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
Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane
Drones 2024, 8(6), 239; https://doi.org/10.3390/drones8060239 (registering DOI) - 2 Jun 2024
Abstract
The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused on the expression of the roof contour, but
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The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused on the expression of the roof contour, but did not extract the wall contour. In view of this, an algorithm based on the geometric features of the neighborhood points of the region-growing clustering fusion surface is proposed to extract the boundary points of the UAV building point cloud. Firstly, the region growth plane is fused to obtain a more accurate segmentation plane. Then, the neighboring points are projected onto the neighborhood plane and a vector between the object point and neighborhood point is constructed. Finally, the azimuth of each vector is calculated, and the boundary points of each segmented plane are extracted according to the difference in adjacent azimuths. Experiment results show that the best boundary points can be extracted when the number of adjacent points is 24 and the difference in adjacent azimuths is 120. The proposed method is superior to other methods in the contour extraction of UAV buildings point clouds. Moreover, it can extract not only the building roof contour points, but also the wall contour points, including the window contour points.
Full article
(This article belongs to the Special Issue Resilient UAV Autonomy and Remote Sensing)
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Open AccessArticle
Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment
by
Xavier Fernando and Abhishek Gupta
Drones 2024, 8(6), 238; https://doi.org/10.3390/drones8060238 (registering DOI) - 2 Jun 2024
Abstract
The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a
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The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions.
Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in UAV Technology)
Open AccessArticle
Path-Following Formation of Fixed-Wing UAVs under Communication Delay: A Vector Field Approach
by
Thiem V. Pham and Thanh Dong Nguyen
Drones 2024, 8(6), 237; https://doi.org/10.3390/drones8060237 (registering DOI) - 2 Jun 2024
Abstract
In many applications, such as atmospheric observation or disaster monitoring, cooperative control of a fleet of UAVs is crucial because it is effective in repeated tasks. In this work, we provide a workable and useful cooperative guiding algorithm for several fixed-wing UAVs to
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In many applications, such as atmospheric observation or disaster monitoring, cooperative control of a fleet of UAVs is crucial because it is effective in repeated tasks. In this work, we provide a workable and useful cooperative guiding algorithm for several fixed-wing UAVs to construct a path-following formation with communication delays. The two primary components of our concept are path-following (lateral guidance) and path formation (longitudinal guidance). The former is in charge of ensuring that, in the presence of wind disturbance, the lateral distance between the UAV and its targeted path converges using a well-known vector field technique. In the event of a communication delay, the latter ensures that several fixed-wing UAVs will create a predetermined formation shape. Furthermore, we provide a maximum delay bound that is dependent on the topology and a controller’s gain. Lastly, in order to confirm the viability and advantages of our suggested approach, we construct an effective platform for a hardware-in-the-loop (HIL) test.
Full article
(This article belongs to the Topic Cooperative Localization, Optimization and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)
Open AccessArticle
Dynamic Task Allocation for Heterogeneous Multi-UAVs in Uncertain Environments Based on 4DI-GWO Algorithm
by
Hanqiao Huang, Zijian Jiang, Tian Yan and Yu Bai
Drones 2024, 8(6), 236; https://doi.org/10.3390/drones8060236 (registering DOI) - 1 Jun 2024
Abstract
As the missions and environments of unmanned aerial vehicles (UAVs) become increasingly complex in both space and time, it is essential to investigate the dynamic task assignment problem of heterogeneous multi-UAVs aiming at ground targets in an uncertain environment. Considering that most of
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As the missions and environments of unmanned aerial vehicles (UAVs) become increasingly complex in both space and time, it is essential to investigate the dynamic task assignment problem of heterogeneous multi-UAVs aiming at ground targets in an uncertain environment. Considering that most of these existing tasking methods are limited to static allocation in a deterministic environment, this paper firstly constructs the fuzzy multiconstraint programming model for heterogeneous multi-UAV dynamic task assignment based on binary interval theory, taking into account the effects of uncertain factors like target location information, mission execution time, and the survival probability of UAVs. Then, the dynamic task allocation strategy is designed, consisting of two components: dynamic time slice setting and the four-dimensional information grey wolf optimization (4DI-GWO) algorithm. The dynamic time slices create the dynamic adjustment of solving frequency and effect, and the 4DI-GWO algorithm is improved by designing the four-dimensional information strategy that expands population diversity and enhances global search capability and other strategies. The numerical analysis shows that the proposed strategy can effectively solve the dynamic task assignment problem of heterogeneous multi-UAVs under an uncertain environment, and the optimization of fitness values demonstrates improvements of 5%~30% in comparison with other optimization algorithms.
