Drones

22 pages, 6891 KB

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 5 | Viewed by 4402

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 KB

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 44 | Viewed by 17581

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 KB

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 6 | Viewed by 3836

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 KB

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 11 | Viewed by 6660

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 KB

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 14 | Viewed by 5478

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 KB

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

Cited by 5 | Viewed by 3753

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 KB

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 119 | Viewed by 29117

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 KB

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 32 | Viewed by 5962

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 KB

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 99 | Viewed by 59359

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 KB

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 23 | Viewed by 9189

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 KB

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 22 | Viewed by 8304

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 KB

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 35 | Viewed by 5523

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 KB

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 31 | Viewed by 7277

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 KB

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 122 | Viewed by 17003

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 KB

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 42 | Viewed by 13136

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 KB

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 45 | Viewed by 5515

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 KB

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 19 | Viewed by 12505

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 KB

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 19 | Viewed by 8294

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 KB

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 16 | Viewed by 8492

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 KB

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 141 | Viewed by 10520

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 KB

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 66 | Viewed by 10776

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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17 pages, 1286 KB

Open AccessEditor’s ChoiceArticle

On the Dynamics of Flexible Wings for Designing a Flapping-Wing UAV

by Renan Cavenaghi Silva and Douglas D. Bueno

Drones 2024, 8(2), 56; https://doi.org/10.3390/drones8020056 - 7 Feb 2024

Cited by 8 | Viewed by 6569

Abstract

The increasing number of applications involving the use of UAVs has motivated the research for design considerations that increase the safety, endurance, range, and payload capability of these vehicles. In this article, the dynamics of a flexible flapping wing is investigated, focused on [...] Read more.

The increasing number of applications involving the use of UAVs has motivated the research for design considerations that increase the safety, endurance, range, and payload capability of these vehicles. In this article, the dynamics of a flexible flapping wing is investigated, focused on designing bio-inspired UAVs. A dynamic model of the Flapping-Wing UAV is proposed by using 2D beam elements defined in the absolute nodal coordinate formulation, and the flapping is imposed through constraint equations coupled to the equation of motion using Lagrange multipliers. The nodal coordinate trajectories are obtained by integrating the equation of motion using the Runge–Kutta algorithm. The imposed flapping is modulated using a proposed smooth function to reduce transient vibrations at the start of the motion. The results shows that wing flexibility yields significant differences compared to rigid-wing models, depending on the flapping frequency. Limited amplitude of oscillation is obtained when considering a non-resonant flapping strategy, whereas in resonance, the energy levels efficiently increase. The results also demonstrate the influence of different flapping strategies on the energy dissipation, which are relevant to increasing the time of flight. The proposed approach is an interesting alternative for designing flexible, bio-inspired, flapping-wing UAVs. Full article

(This article belongs to the Special Issue Conceptual Design, Modeling, and Control Strategies of Drones-II)

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15 pages, 9694 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Thermal Image Tracking for Search and Rescue Missions with a Drone

by Seokwon Yeom

Drones 2024, 8(2), 53; https://doi.org/10.3390/drones8020053 - 5 Feb 2024

Cited by 60 | Viewed by 23507

Abstract

Infrared thermal imaging is useful for human body recognition for search and rescue (SAR) missions. This paper discusses thermal object tracking for SAR missions with a drone. The entire process consists of object detection and multiple-target tracking. The You-Only-Look-Once (YOLO) detection model is [...] Read more.

Infrared thermal imaging is useful for human body recognition for search and rescue (SAR) missions. This paper discusses thermal object tracking for SAR missions with a drone. The entire process consists of object detection and multiple-target tracking. The You-Only-Look-Once (YOLO) detection model is utilized to detect people in thermal videos. Multiple-target tracking is performed via track initialization, maintenance, and termination. Position measurements in two consecutive frames initialize the track. Tracks are maintained using a Kalman filter. A bounding box gating rule is proposed for the measurement-to-track association. This proposed rule is combined with the statistically nearest neighbor association rule to assign measurements to tracks. The track-to-track association selects the fittest track for a track and fuses them. In the experiments, three videos of three hikers simulating being lost in the mountains were captured using a thermal imaging camera on a drone. Capturing was assumed under difficult conditions; the objects are close or occluded, and the drone flies arbitrarily in horizontal and vertical directions. Robust tracking results were obtained in terms of average total track life and average track purity, whereas the average mean track life was shortened in harsh searching environments. Full article

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

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29 pages, 1775 KB

Open AccessEditor’s ChoiceReview

Research on Unmanned Aerial Vehicle Path Planning

by Junhai Luo, Yuxin Tian and Zhiyan Wang

Drones 2024, 8(2), 51; https://doi.org/10.3390/drones8020051 - 4 Feb 2024

Cited by 91 | Viewed by 19075

Abstract

As the technology of unmanned aerial vehicles (UAVs) advances, these vehicles are increasingly being used in various industries. However, the navigation of UAVs often faces restrictions and obstacles, necessitating the implementation of path-planning algorithms to ensure safe and efficient flight. This paper presents [...] Read more.

As the technology of unmanned aerial vehicles (UAVs) advances, these vehicles are increasingly being used in various industries. However, the navigation of UAVs often faces restrictions and obstacles, necessitating the implementation of path-planning algorithms to ensure safe and efficient flight. This paper presents innovative path-planning algorithms designed explicitly for UAVs and categorizes them based on algorithmic and functional levels. Moreover, it comprehensively discusses the advantages, disadvantages, application challenges, and notable outcomes of each path-planning algorithm, aiming to examine their performance thoroughly. Additionally, this paper provides insights into future research directions for UAVs, intending to assist researchers in future explorations. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicle Path Planning: Challenges, Solutions, and Future Directions)

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23 pages, 10401 KB

Open AccessEditor’s ChoiceArticle

Adaptive AUV Mission Control System Tested in the Waters of Baffin Bay

by Jimin Hwang, Neil Bose, Gina Millar, Craig Bulger, Ginelle Nazareth and Xi Chen

Drones 2024, 8(2), 45; https://doi.org/10.3390/drones8020045 - 1 Feb 2024

Cited by 12 | Viewed by 6407

Abstract

The primary objectives of this paper are to test an adaptive sampling method for an autonomous underwater vehicle, specifically tailored to track a hydrocarbon plume in the water column. An overview of the simulation of the developed applications within the autonomous system is [...] Read more.

