industry, aviation authorities, and research communities across the world have provided prescriptive ways, such as the implementation of dynamic weather corridors for safe operation, classification of atmospheric disturbance levels for certification, etc., within the proposed concepts of operation (ConOps), certification standards, and guidelines. However, a notable hindrance to the efficacy of these solutions lies in the scarcity of operational UAM and observational wind data in urban environments. One way to address this deficiency in data is via microscale wind modelling, which has been long established in the context of studying atmospheric dynamics, weather forecasting, turbine blade load estimation, etc. Thus, this paper aims to provide a critical literature review of a variety of wind flow estimation and forecasting techniques that can be and have been utilized by the UAM community. Furthermore, a compare-and-contrast study of the commonly used wind flow models employed within the wind engineering and atmospheric science domain is furnished along with an overview of the urban wind flow conditions. Full article

(This article belongs to the Section Innovative Urban Mobility)

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18 pages, 1851 KiB

Open AccessEditor’s ChoiceArticle

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

by Dan Han, Hao Jiang, Lifang Wang, Xinyu Zhu, Yaqing Chen and Qizhou Yu

Drones 2024, 8(4), 138; https://doi.org/10.3390/drones8040138 - 3 Apr 2024

Cited by 16 | Viewed by 2754

Abstract

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique [...] Read more.

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations. Full article

(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems)

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17 pages, 4983 KiB

Open AccessEditor’s ChoiceArticle

HiFly-Dragon: A Dragonfly Inspired Flapping Flying Robot with Modified, Resonant, Direct-Driven Flapping Mechanisms

by He Ma, Peiyi Gong, Yuqiang Tian, Qingnan Wu, Min Pan, Hao Yin, Youjiang Liu and Chilai Chen

Drones 2024, 8(4), 126; https://doi.org/10.3390/drones8040126 - 28 Mar 2024

Cited by 7 | Viewed by 4665

Abstract

This paper describes a dragonfly-inspired Flapping Wing Micro Air Vehicle (FW-MAV), named HiFly-Dragon. Dragonflies exhibit exceptional flight performance in nature, surpassing most of the other insects, and benefit from their abilities to independently move each of their four wings, including adjusting the flapping [...] Read more.

This paper describes a dragonfly-inspired Flapping Wing Micro Air Vehicle (FW-MAV), named HiFly-Dragon. Dragonflies exhibit exceptional flight performance in nature, surpassing most of the other insects, and benefit from their abilities to independently move each of their four wings, including adjusting the flapping amplitude and the flapping amplitude offset. However, designing and fabricating a flapping robot with multi-degree-of-freedom (multi-DOF) flapping driving mechanisms under stringent size, weight, and power (SWaP) constraints poses a significant challenge. In this work, we propose a compact microrobot dragonfly with four tandem independently controllable wings, which is directly driven by four modified resonant flapping mechanisms integrated on the Printed Circuit Boards (PCBs) of the avionics. The proposed resonant flapping mechanism was tested to be able to enduringly generate 10 gf lift at a frequency of 28 Hz and an amplitude of 180° for a single wing with an external DC power supply, demonstrating the effectiveness of the resonance and durability improvement. All of the mechanical parts were integrated on two PCBs, and the robot demonstrates a substantial weight reduction. The latest prototype has a wingspan of 180 mm, a total mass of 32.97 g, and a total lift of 34 gf. The prototype achieved lifting off on a balance beam, demonstrating that the directly driven robot dragonfly is capable of overcoming self-gravity with onboard batteries. Full article

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34 pages, 3241 KiB

Open AccessEditor’s ChoiceReview

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

by Serhat Sönmez, Matthew J. Rutherford and Kimon P. Valavanis

Drones 2024, 8(4), 116; https://doi.org/10.3390/drones8040116 - 22 Mar 2024

Cited by 5 | Viewed by 3377

Abstract

Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying [...] Read more.

Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environments in which multirotors operate. Learning algorithms combined with model-based control techniques have proven beneficial when applied to multirotors. This survey summarizes the research published since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories and then further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey is on online learning algorithms as applied to multirotors, with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, how, and how fast. The outcome of the survey offers a clear understanding of the recent state of the art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real time. Full article

(This article belongs to the Special Issue Selected Papers from the 2023 International Conference on Unmanned Aircraft Systems (ICUAS 2023))

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25 pages, 3769 KiB

Open AccessEditor’s ChoiceArticle

Harmonized Skies: A Survey on Drone Acceptance across Europe

by Maria Stolz, Anne Papenfuß, Franziska Dunkel and Eva Linhuber

Drones 2024, 8(3), 107; https://doi.org/10.3390/drones8030107 - 20 Mar 2024

Cited by 6 | Viewed by 4065

Abstract

This study investigated the public acceptance of drones in six European countries. For this purpose, an online questionnaire was created, which was completed by 2998 participants. The general attitude towards drones, concerns, approval for different use cases, minimum tolerable flight altitude, acceptable flight [...] Read more.

This study investigated the public acceptance of drones in six European countries. For this purpose, an online questionnaire was created, which was completed by 2998 participants. The general attitude towards drones, concerns, approval for different use cases, minimum tolerable flight altitude, acceptable flight areas, and the impact of personal and demographic attributes on drone acceptance were analyzed. Overall, attitudes towards drones were quite positive in the entire sample and even improved slightly in a second measurement at the end of the questionnaire. However, the results also show that acceptance strongly depends on the use case. Drones for civil and public applications are more widely accepted than those for private and commercial applications. Moreover, the population still has high concerns about privacy and safety. Knowledge about drones, interest in technologies, and age proved essential to predicting acceptance. Thus, tailored communication strategies, for example, through social media, can enhance public awareness and acceptance. Full article

(This article belongs to the Collection Feature Papers of Drones Volume II)

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20 pages, 3022 KiB

Open AccessEditor’s ChoiceArticle

PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8

by Noor Ul Ain Tahir, Zhe Long, Zuping Zhang, Muhammad Asim and Mohammed ELAffendi

Drones 2024, 8(3), 84; https://doi.org/10.3390/drones8030084 - 28 Feb 2024

Cited by 62 | Viewed by 6866

Abstract

In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small [...] Read more.

In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions ( for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset. Full article

(This article belongs to the Special Issue Advances in Detection and Tracking Applications for Drones and UAM Systems)

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21 pages, 7284 KiB

Open AccessEditor’s ChoiceArticle

Dynamic Scene Path Planning of UAVs Based on Deep Reinforcement Learning

by Jin Tang, Yangang Liang and Kebo Li

Drones 2024, 8(2), 60; https://doi.org/10.3390/drones8020060 - 9 Feb 2024

Cited by 20 | Viewed by 5136

Abstract

Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning [...] Read more.

Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning in dynamic scenarios. Firstly, we establish a task scenario including an obstacle assessment model and model the UAV’s path planning problem using the Markov Decision Process. We translate the MDP model into the framework of reinforcement learning and design the state space, action space, and reward function while incorporating heuristic rules into the action exploration policy. Secondly, we utilize the Q function approximation of an enhanced D3QN with a prioritized experience replay mechanism and design the algorithm’s network structure based on the TensorFlow framework. Through extensive training, we obtain reinforcement learning path planning policies for both static and dynamic scenes and innovatively employ a visualized action field to analyze their planning effectiveness. Simulations demonstrate that the proposed algorithm can accomplish UAV dynamic scene path planning tasks and outperforms classical methods such as A*, RRT, and DQN in terms of planning effectiveness. Full article

(This article belongs to the Special Issue Advances in Quadrotor Unmanned Aerial Vehicles)

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