Journal Description
Drones
Drones
is an international, peer-reviewed, open access journal published monthly online by MDPI. The journal focuses on design and applications of drones, including unmanned aerial vehicle (UAV), Unmanned Aircraft Systems (UAS), and Remotely Piloted Aircraft Systems (RPAS), etc. Likewise, contributions based on unmanned water/underwater drones and unmanned ground vehicles are also welcomed.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High visibility: indexed within Scopus, SCIE (Web of Science), Inspec, and other databases.
- Journal Rank: JCR - Q2 (Remote Sensing) / CiteScore - Q1 (Aerospace Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.9 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
4.8 (2022);
5-Year Impact Factor:
5.5 (2022)
Latest Articles
Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images Considering Turbid Water Distribution in a Reservoir
Drones 2024, 8(6), 224; https://doi.org/10.3390/drones8060224 (registering DOI) - 29 May 2024
Abstract
The observation of the phytoplankton distribution with a high spatiotemporal resolution is necessary to track the nutrient sources that cause algal blooms and to understand their behavior in response to hydraulic phenomena. Photography from UAVs, which has an excellent temporal and spatial resolution,
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The observation of the phytoplankton distribution with a high spatiotemporal resolution is necessary to track the nutrient sources that cause algal blooms and to understand their behavior in response to hydraulic phenomena. Photography from UAVs, which has an excellent temporal and spatial resolution, is an effective method to obtain water quality information comprehensively. In this study, we attempted to develop a method for estimating the chlorophyll concentration from aerial images using machine learning that considers brightness correction based on insolation and the spatial distribution of turbidity evaluated by satellite image analysis. The reflectance of harmful algae bloom (HAB) was different from that of phytoplankton seen under normal conditions; so, the images containing HAB were the causes of error in the estimation of the chlorophyll concentration. First, the images when the bloom occurred were extracted by the discrimination with machine learning. Then, the other images were used for the regression of the concentration. Finally, the coefficient of determination between the estimated chlorophyll concentration when no bloom occurred by the image analysis and the observed value reached 0.84. The proposed method enables the detailed depiction of the spatial distribution of the chlorophyll concentration, which contributes to the improvement in water quality management in reservoirs.
Full article
(This article belongs to the Topic Remote Sensing and Geoinformatics in Agriculture and Environment Volume II)
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Open AccessReview
Use of Participatory sUAS in Resilient Socioecological Systems (SES) Research: A Review and Case Study from the Southern Great Plains, USA
by
Todd D. Fagin, Jacqueline M. Vadjunec, Austin L. Boardman and Lanah M. Hinsdale
Drones 2024, 8(6), 223; https://doi.org/10.3390/drones8060223 (registering DOI) - 29 May 2024
Abstract
Since the publication of the seminal work People and Pixels: Linking Remote Sensing and the Social Sciences, the call to “socialize the pixel” and “pixelize the social” has gone largely unheeded from a truly participatory research context. Instead, participatory remote sensing has
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Since the publication of the seminal work People and Pixels: Linking Remote Sensing and the Social Sciences, the call to “socialize the pixel” and “pixelize the social” has gone largely unheeded from a truly participatory research context. Instead, participatory remote sensing has primarily involved ground truthing to verify remote sensing observations and/or participatory mapping methods to complement remotely sensed data products. However, the recent proliferation of relatively low-cost, ready-to-fly small unoccupied aerial systems (sUAS), colloquially known as drones, may be changing this trajectory. sUAS may provide a means for community participation in all aspects of the photogrammetric/remote sensing process, from mission planning and data acquisition to data processing and analysis. We present an overview of the present state of so-called participatory sUAS through a comprehensive literature review of recent English-language journal articles. This is followed by an overview of our own experiences with the use of sUAS in a multi-year participatory research project in an agroecological system encompassing a tri-county/tri-state region in the Southern Great Plains, USA. We conclude with a discussion of opportunities and challenges associated with our experience.
Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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Open AccessArticle
A Dynamic Visual SLAM System Incorporating Object Tracking for UAVs
by
Minglei Li, Jia Li, Yanan Cao and Guangyong Chen
Drones 2024, 8(6), 222; https://doi.org/10.3390/drones8060222 - 29 May 2024
Abstract
The capability of unmanned aerial vehicles (UAVs) to capture and utilize dynamic object information assumes critical significance for decision making and scene understanding. This paper presents a method for UAV relative positioning and target tracking based on a visual simultaneousocalization and mapping (SLAM)
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The capability of unmanned aerial vehicles (UAVs) to capture and utilize dynamic object information assumes critical significance for decision making and scene understanding. This paper presents a method for UAV relative positioning and target tracking based on a visual simultaneousocalization and mapping (SLAM) framework. By integrating an object detection neural network into the SLAM framework, this method can detect moving objects and effectively reconstruct the 3D map of the environment from image sequences. For multiple object tracking tasks, we combine the region matching of semantic detection boxes and the point matching of the optical flow method to perform dynamic object association. This joint association strategy can prevent trackingoss due to the small proportion of the object in the whole image sequence. To address the problem ofacking scale information in the visual SLAM system, we recover the altitude data based on a RANSAC-based plane estimation approach. The proposed method is tested on both the self-created UAV dataset and the KITTI dataset to evaluate its performance. The results demonstrate the robustness and effectiveness of the solution in facilitating UAV flights.
Full article
(This article belongs to the Special Issue When Deep Learning Meets Geometry for Air-to-Ground Perception on Drones)
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Open AccessArticle
HHPSO: A Heuristic Hybrid Particle Swarm Optimization Path Planner for Quadcopters
by
Jiabin Lou, Rong Ding and Wenjun Wu
Drones 2024, 8(6), 221; https://doi.org/10.3390/drones8060221 - 28 May 2024
Abstract
Path planning for quadcopters has been proven to be one kind of NP-hard problem with huge search space and tiny feasible solution range. Metaheuristic algorithms are widely used in such types of problems for their flexibility and effectiveness. Nevertheless, most of them cannot
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Path planning for quadcopters has been proven to be one kind of NP-hard problem with huge search space and tiny feasible solution range. Metaheuristic algorithms are widely used in such types of problems for their flexibility and effectiveness. Nevertheless, most of them cannot meet the needs in terms of efficiency and suffer from the limitations of premature convergence and local minima. This paper proposes a novel algorithm named Heuristic Hybrid Particle Swarm Optimization (HHPSO) to address the path planning problem. On the heuristic side, we use the control points of cubic b-splines as variables instead of waypoints and establish some heuristic rules during algorithm initialization to generate higher-quality particles. On the hybrid side, we introduce an iteration-varying penalty term to shrink the search range gradually, a Cauchy mutation operator to improve the exploration ability, and an injection operator to prevent population homogenization. Numerical simulations, physical model-based simulations, and a real-world experiment demonstrate the proposed algorithm’s superiority, effectiveness and robustness.
Full article
(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs)
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Open AccessArticle
Development of Unmanned Aerial Vehicle Navigation and Warehouse Inventory System Based on Reinforcement Learning
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Huei-Yung Lin, Kai-Lun Chang and Hsin-Ying Huang
Drones 2024, 8(6), 220; https://doi.org/10.3390/drones8060220 - 28 May 2024
Abstract
In this paper, we present the exploration of indoor positioning technologies for UAVs, as well as navigation techniques for path planning and obstacle avoidance. The objective was to perform warehouse inventory tasks, using a drone to search for barcodes or markers to identify
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In this paper, we present the exploration of indoor positioning technologies for UAVs, as well as navigation techniques for path planning and obstacle avoidance. The objective was to perform warehouse inventory tasks, using a drone to search for barcodes or markers to identify objects. For the indoor positioning techniques, we employed visual-inertial odometry (VIO), ultra-wideband (UWB), AprilTag fiducial markers, and simultaneous localization and mapping (SLAM). These algorithms included global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. For UAV navigation, we combined the SLAM-based RTAB-map indoor mapping and navigation path planning of the ROS for indoor environments. This system enabled precise drone positioning indoors and utilized global and local path planners to generate flight paths that avoided dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. To achieve warehouse inventory inspection, a reinforcement learning approach was proposed, recognizing markers by adjusting the UAV’s viewpoint. We addressed several of the main problems in inventory management, including efficiently planning of paths, while ensuring a certain detection rate. Two reinforcement learning techniques, AC (actor–critic) and PPO (proximal policy optimization), were implemented based on AprilTag identification. Testing was performed in both simulated and real-world environments, and the effectiveness of the proposed method was validated.