The primary objectives of this paper are to test an adaptive sampling method for an autonomous underwater vehicle, specifically tailored to track a hydrocarbon plume in the water column. An overview of the simulation of the developed applications within the autonomous system is presented together with the subsequent validation achieved through field trials in an area of natural oil seeps near to Scott Inlet in Baffin Bay. This builds upon our prior published work in methodological development. The method employed involves an integrated backseat drive of the AUV, which processes in situ sensor data in real time, assesses mission status, and determines the next task. The core of the developed system comprises three modular components—Search, Survey, and Sample—each designed for independent and sequential execution. Results from tests in Baffin Bay demonstrate that the backseat drive operating system successfully accomplished mission goals, recovering water samples at depths of 20 m, 50 m, and 200 m before mission completion and vehicle retrieval. The principal conclusion drawn from these trials underscores the system’s resilience in enhanced decision autonomy and validates its applicability to marine pollutant assessment and mitigation. Full article

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21 pages, 512 KB

Open AccessEditor’s ChoiceReview

Review of Aerial Transportation of Suspended-Cable Payloads with Quadrotors

by Julian Estevez, Gorka Garate, Jose Manuel Lopez-Guede and Mikel Larrea

Drones 2024, 8(2), 35; https://doi.org/10.3390/drones8020035 - 25 Jan 2024

Cited by 81 | Viewed by 13970

Abstract

Payload transportation and manipulation by rotorcraft drones are receiving a lot of attention from the military, industrial and logistics research areas. The interactions between the UAV and the payload, plus the means of object attachment or manipulation (such as cables or anthropomorphic robotic [...] Read more.

Payload transportation and manipulation by rotorcraft drones are receiving a lot of attention from the military, industrial and logistics research areas. The interactions between the UAV and the payload, plus the means of object attachment or manipulation (such as cables or anthropomorphic robotic arms), may be nonlinear, introducing difficulties in the overall system performance. In this paper, we focus on the current state of the art of aerial transportation systems with suspended loads by a single UAV and a team of them and present a review of different dynamic cable models and control systems. We cover the last sixteen years of the existing literature, and we add a discussion for evaluating the main trends in the referenced research works. Full article

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

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27 pages, 16496 KB

Open AccessEditor’s ChoiceReview

Unmanned Aerial Vehicles (UAVs) in Landslide Investigation and Monitoring: A Review

by Jianwei Sun, Guoqin Yuan, Laiyun Song and Hongwen Zhang

Drones 2024, 8(1), 30; https://doi.org/10.3390/drones8010030 - 22 Jan 2024

Cited by 128 | Viewed by 31135

Abstract

Over the past decade, Unmanned Aerial Vehicles (UAVs) have emerged as essential tools for landslide studies, particularly in on-site investigations. This paper reviews UAV applications in landslide studies, with a focus on static geological characteristics, monitoring temporal and spatial dynamics, and responses post-events. [...] Read more.

Over the past decade, Unmanned Aerial Vehicles (UAVs) have emerged as essential tools for landslide studies, particularly in on-site investigations. This paper reviews UAV applications in landslide studies, with a focus on static geological characteristics, monitoring temporal and spatial dynamics, and responses post-events. We discuss the functions and limitations of various types of UAVs and sensors (RGB cameras, multi-spectral cameras, thermal IR cameras, SAR, LiDAR), outlining their roles and data processing methods in landslide applications. This review focuses on the UAVs’ roles in landslide geology surveys, emphasizing landslide mapping, modeling and characterization. For change monitoring, it provides an overview of the temporal and spatial evolution through UAV-based monitoring, shedding light on dynamic landslide processes. Moreover, this paper underscores UAVs’ crucial role in emergent response scenarios, detailing strategies and automated detection using machine learning algorithms. The discussion on challenges and opportunities highlights the need for ongoing UAV technology advancements, addressing regulatory hurdles, hover time limitations, 3D reconstruction accuracy and potential integration with technologies like UAV swarms. Full article

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

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34 pages, 7532 KB

Open AccessEditor’s ChoiceArticle

Rockfall Analysis from UAV-Based Photogrammetry and 3D Models of a Cliff Area

by Daniele Cirillo, Michelangelo Zappa, Anna Chiara Tangari, Francesco Brozzetti and Fabio Ietto

Drones 2024, 8(1), 31; https://doi.org/10.3390/drones8010031 - 22 Jan 2024

Cited by 63 | Viewed by 13055

Abstract

The application of Unmanned Aerial Vehicles (UAVs), commonly known as drones, in geological, geomorphological, and geotechnical studies has gained significant attention due to their versatility and capability to capture high-resolution data from challenging terrains. This research uses drone-based high-resolution photogrammetry to assess the [...] Read more.

The application of Unmanned Aerial Vehicles (UAVs), commonly known as drones, in geological, geomorphological, and geotechnical studies has gained significant attention due to their versatility and capability to capture high-resolution data from challenging terrains. This research uses drone-based high-resolution photogrammetry to assess the geomechanical properties and rockfall potential of several rock scarps within a wide area of 50 ha. Traditional methods for evaluating geomechanical parameters on rock scarps involve time-consuming field surveys and measurements, which can be hazardous in steep and rugged environments. By contrast, drone photogrammetry offers a safer and more efficient approach, allowing for the creation of detailed 3D models of a cliff area. These models provide valuable insights into the topography, geological structures, and potential failure mechanisms. This research processed the acquired drone imagery using advanced geospatial software to generate accurate orthophotos and digital elevation models. These outputs analysed the key factors contributing to rockfall triggering, including identifying discontinuities, joint orientations, kinematic analysis of failures, and fracturing frequency. More than 8.9 × 10⁷ facets, representing discontinuity planes, were recognised and analysed for the kinematic failure modes, showing that direct toppling is the most abundant rockfall type, followed by planar sliding and flexural toppling. Three different fracturation grades were also identified based on the number of planar facets recognised on rock surfaces. The approach used in this research contributes to the ongoing development of fast, practical, low-cost, and non-invasive techniques for geomechanical assessment on vertical rock scarps. In particular, the results show the effectiveness of drone-based photogrammetry for rapidly collecting comprehensive geomechanical data valid to recognise the prone areas to rockfalls in vast regions. Full article

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36 pages, 2723 KB

Open AccessFeature PaperEditor’s ChoiceReview

Multi-Robot Coverage Path Planning for the Inspection of Offshore Wind Farms: A Review

by Ashley J. I. Foster, Mario Gianni, Amir Aly, Hooman Samani and Sanjay Sharma

Drones 2024, 8(1), 10; https://doi.org/10.3390/drones8010010 - 31 Dec 2023

Cited by 14 | Viewed by 7882

Abstract

Offshore wind turbine (OWT) inspection research is receiving increasing interest as the sector grows worldwide. Wind farms are far from emergency services and experience extreme weather and winds. This hazardous environment lends itself to unmanned approaches, reducing human exposure to risk. Increasing automation [...] Read more.