Full article
(This article belongs to the Section Drone Design and Development)
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Open AccessReview
Beyond Flight: Enhancing the Internet of Drones with Blockchain Technologies
by
Kyriaki A. Tychola, Konstantinos Voulgaridis and Thomas Lagkas
Drones 2024, 8(6), 219; https://doi.org/10.3390/drones8060219 - 26 May 2024
Abstract
The Internet of Drones (IoD) is a decentralized network linking drones’ access to controlled airspace, providing high adaptability to complex scenarios and services to various drone applications, such as package delivery, traffic surveillance, and rescue, including navigation services. Unmanned Aerial Vehicles (UAVs), combined
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The Internet of Drones (IoD) is a decentralized network linking drones’ access to controlled airspace, providing high adaptability to complex scenarios and services to various drone applications, such as package delivery, traffic surveillance, and rescue, including navigation services. Unmanned Aerial Vehicles (UAVs), combined with IoD principles, offer numerous strengths, e.g., high mobility, wireless coverage areas, and the ability to reach inaccessible locations, including significant improvements such as reliability, connectivity, throughput, and decreased delay. Additionally, emerging blockchain solutions integrated within the concept of the IoD enable effective outcomes that surpass traditional security approaches, while enabling decentralized features for smart human-centered applications. Nevertheless, the combination of the IoD and blockchain faces many challenges with emerging open issues that require further investigation. In this work, we thoroughly survey the technological concept of the IoD and fundamental aspects of blockchain, while investigating its contribution to current IoD practices, the impact of novel enabling technologies, and their active role in the combination of the corresponding synergy. Moreover, we promote the combination of the two technologies by researching their collaborative functionality through different use cases and application fields that implement decentralized IoD solutions and highlighting their indicative benefits, while discussing important challenges and future directions on open issues.
Full article
Open AccessArticle
Joint Drone Access and LEO Satellite Backhaul for a Space–Air–Ground Integrated Network: A Multi-Agent Deep Reinforcement Learning-Based Approach
by
Xuan Huang, Xu Xia, Zhibo Wang and Mugen Peng
Drones 2024, 8(6), 218; https://doi.org/10.3390/drones8060218 - 25 May 2024
Abstract
The space–air–ground integrated network can provide services to ground users in remote areas by utilizing high-altitude platform (HAP) drones to support stable user access and using low earth orbit (LEO) satellites to provide large-scale traffic backhaul. However, the rapid movement of LEO satellites
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The space–air–ground integrated network can provide services to ground users in remote areas by utilizing high-altitude platform (HAP) drones to support stable user access and using low earth orbit (LEO) satellites to provide large-scale traffic backhaul. However, the rapid movement of LEO satellites requires dynamic maintenance of the matching relationship between LEO satellites and HAP drones. Additionally, different traffic types generated at HAP drones hold varying levels of values. Therefore, a tripartite matching problem among LEO satellites, HAP drones, and traffic types jointly considering multi-dimensional characteristics such as remaining visible time, channel condition, handover latency, and traffic storage capacity is formulated as mixed integer nonlinear programming to maximize the average transmitted traffic value. The traffic generation state for HAP drones is modeled as a mixture of stochasticity and determinism, which aligns with real-world scenarios, posing challenges for traditional optimization solvers. Thus, the original problem is decoupled into two independent sub-problems: traffic–drone matching and LEO–drone matching, which are addressed by mathematical simplification and multi-agent deep reinforcement learning with centralized training and decentralized execution, respectively. Simulation results verify the effectiveness and superiority of the proposed tripartite matching approach.
Full article
(This article belongs to the Special Issue Space–Air–Ground Integrated Networks for 6G)
Open AccessArticle
Design, Construction, and Flight Performance of an Electrically Operated Fixed-Wing UAV
by
Ilias Panagiotopoulos, Lefteris Sakellariou and Antonios Hatziefremidis
Drones 2024, 8(6), 217; https://doi.org/10.3390/drones8060217 - 24 May 2024
Abstract
The development of unmanned aerial vehicles (UAVs) has attracted much attention in the global community and aviation industry. As UAVs have the potential to be applied for multiple missions, the level of research into improving their design and flight performance has also increased.
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The development of unmanned aerial vehicles (UAVs) has attracted much attention in the global community and aviation industry. As UAVs have the potential to be applied for multiple missions, the level of research into improving their design and flight performance has also increased. In this context, the present paper aims to present the design, construction, and flight performance of an electrically operated fixed-wing UAV. As a first step in the design process, key performance requirements are defined, such as the thrust required, the stall speed, the minimum drag velocity, and the minimum power velocity. Wing and associated power loadings are calculated according to the defined performance requirements. In addition, payload and endurance requirements are set up in order to determine the wing and tail areas, the total mass, the power requirements, and the motor size. Aerodynamics and stability designs are also calculated. After the completion of the design process, the manufacturing of the UAV follows by using appropriate materials. Flight tests were carried out for the evaluation of the UAV’s flight performance, where the success of the design was demonstrated.
Full article
(This article belongs to the Section Drone Design and Development)
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Open AccessArticle
Predicting Grape Yield with Vine Canopy Morphology Analysis from 3D Point Clouds Generated by UAV Imagery
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Adam Šupčík, Gabor Milics and Igor Matečný
Drones 2024, 8(6), 216; https://doi.org/10.3390/drones8060216 - 24 May 2024
Abstract
With its ability to estimate yield, winemakers may better manage their vineyards and obtain important insights into the possible crop. The proper estimation of grape output is contingent upon an accurate evaluation of the morphology of the vine canopy, as this has a
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With its ability to estimate yield, winemakers may better manage their vineyards and obtain important insights into the possible crop. The proper estimation of grape output is contingent upon an accurate evaluation of the morphology of the vine canopy, as this has a substantial impact on the final product. This study’s main goals were to gather canopy morphology data using a sophisticated 3D model and assess how well different morphology characteristics predicted yield results. An unmanned aerial vehicle (UAV) with an RGB camera was used in the vineyards of Topoľčianky, Slovakia, to obtain precise orthophotos of individual vine rows. Following the creation of an extensive three-dimensional (3D) model of the assigned region, a thorough examination was carried out to determine many canopy characteristics, including thickness, side section dimensions, volume, and surface area. According to the study, the best combination for predicting grape production was the side section and thickness. Using more than one morphological parameter is advised for a more precise yield estimate as opposed to depending on only one.
Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
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Open AccessArticle
UAS Photogrammetry and TLS Technology: A Novel Approach to Predictive Maintenance in Industrial Tank Systems
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Sergio García-Martos, Pedro García-Trenza, Antonio Saura-Campos, Antonio Guerrero-González and Fernando Hidalgo-Castelo
Drones 2024, 8(6), 215; https://doi.org/10.3390/drones8060215 - 22 May 2024
Abstract
This paper explores the integration of terrestrial laser scanning (TLS) and unmanned aerial system (UAS) photogrammetry for the diagnosis and evaluation of deformations in industrial tanks, demonstrating their significant contribution to preventive maintenance. TLS accurately measures distances to the tank’s surface, generating detailed
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This paper explores the integration of terrestrial laser scanning (TLS) and unmanned aerial system (UAS) photogrammetry for the diagnosis and evaluation of deformations in industrial tanks, demonstrating their significant contribution to preventive maintenance. TLS accurately measures distances to the tank’s surface, generating detailed 3D point clouds, while UAS photogrammetry captures high-resolution images from various angles and altitudes. By combining TLS and UAS data into comprehensive 3D models, engineers can identify subtle deformations and anticipate structural failures. The study results revealed significant deviations in tank shell verticality and roundness using TLS and notable roof unevenness using UAS. Comparing 3D models before and after corrective measures showed improved structural integrity. This approach enhances safety, optimizes resources, and enables targeted interventions. The findings highlight the potential of TLS and UAS technologies to revolutionize preventive maintenance, offering an efficient, precise, and less intrusive methodology for critical infrastructure inspection. Adopting these technologies can improve safety, reduce operational risks, and optimize asset management in various industrial sectors.