Offshore wind turbine (OWT) inspection research is receiving increasing interest as the sector grows worldwide. Wind farms are far from emergency services and experience extreme weather and winds. This hazardous environment lends itself to unmanned approaches, reducing human exposure to risk. Increasing automation in inspections can reduce human effort and financial costs. Despite the benefits, research on automating inspection is sparse. This work proposes that OWT inspection can be described as a multi-robot coverage path planning problem. Reviews of multi-robot coverage exist, but to the best of our knowledge, none captures the domain-specific aspects of an OWT inspection. In this paper, we present a review on the current state of the art of multi-robot coverage to identify gaps in research relating to coverage for OWT inspection. To perform a qualitative study, the PICo (population, intervention, and context) framework was used. The retrieved works are analysed according to three aspects of coverage approaches: environmental modelling, decision making, and coordination. Based on the reviewed studies and the conducted analysis, candidate approaches are proposed for the structural coverage of an OWT. Future research should involve the adaptation of voxel-based ray-tracing pose generation to UAVs and exploration, applying semantic labels to tasks to facilitate heterogeneous coverage and semantic online task decomposition to identify the coverage target during the run time. Full article

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14 pages, 4004 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Exploring Meteorological Conditions and Microscale Temperature Inversions above the Great Barrier Reef through Drone-Based Measurements

by Christian Eckert, Kim I. Monteforte, Daniel P. Harrison and Brendan P. Kelaher

Drones 2023, 7(12), 695; https://doi.org/10.3390/drones7120695 - 4 Dec 2023

Cited by 8 | Viewed by 5048

Abstract

Understanding the atmospheric conditions in remote areas contributes to assessing local weather phenomena. Obtaining vertical profiles of the atmosphere in isolated locations can introduce significant challenges for the deployment and maintenance of equipment, as well as regulatory obstacles. Here, we assessed the potential [...] Read more.

Understanding the atmospheric conditions in remote areas contributes to assessing local weather phenomena. Obtaining vertical profiles of the atmosphere in isolated locations can introduce significant challenges for the deployment and maintenance of equipment, as well as regulatory obstacles. Here, we assessed the potential of consumer drones equipped with lightweight atmospheric sensors to collect vertical meteorological profiles off One Tree Island (Great Barrier Reef), located approximately 85 km off the east coast of Australia. We used a DJI Matrice 300 drone with two InterMet Systems iMet-XQ2 UAV sensors, capturing data on atmospheric pressure, temperature, relative humidity, and wind up to an altitude of 1500 m. These flights were conducted three times per day (9 a.m., 12 noon, and 3 p.m.) and compared against ground-based weather sensors. Over the Austral summer/autumn, we completed 72 flights, obtaining 24 complete sets of daily measurements of atmospheric characteristics over the entire vertical profile. On average, the atmospheric temperature and dewpoint temperature were significantly influenced by the time of sampling, and also varied among days. The mean daily temperature and dewpoint temperature reached their peaks at 3 p.m., with the temperature gradually rising from its morning low. The mean dewpoint temperature obtained its lowest point around noon. We also observed wind speed variations, but changes in patterns throughout the day were much less consistent. The drone-mounted atmospheric sensors exhibited a consistent warm bias in temperature compared to the reference weather station. Relative humidity showed greater variability with no clear bias pattern, indicating potential limitations in the humidity sensor’s performance. Microscale temperature inversions were prevalent around 1000 m, peaking around noon and present in approximately 27% of the profiles. Overall, the drone-based vertical profiles helped characterise atmospheric dynamics around One Tree Island Reef and demonstrated the utility of consumer drones in providing cost-effective meteorological information in remote, environmentally sensitive areas. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicles in Atmospheric Research)

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17 pages, 702 KB

Open AccessEditor’s ChoiceReview

Challenges for the Routine Application of Drones in Healthcare: A Scoping Review

by Sara De Silvestri, Pasquale Junior Capasso, Alessandra Gargiulo, Sara Molinari and Alberto Sanna

Drones 2023, 7(12), 685; https://doi.org/10.3390/drones7120685 - 21 Nov 2023

Cited by 31 | Viewed by 17302

Abstract

Uncrewed aerial vehicles (UAVs), commonly known as drones, have emerged as transformative tools in the healthcare sector, offering the potential to revolutionize medical logistics, emergency response, and patient care. This scoping review provides a comprehensive exploration of the diverse applications of drones in [...] Read more.

Uncrewed aerial vehicles (UAVs), commonly known as drones, have emerged as transformative tools in the healthcare sector, offering the potential to revolutionize medical logistics, emergency response, and patient care. This scoping review provides a comprehensive exploration of the diverse applications of drones in healthcare, addressing critical gaps in existing literature. While previous reviews have primarily focused on specific facets of drone technology within the medical field, this study offers a holistic perspective, encompassing a wide range of potential healthcare applications. The review categorizes and analyzes the literature according to key domains, including the transport of biomedical goods, automated external defibrillator (AED) delivery, healthcare logistics, air ambulance services, and various other medical applications. It also examines public acceptance and the regulatory framework surrounding medical drone services. Despite advancements, critical knowledge gaps persist, particularly in understanding the intricate interplay between technological challenges, the existing regulatory framework, and societal acceptance. This review highlights the need for the extensive validation of cost-effective business cases, the development of control techniques that can address time and resource savings within the constraints of real-life scenarios, the design of crash-protected containers, and the establishment of corresponding tests and standards to demonstrate their conformity. Full article

(This article belongs to the Special Issue Drones: Opportunities and Challenges)

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18 pages, 2479 KB

Open AccessEditor’s ChoiceArticle

Implementation of an Edge-Computing Vision System on Reduced-Board Computers Embedded in UAVs for Intelligent Traffic Management

by Sergio Bemposta Rosende, Sergio Ghisler, Javier Fernández-Andrés and Javier Sánchez-Soriano

Drones 2023, 7(11), 682; https://doi.org/10.3390/drones7110682 - 20 Nov 2023

Cited by 39 | Viewed by 6812

Abstract

Advancements in autonomous driving have seen unprecedented improvement in recent years. This work addresses the challenge of enhancing the navigation of autonomous vehicles in complex urban environments such as intersections and roundabouts through the integration of computer vision and unmanned aerial vehicles (UAVs). [...] Read more.