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Open AccessArticle
UAV Swarm Cooperative Dynamic Target Search: A MAPPO-Based Discrete Optimal Control Method
by
Dexing Wei, Lun Zhang, Quan Liu, Hao Chen and Jian Huang
Drones 2024, 8(6), 214; https://doi.org/10.3390/drones8060214 - 22 May 2024
Abstract
Unmanned aerial vehicles (UAVs) are commonly employed in pursuit and rescue missions, where the target’s trajectory is unknown. Traditional methods, such as evolutionary algorithms and ant colony optimization, can generate a search route in a given scenario. However, when the scene changes, the
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Unmanned aerial vehicles (UAVs) are commonly employed in pursuit and rescue missions, where the target’s trajectory is unknown. Traditional methods, such as evolutionary algorithms and ant colony optimization, can generate a search route in a given scenario. However, when the scene changes, the solution needs to be recalculated. In contrast, more advanced deep reinforcement learning methods can train an agent that can be directly applied to a similar task without recalculation. Nevertheless, there are several challenges when the agent learns how to search for unknown dynamic targets. In this search task, the rewards are random and sparse, which makes learning difficult. In addition, because of the need for the agent to adapt to various scenario settings, interactions required between the agent and the environment are more comparable to typical reinforcement learning tasks. These challenges increase the difficulty of training agents. To address these issues, we propose the OC-MAPPO method, which combines optimal control (OC) and Multi-Agent Proximal Policy Optimization (MAPPO) with GPU parallelization. The optimal control model provides the agent with continuous and stable rewards. Through parallelized models, the agent can interact with the environment and collect data more rapidly. Experimental results demonstrate that the proposed method can help the agent learn faster, and the algorithm demonstrated a 26.97% increase in the success rate compared to genetic algorithms.
Full article
(This article belongs to the Special Issue Advances in Cartography, Mission Planning, Path Search, and Path Following for Drones)
Open AccessArticle
Probabilistic Chain-Enhanced Parallel Genetic Algorithm for UAV Reconnaissance Task Assignment
by
Jiaze Tang, Dan Liu, Qisong Wang, Junbao Li and Jinwei Sun
Drones 2024, 8(6), 213; https://doi.org/10.3390/drones8060213 - 21 May 2024
Abstract
With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough
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With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough solutions, they encounter substantial challenges in addressing large-scale task assignments due to their extensive computational demands. Conversely, while heuristic algorithms are capable of delivering satisfactory solutions with limited computational resources, they frequently struggle with converging on locally optimal solutions and are characterized by low iteration rates. In response to these limitations, this paper presents a novel approach: the probabilistic chain-enhanced parallel genetic algorithm (PC-EPGA). The PC-EPGA combines probabilistic chains with genetic algorithms to significantly enhance the quality of solutions. In our approach, each UAV flight is considered a Dubins vehicle, incorporating kinematic constraints. In addition, it integrates parallel genetic algorithms to improve hardware performance and processing speed. In our study, we represent task points as chromosome nodes and construct probabilistic connection chains between these nodes. This structure is specifically designed to influence the genetic algorithm’s crossover and mutation processes by taking into account both the quantity of tasks assigned to UAVs and the associated costs of inter-task flights. In addition, we propose a fitness-based adaptive crossover operator to circumvent local optima more effectively. To optimize the parameters of the PC-EPGA, Bayesian networks are utilized, which improves the efficiency of the whole parameter search process. The experimental results show that compared to the traditional heuristic algorithms, the probabilistic chain algorithm significantly improves the quality of solutions and computational efficiency.