Advancements in autonomous driving have seen unprecedented improvement in recent years. This work addresses the challenge of enhancing the navigation of autonomous vehicles in complex urban environments such as intersections and roundabouts through the integration of computer vision and unmanned aerial vehicles (UAVs). UAVs, owing to their aerial perspective, offer a more effective means of detecting vehicles involved in these maneuvers. The primary objective is to develop, evaluate, and compare different computer vision models and reduced-board (and small-power) hardware for optimizing traffic management in these scenarios. A dataset was constructed using two sources, several models (YOLO 5 and 8, DETR, and EfficientDetLite) were selected and trained, four reduced-board computers were chosen (Raspberry Pi 3B+ and 4, Jetson Nano, and Google Coral), and the models were tested on these boards for edge computing in UAVs. The experiments considered training times (with the dataset and its optimized version), model metrics were obtained, inference frames per second (FPS) were measured, and energy consumption was quantified. After the experiments, it was observed that the combination that best suits our use case is the YoloV8 model with the Jetson Nano. On the other hand, a combination with much higher inference speed but lower accuracy involves the EfficientDetLite models with the Google Coral board. Full article

(This article belongs to the Special Issue Edge Computing and IoT Technologies for Drones)

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13 pages, 2415 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Drone with Mounted Thermal Infrared Cameras for Monitoring Terrestrial Mammals

by Hanne Lyngholm Larsen, Katrine Møller-Lassesen, Esther Magdalene Ellersgaard Enevoldsen, Sarah Bøgh Madsen, Maria Trier Obsen, Peter Povlsen, Dan Bruhn, Cino Pertoldi and Sussie Pagh

Drones 2023, 7(11), 680; https://doi.org/10.3390/drones7110680 - 18 Nov 2023

Cited by 31 | Viewed by 11453

Abstract

This study investigates the use of a drone equipped with a thermal camera for recognizing wild mammal species in open areas and to determine the sex and age of red deer (Cervus elaphus) and roe deer (Capreolus capreoulus) in [...] Read more.

This study investigates the use of a drone equipped with a thermal camera for recognizing wild mammal species in open areas and to determine the sex and age of red deer (Cervus elaphus) and roe deer (Capreolus capreoulus) in a 13 km² moor in Denmark. Two separate surveys were conducted: (1) To achieve drone images for the identification of mammals, the drone was tested around a bait place with a live wildlife camera that was often visited by European badger (Meles meles), stone marten (Martes foina), European hare (Lepus europaeus), roe deer and cattle (Bos taurus). The thermal images of wild animal species could be distinguished by their body measures when the drone filmed with the camera pointed perpendicular to the ground in an altitude range of 50–120 m. A PCA ordination showed nonoverlapping body characteristics and MANOVA showed that the combined body measures used were significantly distinctive F = 6.8, p < 0.001. The reactions and behavioral responses of the different species to the altitude and noise of the drone were also tested in this place. (2) On a 13 km² moor, a drone was used for a population study of deer. Red deer and roe deer were counted and separated by body measures. Red deer individuals could, at the right altitude, be separated into adults and calves, and males and females. Body length was the most conclusive body measure, and therefore a reference measurement in the field is recommended. The frame thermal images were effective in species recognition and for use in population studies of deer, and are thought to be more time-efficient and less invasive than traditional methods. In autumn, the number of stags and the life stage of red deer, as well as the distribution of deer in different types of vegetation, could be determined. Full article

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

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14 pages, 4121 KB

Open AccessEditor’s ChoiceArticle

QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS

by Dipraj Debnath, Ahmad Faizul Hawary, Muhammad Iftishah Ramdan, Fernando Vanegas Alvarez and Felipe Gonzalez

Drones 2023, 7(11), 678; https://doi.org/10.3390/drones7110678 - 17 Nov 2023

Cited by 16 | Viewed by 5994

Abstract

Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed [...] Read more.

Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed to function as a pre-planned onboard navigation system for UAS flying in a known obstacle-cluttered environment. Our method uses a geometrical approach and a predefined safe perimeter (square area) based on Euclidean Geometry for the estimation of intercepting points, as a simple and efficient way to detect obstacles. The square region is treated as the restricted zone that the UAS must avoid entering, therefore providing a perimeter for manoeuvring and arriving at the next waypoints. The proposed algorithm is developed in a MATLAB environment and can be easily translated into other programming languages. The proposed algorithm is tested in scenarios with increasing levels of complexity, demonstrating that the QuickNav algorithm is able to successfully and efficiently generate a series of avoiding waypoints. Furthermore, QuickNav produces shorter distances as compared to those of the brute force method and is able to solve difficult obstacle avoidance problems in fractions of the time and distance required by the other methods. QuickNav can be used to improve the safety and efficiency of UAV missions and can be applied to the deployment of UAVs for surveillance, search and rescue, and delivery operations. Full article

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21 pages, 3479 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Burrow-Nesting Seabird Survey Using UAV-Mounted Thermal Sensor and Count Automation

by Jacob Virtue, Darren Turner, Guy Williams, Stephanie Zeliadt, Henry Walshaw and Arko Lucieer

Drones 2023, 7(11), 674; https://doi.org/10.3390/drones7110674 - 13 Nov 2023

Cited by 9 | Viewed by 6813

Abstract

Seabird surveys are used to monitor population demography and distribution and help us understand anthropogenic pressures on seabird species. Burrow-nesting seabirds are difficult to survey. Current ground survey methods are invasive, time-consuming and detrimental to colony health. Data derived from short transects used [...] Read more.

Seabird surveys are used to monitor population demography and distribution and help us understand anthropogenic pressures on seabird species. Burrow-nesting seabirds are difficult to survey. Current ground survey methods are invasive, time-consuming and detrimental to colony health. Data derived from short transects used in ground surveys are extrapolated to derive whole-colony population estimates, which introduces sampling bias due to factors including uneven burrow distribution and varying terrain. We investigate a new survey technique for nocturnally active burrow-nesting seabirds using unoccupied aerial vehicles (UAVs) and thermal sensor technology. We surveyed a three-hectare short-tailed shearwater (Ardenna tenuirostris) colony in Tasmania, Australia. Occupied burrows with resident chicks produced pronounced thermal signatures. This survey method captured a thermal response of every occupied burrow in the colony. Count automation techniques were developed to detect occupied burrows. To validate the results, we compared automated and manual counts of thermal imagery. Automated counts of occupied burrows were 9.3% higher and took approximately 5% of the time needed for manual counts. Using both manual and automated counts, we estimated that there were 5249–5787 chicks for the 2021/2022 breeding season. We provide evidence that high-resolution UAV thermal remote sensing and count automation can improve population estimates of burrow-nesting seabirds. Full article

(This article belongs to the Special Issue Drone Advances in Wildlife Research)

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21 pages, 775 KB

Open AccessEditor’s ChoiceReview

Unmanned Aerial Vehicles (UAVs) in Marine Mammal Research: A Review of Current Applications and Challenges

by Miguel Álvarez-González, Paula Suarez-Bregua, Graham J. Pierce and Camilo Saavedra

Drones 2023, 7(11), 667; https://doi.org/10.3390/drones7110667 - 9 Nov 2023

Cited by 49 | Viewed by 13067

Abstract

Research on the ecology and biology of marine mammal populations is necessary to understand ecosystem dynamics and to support conservation management. Emerging monitoring tools and instruments offer the opportunity to obtain such information in an affordable and effective way. In recent years, unmanned [...] Read more.

Research on the ecology and biology of marine mammal populations is necessary to understand ecosystem dynamics and to support conservation management. Emerging monitoring tools and instruments offer the opportunity to obtain such information in an affordable and effective way. In recent years, unmanned aerial vehicles (UAVs) have become an important tool in the study of marine mammals. Here, we reviewed 169 research articles using UAVs to study marine mammals, published up until December 2022. The goals of these studies included estimating the number of individuals in populations and groups via photo-identification, determining biometrics and body condition through photogrammetry, collecting blow samples, and studying behavioural patterns. UAVs can be a valuable, non-invasive, and useful tool for a wide range of applications in marine mammal research. However, it is important to consider some limitations of this technology, mainly associated with autonomy, resistance to the marine environment, and data processing time, which could probably be overcome in the near future. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation)

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17 pages, 6989 KB

Open AccessEditor’s ChoiceArticle

UAV-Based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring

by Aris Thomasberger and Mette Møller Nielsen

Drones 2023, 7(11), 647; https://doi.org/10.3390/drones7110647 - 25 Oct 2023

Cited by 10 | Viewed by 4773

Abstract

Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, [...] Read more.

Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, as well as for the identification of ecologically relevant information such as the vegetation depth limit. To address these limitations, a payload system was developed to collect subsurface data in the form of videos and depth measurements. In a 7 ha large study area, 136 point observations were collected and subsequently used to (1) train and validate the object-based classification of aerial imagery, (2) create a class distribution map based on the interpolation of point observations, (3) identify additional ecological relevant information and (4) create a bathymetry map of the study area. The classification based on ground-truthing samples achieved an overall accuracy of 98% and agreed to 84% with the class distribution map based on point interpolation. Additional ecologically relevant information, such as the vegetation depth limit, was recorded, and a bathymetry map of the study site was created. The findings of this study show that UAV-based shallow-water monitoring can be improved by applying the proposed tool. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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42 pages, 8000 KB

Open AccessEditor’s ChoiceReview

Eyes in the Sky: Drones Applications in the Built Environment under Climate Change Challenges

by Norhan Bayomi and John E. Fernandez

Drones 2023, 7(10), 637; https://doi.org/10.3390/drones7100637 - 16 Oct 2023

Cited by 126 | Viewed by 17458

Abstract

This paper reviews the diverse applications of drone technologies in the built environment and their role in climate change research. Drones, or unmanned aerial vehicles (UAVs), have emerged as valuable tools for environmental scientists, offering new possibilities for data collection, monitoring, and analysis [...] Read more.

This paper reviews the diverse applications of drone technologies in the built environment and their role in climate change research. Drones, or unmanned aerial vehicles (UAVs), have emerged as valuable tools for environmental scientists, offering new possibilities for data collection, monitoring, and analysis in the urban environment. The paper begins by providing an overview of the different types of drones used in the built environment, including quadcopters, fixed-wing drones, and hybrid models. It explores their capabilities and features, such as high-resolution cameras, LiDAR sensors, and thermal imaging, which enable detailed data acquisition for studying climate change impacts in urban areas. The paper then examines the specific applications of drones in the built environment and their contribution to climate change research. These applications include mapping urban heat islands, assessing the energy efficiency of buildings, monitoring air quality, and identifying sources of greenhouse gas emissions. UAVs enable researchers to collect spatially and temporally rich data, allowing for a detailed analysis and identifying trends and patterns. Furthermore, the paper discusses integrating UAVs with artificial intelligence (AI) to derive insights and develop predictive models for climate change mitigation and adaptation in urban environments. Finally, the paper addresses drone technologies’ challenges and the future directions in the built environment. These challenges encompass regulatory frameworks, privacy concerns, data management, and the need for an interdisciplinary collaboration. By harnessing the potential of drones, environmental scientists can enhance their understanding of climate change impacts in urban areas and contribute to developing sustainable strategies for resilient cities. Full article

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18 pages, 6362 KB

Open AccessEditor’s ChoiceArticle

Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study

by Tej Bahadur Shahi, Sweekar Dahal, Chiranjibi Sitaula, Arjun Neupane and William Guo

Drones 2023, 7(10), 624; https://doi.org/10.3390/drones7100624 - 7 Oct 2023

Cited by 63 | Viewed by 13858

Abstract

Semantic segmentation has been widely used in precision agriculture, such as weed detection, which is pivotal to increasing crop yields. Various well-established and swiftly evolved AI models have been developed of late for semantic segmentation in weed detection; nevertheless, there is insufficient information [...] Read more.

Semantic segmentation has been widely used in precision agriculture, such as weed detection, which is pivotal to increasing crop yields. Various well-established and swiftly evolved AI models have been developed of late for semantic segmentation in weed detection; nevertheless, there is insufficient information about their comparative study for optimal model selection in terms of performance in this field. Identifying such a model helps the agricultural community make the best use of technology. As such, we perform a comparative study of cutting-edge AI deep learning-based segmentation models for weed detection using an RGB image dataset acquired with UAV, called CoFly-WeedDB. For this, we leverage AI segmentation models, ranging from SegNet to DeepLabV3+, combined with five backbone convolutional neural networks (VGG16, ResNet50, DenseNet121, EfficientNetB0 and MobileNetV2). The results show that UNet with EfficientNetB0 as a backbone CNN is the best-performing model compared with the other candidate models used in this study on the CoFly-WeedDB dataset, imparting Precision (88.20%), Recall (88.97%), F1-score (88.24%) and mean Intersection of Union (56.21%). From this study, we suppose that the UNet model combined with EfficientNetB0 could potentially be used by the concerned stakeholders (e.g., farmers, the agricultural industry) to detect weeds more accurately in the field, thereby removing them at the earliest point and increasing crop yields. Full article

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

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29 pages, 20761 KB

Open AccessEditor’s ChoiceReview

Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review

by Zhen Cao, Lammert Kooistra, Wensheng Wang, Leifeng Guo and João Valente

Drones 2023, 7(10), 620; https://doi.org/10.3390/drones7100620 - 3 Oct 2023

Cited by 114 | Viewed by 21507

Abstract

Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection [...] Read more.

Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection in different fields has become increasingly important. However, since real-time UAV object detection is a comprehensive task involving hardware, algorithms, and other components, the complete implementation of real-time object detection is often overlooked. Although there is a large amount of literature on real-time object detection based on UAV remote sensing, little attention has been given to its workflow. This paper aims to systematically review previous studies about UAV real-time object detection from application scenarios, hardware selection, real-time detection paradigms, detection algorithms and their optimization technologies, and evaluation metrics. Through visual and narrative analyses, the conclusions cover all proposed research questions. Real-time object detection is more in demand in scenarios such as emergency rescue and precision agriculture. Multi-rotor UAVs and RGB images are of more interest in applications, and real-time detection mainly uses edge computing with documented processing strategies. GPU-based edge computing platforms are widely used, and deep learning algorithms is preferred for real-time detection. Meanwhile, optimization algorithms need to be focused on resource-limited computing platform deployment, such as lightweight convolutional layers, etc. In addition to accuracy, speed, latency, and energy are equally important evaluation metrics. Finally, this paper thoroughly discusses the challenges of sensor-, edge computing-, and algorithm-related lightweight technologies in real-time object detection. It also discusses the prospective impact of future developments in autonomous UAVs and communications on UAV real-time target detection. Full article

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22 pages, 23235 KB

Open AccessEditor’s ChoiceArticle

Efficient YOLOv7-Drone: An Enhanced Object Detection Approach for Drone Aerial Imagery

by Xiaofeng Fu, Guoting Wei, Xia Yuan, Yongshun Liang and Yuming Bo

Drones 2023, 7(10), 616; https://doi.org/10.3390/drones7100616 - 1 Oct 2023

Cited by 44 | Viewed by 9166

Abstract

In recent years, the rise of low-cost mini rotary-wing drone technology across diverse sectors has emphasized the crucial role of object detection within drone aerial imagery. Low-cost mini rotary-wing drones come with intrinsic limitations, especially in computational power. Drones come with intrinsic limitations, [...] Read more.

In recent years, the rise of low-cost mini rotary-wing drone technology across diverse sectors has emphasized the crucial role of object detection within drone aerial imagery. Low-cost mini rotary-wing drones come with intrinsic limitations, especially in computational power. Drones come with intrinsic limitations, especially in resource availability. This context underscores an urgent need for solutions that synergize low latency, high precision, and computational efficiency. Previous methodologies have primarily depended on high-resolution images, leading to considerable computational burdens. To enhance the efficiency and accuracy of object detection in drone aerial images, and building on the YOLOv7, we propose the Efficient YOLOv7-Drone. Recognizing the common presence of small objects in aerial imagery, we eliminated the less efficient P5 detection head and incorporated the P2 detection head for increased precision in small object detection. To ensure efficient feature relay from the Backbone to the Neck, channels within the CBS module were optimized. To focus the model more on the foreground and reduce redundant computations, the TGM-CESC module was introduced, achieving the generation of pixel-level constrained sparse convolution masks. Furthermore, to mitigate potential data losses from sparse convolution, we embedded the head context-enhanced method (HCEM). Comprehensive evaluation using the VisDrone and UAVDT datasets demonstrated our model’s efficacy and practical applicability. The Efficient Yolov7-Drone achieved state-of-the-art scores while ensuring real-time detection performance. Full article

(This article belongs to the Special Issue Advanced Unmanned System Control and Data Processing)

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18 pages, 4298 KB

Open AccessEditor’s ChoiceArticle

Large-Sized Multirotor Design: Accurate Modeling with Aerodynamics and Optimization for Rotor Tilt Angle

by Anhuan Xie, Xufei Yan, Weisheng Liang, Shiqiang Zhu and Zheng Chen

Drones 2023, 7(10), 614; https://doi.org/10.3390/drones7100614 - 29 Sep 2023

Cited by 4 | Viewed by 6725

Abstract

Advancements in aerial mobility (AAM) are driven by needs in transportation, logistics, rescue, and disaster relief. Consequently, large-sized multirotor unmanned aerial vehicles (UAVs) with strong power and ample space show great potential. In order to optimize the design process for large-sized multirotors and [...] Read more.

Advancements in aerial mobility (AAM) are driven by needs in transportation, logistics, rescue, and disaster relief. Consequently, large-sized multirotor unmanned aerial vehicles (UAVs) with strong power and ample space show great potential. In order to optimize the design process for large-sized multirotors and reduce physical trial and error, a detailed dynamic model is firstly established, with an accurate aerodynamic model. In addition, the center of gravity (CoG) offset and actuator dynamics are also well considered, which are usually ignored in small-sized multirotors. To improve the endurance and maneuverability of large-sized multirotors, which is the key concern in real applications, a two-loop optimization method for rotor tilt angle design is proposed based on the mathematical model established previously. Its inner loop solves the dynamic equilibrium points to relax the complex dynamic constraints caused by aerodynamics in the overall optimization problem, which improves the solution efficiency. The ideal design results can be obtained through the offline process, which greatly reduces the difficulties of physical trial and error. Finally, various experiments are carried out to demonstrate the accuracy of the established model and the effectiveness of the optimization method. Full article

(This article belongs to the Special Issue Optimal Design, Dynamics, and Navigation of Drones)

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20 pages, 16424 KB

Open AccessEditor’s ChoiceArticle

Estimating Maize Maturity by Using UAV Multi-Spectral Images Combined with a CCC-Based Model

by Zhao Liu, Huapeng Li, Xiaohui Ding, Xinyuan Cao, Hui Chen and Shuqing Zhang

Drones 2023, 7(9), 586; https://doi.org/10.3390/drones7090586 - 19 Sep 2023

Cited by 5 | Viewed by 4356

Abstract

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, [...] Read more.