Full article
(This article belongs to the Special Issue Intelligent Autonomous Control and Swarm Cooperative Control of Unmanned Systems)
Open AccessReview
Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms
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Takashi Sonam Tashi Tanaka, Sheng Wang, Johannes Ravn Jørgensen, Marco Gentili, Armelle Zaragüeta Vidal, Anders Krogh Mortensen, Bharat Sharma Acharya, Brittany Deanna Beck and René Gislum
Drones 2024, 8(6), 212; https://doi.org/10.3390/drones8060212 - 21 May 2024
Abstract
The phenotyping of field crops quantifies a plant’s structural and physiological characteristics to facilitate crop breeding. High-throughput unmanned aerial vehicle (UAV)-based remote sensing platforms have been extensively researched as replacements for more laborious and time-consuming manual field phenotyping. This review aims to elucidate
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The phenotyping of field crops quantifies a plant’s structural and physiological characteristics to facilitate crop breeding. High-throughput unmanned aerial vehicle (UAV)-based remote sensing platforms have been extensively researched as replacements for more laborious and time-consuming manual field phenotyping. This review aims to elucidate the advantages and challenges of UAV-based phenotyping techniques. This is a comprehensive overview summarizing the UAV platforms, sensors, and data processing while also introducing recent technological developments. Recently developed software and sensors greatly enhance the accessibility of UAV-based phenotyping, and a summary of recent research (publications 2019–2024) provides implications for future research. Researchers have focused on integrating multiple sensing data or utilizing machine learning algorithms, such as ensemble learning and deep learning, to enhance the prediction accuracies of crop physiological traits. However, this approach will require big data alongside laborious destructive measurements in the fields. Future research directions will involve standardizing the process of merging data from multiple field experiments and data repositories. Previous studies have focused mainly on UAV technology in major crops, but there is a high potential in minor crops or cropping systems for future sustainable crop production. This review can guide new practitioners who aim to implement and utilize UAV-based phenotyping.
Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
Open AccessArticle
Suboptimal Trajectory Planning Technique in Real UAV Scenarios with Partial Knowledge of the Environment
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Matilde Gelli, Luca Bigazzi, Enrico Boni and Michele Basso
Drones 2024, 8(6), 211; https://doi.org/10.3390/drones8060211 - 21 May 2024
Abstract
In recent years, the issue of trajectory planning for autonomous unmanned aerial vehicles (UAVs) has received significant attention due to the rising demand for these vehicles across various applications. Despite advancements, real-time trajectory planning remains computationally demanding, particularly with the inclusion of 3D
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In recent years, the issue of trajectory planning for autonomous unmanned aerial vehicles (UAVs) has received significant attention due to the rising demand for these vehicles across various applications. Despite advancements, real-time trajectory planning remains computationally demanding, particularly with the inclusion of 3D localization using computer vision or advanced sensors. Consequently, much of the existing research focuses on semi-autonomous systems, which rely on ground assistance through the use of external sensors (motion capture systems) and remote computing power. This study addresses the challenge by proposing a fully autonomous trajectory planning solution. By introducing a real-time path planning algorithm based on the minimization of the snap, the optimal trajectory is dynamically recalculated as needed. Evaluation of the algorithm’s performance is conducted in an unknown real-world scenario, utilizing both simulations and experimental data. The algorithm was implemented in MATLAB and subsequently translated to C++ for onboard execution on the drone.
Full article
(This article belongs to the Special Issue Optimal Design, Dynamics, and Navigation of Drones)
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Open AccessArticle
The Challenges of Blood Sample Delivery via Drones in Urban Environment: A Feasibility Study through Specific Operation Risk Assessment Methodology
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Sara Molinari, Riccardo Patriarca and Marco Ducci
Drones 2024, 8(5), 210; https://doi.org/10.3390/drones8050210 - 20 May 2024
Abstract
In recent years, Unmanned Aircraft System (UAS) usage in the medical sector as an alternative to traditional means of goods transport has grown significantly. Even though the reduced response time achieved with UASs can be lifesaving in critical situations, their usage must comply
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In recent years, Unmanned Aircraft System (UAS) usage in the medical sector as an alternative to traditional means of goods transport has grown significantly. Even though the reduced response time achieved with UASs can be lifesaving in critical situations, their usage must comply with technological constraints such as range, speed and capacity, while minimizing potential risks. In this paper, the feasibility of a drone operation dedicated to the transport of blood samples in an urban area is studied through a safety risk analysis. The assessment utilizes the Specific Operation Risk Assessment (SORA) framework, in line with current European regulations, and extends it to define flight trajectories with minimal risk. A case study in the Helsinki urban area is used as a reference, with an exemplary case of commercial drone transportation of blood samples between the Töölö and Malmi Hospitals. By leveraging the drone performance capabilities and minimizing the risk for people on the ground, this approach demonstrates that medical delivery using drones in densely populated urban environments remains challenging. Nonetheless, it argues that the proposed method can enhance risk awareness and support the planning of feasible operations.
Full article
(This article belongs to the Special Issue Flight Control and Collision Avoidance of UAVs)
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Open AccessCorrection
Correction: Bianchi et al. Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator. Drones 2024, 8, 29
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Domenico Bianchi, Alessandro Borri, Federico Cappuzzo and Stefano Di Gennaro
Drones 2024, 8(5), 209; https://doi.org/10.3390/drones8050209 - 20 May 2024
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Figure corrections [...]
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Open AccessArticle
A Framework of Grasp Detection and Operation for Quadruped Robot with a Manipulator
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Jiamin Guo, Hui Chai, Qin Zhang, Haoning Zhao, Meiyi Chen, Yueyang Li and Yibin Li
Drones 2024, 8(5), 208; https://doi.org/10.3390/drones8050208 - 19 May 2024
Abstract
Quadruped robots equipped with manipulators need fast and precise grasping and detection algorithms for the transportation of disaster relief supplies. To address this, we developed a framework for these robots, comprising a Grasp Detection Controller (GDC), a Joint Trajectory Planner (JTP), a Leg
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Quadruped robots equipped with manipulators need fast and precise grasping and detection algorithms for the transportation of disaster relief supplies. To address this, we developed a framework for these robots, comprising a Grasp Detection Controller (GDC), a Joint Trajectory Planner (JTP), a Leg Joint Controller (LJC), and a Manipulator Joint Controller (MJC). In the GDC, we proposed a lightweight grasp detection CNN based on DenseBlock called DES-LGCNN, which reduced algorithm complexity while maintaining accuracy by incorporating UP and DOWN modules with DenseBlock. For JTP, we optimized the model based on quadruped robot kinematics to enhance wrist camera visibility in dynamic environments. We integrated the network and model into our homemade robot control system and verified our framework through multiple experiments. First, we evaluated the accuracy of the grasp detection algorithm using the Cornell and Jacquard datasets. On the Jacquard dataset, we achieved a detection accuracy of 92.49% for grasp points within 6 ms. Second, we verified its visibility through simulation. Finally, we conducted dynamic scene experiments which consisted of a dynamic target scenario (DTS), a dynamic base scenario (DBS), and a dynamic target and base scenario (DTBS) using an SDU-150 physical robot. In all three scenarios, the object was successfully grasped. The results demonstrate the effectiveness of our framework in managing dynamic environments throughout task execution.