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, and senescence. The main goal of this study was to estimate maize GMC at maturity through CCC retrieved from multi-spectral UAV images using a PROSAIL model inversion and compare its performance with GMC estimation through simple vegetation indices (VIs) approaches. This study was conducted in two separate maize fields of 50.3 and 56 ha located in Hailun County, Heilongjiang Province, China. Each of the fields was cultivated with two maize varieties. One field was used as reference data for constructing the model, and the other field was applied to validate. The leaf chlorophyll content (LCC) and leaf area index (LAI) of maize were collected at three critical stages of crop growth, and meanwhile, the GMC of maize at maturity was also obtained. During the collection of field data, a UAV flight campaign was performed to obtain multi-spectral images from two fields at three main crop growth stages. In order to calibrate and evaluate the PROSAIL model for obtaining maize CCC, crop canopy spectral reflectance was simulated using crop-specific parameters. In addition, various VIs were computed from multi-spectral images to estimate maize GMC at maturity and compare the results with CCC estimations. When the CCC-retrieved results were compared to measured data, the R² value was 0.704, the RMSE was 34.58 μg/cm², and the MAE was 26.27 μg/cm². The estimation accuracy of the maize GMC based on the normalized red edge index (NDRE) was demonstrated to be the greatest among the selected VIs in both fields, with R² values of 0.6 and 0.619, respectively. Although the VIs of UAV inversion GMC accuracy are lower than those of CCC, their rapid acquisition, high spatial and temporal resolution, suitability for empirical models, and capture of growth differences within the field are still helpful techniques for field-scale crop monitoring. We found that maize varieties are the main reason for the maturity variation of maize under the same geographical and environmental conditions. The method described in this article enables precision agriculture based on UAV remote sensing by giving growers a spatial reference for crop maturity at the field scale. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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15 pages, 3112 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Impacts of Drone Flight Altitude on Behaviors and Species Identification of Marsh Birds in Florida

by Jeremy P. Orange, Ronald R. Bielefeld, William A. Cox and Andrea L. Sylvia

Drones 2023, 7(9), 584; https://doi.org/10.3390/drones7090584 - 16 Sep 2023

Cited by 7 | Viewed by 4824

Abstract

Unmanned aerial vehicles (hereafter drones) are rapidly replacing manned aircraft as the preferred tool used for aerial wildlife surveys, but questions remain about which survey protocols are most effective and least impactful on wildlife behaviors. We evaluated the effects of drone overflights on [...] Read more.

Unmanned aerial vehicles (hereafter drones) are rapidly replacing manned aircraft as the preferred tool used for aerial wildlife surveys, but questions remain about which survey protocols are most effective and least impactful on wildlife behaviors. We evaluated the effects of drone overflights on nontarget species to inform the development of a Florida mottled duck (MODU; Anas fulvigula fulvigula) survey. Our objectives were to (1) evaluate the effect of flight altitude on the behavior of marsh birds, (2) evaluate the effect of altitude on a surveyor’s ability to identify the species of detected birds, and (3) test protocols for upcoming MODU surveys. We flew 120 continuously moving transects at altitudes ranging from 12 to 91 m and modeled variables that influenced detection, species identification, and behavior of nontarget species. Few marsh birds were disturbed during drone flights, but we were unable to confidently detect birds at the two highest altitudes, and we experienced difficulties identifying the species of birds detected in video collected at 30 m. Our findings indicate that MODUs could be surveyed at altitudes as low as 12–30 m with minimal impact to adjacent marsh birds and that larger-bodied nontarget marsh species can be identified from videos collected during MODU drone surveys. Full article

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

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34 pages, 20980 KB

Open AccessEditor’s ChoiceArticle

Go with the Flow: Estimating Wind Using Uncrewed Aircraft

by Marc D. Compere, Kevin A. Adkins and Avinash Muthu Krishnan

Drones 2023, 7(9), 564; https://doi.org/10.3390/drones7090564 - 1 Sep 2023

Cited by 5 | Viewed by 7241

Abstract

This paper presents a fundamentally different approach to wind estimation using Uncrewed Aircraft (UA) than the vast majority of existing methods. This method uses no on-board flow sensor and does not attempt to estimate thrust or drag forces. Using only GPS and orientation [...] Read more.

This paper presents a fundamentally different approach to wind estimation using Uncrewed Aircraft (UA) than the vast majority of existing methods. This method uses no on-board flow sensor and does not attempt to estimate thrust or drag forces. Using only GPS and orientation sensors, the strategy estimates wind vectors in an Earth-fixed frame during turning maneuvers. The method presented here is called the Wind-Arc method. The philosophy behind this method has been seen in practice, but this paper presents an alternative derivation with resulting performance evaluations in simulations and flight tests. The simulations verify the method provides perfect performance under ideal conditions using simulated GPS, heading angle, and satisfied assumptions. When applied to experimental flight test data, the method works and follows both the airspeed and wind speed trends, but improvements can still be made. Wind triangles are displayed at each instant in time along the flight path that illustrate the graphical nature of the approach and solution. Future work will include wind gust estimation and a Quality of Estimate (QoE) metric to determine what conditions provide good wind speed estimates while preserving the method’s generality and simplicity. Full article

(This article belongs to the Special Issue Weather Impacts on Uncrewed Aircraft)

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24 pages, 489 KB

Open AccessEditor’s ChoiceReview

Advanced Air Mobility and Evolution of Mobile Networks

by Lechosław Tomaszewski and Robert Kołakowski

Drones 2023, 7(9), 556; https://doi.org/10.3390/drones7090556 - 29 Aug 2023

Cited by 16 | Viewed by 5054

Abstract

Advanced Air Mobility (AAM) is a promising field of services based on Unmanned Aerial Vehicles (UAVs), which aims to provide people and cargo transportation services in underserved areas. The recent advancements in the fields of aviation and mobile telecommunication networks have opened up [...] Read more.

Advanced Air Mobility (AAM) is a promising field of services based on Unmanned Aerial Vehicles (UAVs), which aims to provide people and cargo transportation services in underserved areas. The recent advancements in the fields of aviation and mobile telecommunication networks have opened up multiple opportunities for the development of disruptive AAM applications. This paper presents the overview and identifies the major requirements of emerging AAM use cases to confront them with the features provided by the 5G System (5GS), which is commonly considered the key enabler in providing commercial AAM services. The major benefits, gaps, and issues regarding using 5GS to serve AAM operations are identified and discussed. Finally, the future perspectives for AAM services are outlined with a focus on the potential benefit that can be provided as the mobile network evolves towards 6G. Full article

(This article belongs to the Special Issue AAM Integration: Strategic Insights and Goals)

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25 pages, 1672 KB

Open AccessEditor’s ChoiceArticle

Drone-Based Environmental Emergency Response in the Brazilian Amazon

by Janiele Custodio and Hernan Abeledo

Drones 2023, 7(9), 554; https://doi.org/10.3390/drones7090554 - 27 Aug 2023

Cited by 6 | Viewed by 6007

Abstract

This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited. The resulting discrete facility location–allocation [...] Read more.