Full article
(This article belongs to the Special Issue Advanced Unmanned System Control and Data Processing)
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Open AccessArticle
Chaff Cloud Integrated Communication and TT&C: An Integrated Solution for Single-Station Emergency Communications and TT&C in a Denied Environment
by
Lvyang Ye, Yikang Yang, Binhu Chen, Deng Pan, Fan Yang, Shaojun Cao, Yangdong Yan and Fayu Sun
Drones 2024, 8(5), 207; https://doi.org/10.3390/drones8050207 - 18 May 2024
Abstract
In response to potential denial environments such as canyons, gullies, islands, and cities where users are located, traditional Telemetry, Tracking, and Command (TT&C) systems can still maintain core requirements such as availability, reliability, and sustainability in the face of complex electromagnetic environments and
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In response to potential denial environments such as canyons, gullies, islands, and cities where users are located, traditional Telemetry, Tracking, and Command (TT&C) systems can still maintain core requirements such as availability, reliability, and sustainability in the face of complex electromagnetic environments and non-line-of-sight environments that may cause service degradation or even failure. This paper presents a single-station emergency solution that integrates communication and TT&C (IC&T) functions based on radar chaff cloud technology. Firstly, a suitable selection of frequency bands and modulation methods is provided for the emergency IC&T system to ensure compatibility with existing communication and TT&C systems while catering to the future needs of IC&T. Subsequently, theoretical analyses are conducted on the communication link transmission loss, data transmission, code tracking accuracy, and anti-multipath model of the emergency IC&T system based on the chosen frequency band and modulation mode. This paper proposes a dual-way asynchronous precision ranging and time synchronization (DWAPR&TS) system employing dual one-way ranging (DOWR) measurement, a dual-way asynchronous incoherent Doppler velocity measurement (DWAIDVM) system, and a single baseline angle measurement system. Next, we analyze the physical characteristics of the radar chaff and establish a dynamic model of spherical chaff cloud clusters based on free diffusion. Additionally, we provide the optimal strategy for deploying chaff cloud. Finally, the emergency IC&T application based on the radar chaff cloud relay is simulated, and the results show that for severe interference, taking drones as an example, under a measurement baseline of 100 km, the emergency IC&T solution proposed in this paper can achieve an accuracy range of approximately 100 m, a velocity accuracy of 0.1 m/s, and an angle accuracy of 0.1°. In comparison with existing TT&C system solutions, the proposed system possesses unique and potential advantages that the others do not have. It can serve as an emergency IC&T reference solution in denial environments, offering significant value for both civilian and military applications.
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(This article belongs to the Special Issue UAV Trajectory Generation, Optimization and Cooperative Control)
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Open AccessArticle
Incorporating Symbolic Discrete Controller Synthesis into a Virtual Robot Experimental Platform: An Implementation with Collaborative Unmanned Aerial Vehicle Robots
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Mete Özbaltan and Serkan Çaşka
Drones 2024, 8(5), 206; https://doi.org/10.3390/drones8050206 - 17 May 2024
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We introduce a modeling framework aimed at incorporating symbolic discrete controller synthesis (DCS) into a virtual robot experimental platform. This framework involves symbolically representing the behaviors of robotic systems along with their control objectives using synchronous programming techniques. We employed DCS algorithms through
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We introduce a modeling framework aimed at incorporating symbolic discrete controller synthesis (DCS) into a virtual robot experimental platform. This framework involves symbolically representing the behaviors of robotic systems along with their control objectives using synchronous programming techniques. We employed DCS algorithms through the reactive synchronous environment ReaX to generate controllers that fulfill specified objectives. These resulting controllers were subsequently deployed on the virtual robot experimental platform Simscape. To demonstrate and validate our approach, we provide an implementation example involving collaborative UAV robots.
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Open AccessArticle
A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density
by
Tongyao Yang, Fengbao Yang and Dingzhu Li
Drones 2024, 8(5), 205; https://doi.org/10.3390/drones8050205 - 16 May 2024
Abstract
Path planning is one of the most essential parts of autonomous navigation. Most existing works are based on the strategy of adjusting angles for planning. However, drones are susceptible to collisions in environments with densely distributed and high-speed obstacles, which poses a serious
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Path planning is one of the most essential parts of autonomous navigation. Most existing works are based on the strategy of adjusting angles for planning. However, drones are susceptible to collisions in environments with densely distributed and high-speed obstacles, which poses a serious threat to flight safety. To handle this challenge, we propose a new method based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density (MSAO2H). Firstly, we propose to extend the obstacle avoidance decisions of drones into angle adjustment, speed adjustment, and obstacle clearance. Hybrid action space is adopted to model each decision. Secondly, the state space of the obstacle environment is constructed to provide effective features for learning decision parameters. The instant reward and the ultimate reward are designed to balance the learning efficiency of decision parameters and the ability to explore optimal solutions. Finally, we innovatively introduced the interferometric fluid dynamics system into the parameterized deep Q-network to guide the learning of angle parameters. Compared with other algorithms, the proposed model has high success rates and generates high-quality planned paths. It can meet the requirements for autonomously planning high-quality paths in densely dynamic obstacle environments.
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(This article belongs to the Topic Target Tracking, Guidance, and Navigation for Autonomous Systems)
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