This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited. The resulting discrete facility location–allocation model with mobile servers assumes a centralized network operated out of sight by first responders and government agents. The optimization problem seeks to find the minimal cost configuration that meets operational constraints and performance objectives. To test the practical applicability of the proposed model, a real-life case study was implemented for the municipality of Ji-Paraná, in the Brazilian Amazon, using demand data from a mobile whistle-blower application and from satellite imagery projects that monitor deforestation and fire incidents in the region. Experiments are performed to understand the model’s sensitivity to various demand scenarios and capacity restrictions. Full article

(This article belongs to the Special Issue UAV IoT Sensing and Networking)

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21 pages, 7349 KB

Open AccessEditor’s ChoiceArticle

Stepwise Soft Actor–Critic for UAV Autonomous Flight Control

by Ha Jun Hwang, Jaeyeon Jang, Jongkwan Choi, Jung Ho Bae, Sung Ho Kim and Chang Ouk Kim

Drones 2023, 7(9), 549; https://doi.org/10.3390/drones7090549 - 24 Aug 2023

Cited by 12 | Viewed by 5523

Abstract

Despite the growing demand for unmanned aerial vehicles (UAVs), the use of conventional UAVs is limited, as most of them require being remotely operated by a person who is not within the vehicle’s field of view. Recently, many studies have introduced reinforcement learning [...] Read more.

Despite the growing demand for unmanned aerial vehicles (UAVs), the use of conventional UAVs is limited, as most of them require being remotely operated by a person who is not within the vehicle’s field of view. Recently, many studies have introduced reinforcement learning (RL) to address hurdles for the autonomous flight of UAVs. However, most previous studies have assumed overly simplified environments, and thus, they cannot be applied to real-world UAV operation scenarios. To address the limitations of previous studies, we propose a stepwise soft actor–critic (SeSAC) algorithm for efficient learning in a continuous state and action space environment. SeSAC aims to overcome the inefficiency of learning caused by attempting challenging tasks from the beginning. Instead, it starts with easier missions and gradually increases the difficulty level during training, ultimately achieving the final goal. We also control a learning hyperparameter of the soft actor–critic algorithm and implement a positive buffer mechanism during training to enhance learning effectiveness. Our proposed algorithm was verified in a six-degree-of-freedom (DOF) flight environment with high-dimensional state and action spaces. The experimental results demonstrate that the proposed algorithm successfully completed missions in two challenging scenarios, one for disaster management and another for counter-terrorism missions, while surpassing the performance of other baseline approaches. Full article

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

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22 pages, 11112 KB

Open AccessEditor’s ChoiceArticle

An Evaluation of Sun-Glint Correction Methods for UAV-Derived Secchi Depth Estimations in Inland Water Bodies

by Edvinas Tiškus, Martynas Bučas, Diana Vaičiūtė, Jonas Gintauskas and Irma Babrauskienė

Drones 2023, 7(9), 546; https://doi.org/10.3390/drones7090546 - 23 Aug 2023

Cited by 14 | Viewed by 4462

Abstract

This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to [...] Read more.

This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to model the SD values derived from UAV multispectral imagery, highlighting the role of reflectance accuracy and algorithmic precision in SD modeling. While Goodman’s method showed a higher correlation (0.92) with in situ SD measurements, Hedley’s method exhibited the smallest average deviation (0.65 m), suggesting its potential in water resource management, environmental monitoring, and ecological modeling. The study also underscored the quasi-analytical algorithm (QAA) potential in estimating SD due to its flexibility to process data from various sensors without requiring in situ measurements, offering scalability for large-scale water quality surveys. The accuracy of SD measures calculated using QAA was related to variability in water constituents of colored dissolved organic matter and the solar zenith angle. A practical workflow for SD acquisition using UAVs and multispectral data is proposed for monitoring inland water bodies. Full article

(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)

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23 pages, 46577 KB

Open AccessEditor’s ChoiceArticle

Drone-YOLO: An Efficient Neural Network Method for Target Detection in Drone Images

by Zhengxin Zhang

Drones 2023, 7(8), 526; https://doi.org/10.3390/drones7080526 - 11 Aug 2023

Cited by 332 | Viewed by 32584

Abstract

Object detection in unmanned aerial vehicle (UAV) imagery is a meaningful foundation in various research domains. However, UAV imagery poses unique challenges, including large image sizes, small sizes detection objects, dense distribution, overlapping instances, and insufficient lighting impacting the effectiveness of object detection. [...] Read more.

Object detection in unmanned aerial vehicle (UAV) imagery is a meaningful foundation in various research domains. However, UAV imagery poses unique challenges, including large image sizes, small sizes detection objects, dense distribution, overlapping instances, and insufficient lighting impacting the effectiveness of object detection. In this article, we propose Drone-YOLO, a series of multi-scale UAV image object detection algorithms based on the YOLOv8 model, designed to overcome the specific challenges associated with UAV image object detection. To address the issues of large scene sizes and small detection objects, we introduce improvements to the neck component of the YOLOv8 model. Specifically, we employ a three-layer PAFPN structure and incorporate a detection head tailored for small-sized objects using large-scale feature maps, significantly enhancing the algorithm’s capability to detect small-sized targets. Furthermore, we integrate the sandwich-fusion module into each layer of the neck’s up–down branch. This fusion mechanism combines network features with low-level features, providing rich spatial information about the objects at different layer detection heads. We achieve this fusion using depthwise separable evolution, which balances parameter costs and a large receptive field. In the network backbone, we employ RepVGG modules as downsampling layers, enhancing the network’s ability to learn multi-scale features and outperforming traditional convolutional layers. The proposed Drone-YOLO methods have been evaluated in ablation experiments and compared with other state-of-the-art approaches on the VisDrone2019 dataset. The results demonstrate that our Drone-YOLO (large) outperforms other baseline methods in the accuracy of object detection. Compared to YOLOv8, our method achieves a significant improvement in

m A P_{0.5}

metrics, with a 13.4% increase on the VisDrone2019-test and a 17.40% increase on the VisDrone2019-val. Additionally, the parameter-efficient Drone-YOLO (tiny) with only 5.25 M parameters performs equivalently or better than the baseline method with 9.66M parameters on the dataset. These experiments validate the effectiveness of the Drone-YOLO methods in the task of object detection in drone imagery. Full article

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