Full article
Open AccessArticle
Towards Fully Autonomous Drone Tracking by a Reinforcement Learning Agent Controlling a Pan–Tilt–Zoom Camera
by
Mariusz Wisniewski, Zeeshan A. Rana, Ivan Petrunin, Alan Holt and Stephen Harman
Drones 2024, 8(6), 235; https://doi.org/10.3390/drones8060235 - 30 May 2024
Abstract
Pan–tilt–zoom cameras are commonly used for surveillance applications. Their automation could reduce the workload of human operators and increase the safety of airports by tracking anomalous objects such as drones. Reinforcement learning is an artificial intelligence method that outperforms humans on certain specific
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Pan–tilt–zoom cameras are commonly used for surveillance applications. Their automation could reduce the workload of human operators and increase the safety of airports by tracking anomalous objects such as drones. Reinforcement learning is an artificial intelligence method that outperforms humans on certain specific tasks. However, there exists a lack of data and benchmarks for pan–tilt–zoom control mechanisms in tracking airborne objects. Here, we show a simulated environment that contains a pan–tilt–zoom camera being used to train and evaluate a reinforcement learning agent. We found that the agent can learn to track the drone in our basic tracking scenario, outperforming a solved scenario benchmark value. The agent is also tested on more complex scenarios, where the drone is occluded behind obstacles. While the agent does not quantitatively outperform the optimal human model, it shows qualitative signs of learning to solve the complex, occluded non-linear trajectory scenario. Given further training, investigation, and different algorithms, we believe a reinforcement learning agent could be used to solve such scenarios consistently. Our results demonstrate how complex drone surveillance tracking scenarios may be solved and fully autonomized by reinforcement learning agents. We hope our environment becomes a starting point for more sophisticated autonomy in control of pan–tilt–zoom cameras tracking of drones and surveilling airspace for anomalous objects. For example, distributed, multi-agent systems of pan–tilt–zoom cameras combined with other sensors could lead towards fully autonomous surveillance, challenging experienced human operators.
Full article
(This article belongs to the Special Issue UAV Detection, Classification, and Tracking)
Open AccessArticle
Multi-UAV Cooperative Localization Using Adaptive Wasserstein Filter with Distance-Constrained Bare Bones Self-Recovery Particles
by
Xiuli Xin, Feng Pan, Yuhe Wang and Xiaoxue Feng
Drones 2024, 8(6), 234; https://doi.org/10.3390/drones8060234 - 30 May 2024
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Aiming at the cooperative localization problem for the dynamic UAV swarm in an anchor-limited environment, an adaptive Wasserstein filter (AWF) with distance-constrained bare bones self-recovery particles (CBBP) is proposed. Firstly, to suppress the cumulative error from the inertial navigation system (INS), a position-prediction
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Aiming at the cooperative localization problem for the dynamic UAV swarm in an anchor-limited environment, an adaptive Wasserstein filter (AWF) with distance-constrained bare bones self-recovery particles (CBBP) is proposed. Firstly, to suppress the cumulative error from the inertial navigation system (INS), a position-prediction strategy based on transition particles is designed instead of using inertial measurements directly, which ensures that the generated prior particles can better cover the ground truth and provide the uncertainties of nonlinear estimation. Then, to effectively quantify the difference between the observed and the prior data, the Wasserstein measure based on slice segmentation is introduced to update the posterior weights of the particles, which makes the proposed algorithm robust against distance-measurement noise variance under the strongly nonlinear model. In addition, to solve the problem of particle impoverishment caused by traditional resampling, a diversity threshold based on Gini purity is designed, and a fast bare bones particle self-recovery algorithm with distance constraint is proposed to guide the outlier particles to the high-likelihood region, which effectively improves the accuracy and stability of the estimation. Finally, the simulation results show that the proposed algorithm is robust against cumulative error in an anchor-limited environment and achieves more competitive accuracy with fewer particles.
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Open AccessReview
Monitoring Nodal Transportation Assets with Uncrewed Aerial Vehicles: A Comprehensive Review
by
Taraneh Askarzadeh, Raj Bridgelall and Denver Tolliver
Drones 2024, 8(6), 233; https://doi.org/10.3390/drones8060233 - 30 May 2024
Abstract
Using Uncrewed Aerial Vehicles (UAVs) to monitor the condition of nodal transportation assets—airports, seaports, heliports, vertiports, and cargo terminals—presents a transformative approach to traditional inspection methods. The focus on nodal assets rather than linear assets like roads, railways, bridges, and waterways fills a
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Using Uncrewed Aerial Vehicles (UAVs) to monitor the condition of nodal transportation assets—airports, seaports, heliports, vertiports, and cargo terminals—presents a transformative approach to traditional inspection methods. The focus on nodal assets rather than linear assets like roads, railways, bridges, and waterways fills a gap in addressing the dynamic challenges specific to transportation hubs. This study reviews scholarly literature on applying UAV-based remote sensing (URS) techniques to assess the condition of various transportation hubs, which are critical junctures in global logistics networks. Utilizing a systematic literature review framework, this study reviewed 486 publications from 2015 to 2023 to extract insights from the evolving discourse on URS applications. The findings suggest that these emerging methods resulted in substantial enhancements in time saving, cost efficiency, safety, and reliability. Specifically, this study presents evidence on how URS approaches can overcome the constraints of conventional inspection methods by enabling rapid, high-precision mapping and surveillance in complex and constrained environments. The findings highlight the role of UAVs in enhancing operational workflows and decision making in transportation planning and maintenance. By bridging the gap between traditional practices and innovative technology, this research offers significant implications for stakeholders in the field, advocating for a shift towards more dynamic, cost-effective, and precise asset management strategies.
Full article
(This article belongs to the Special Issue Applications of UVs in Digital Photogrammetry and Image Processing)
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Open AccessArticle
A Robust Strategy for UAV Autonomous Landing on a Moving Platform under Partial Observability
by
Godwyll Aikins, Sagar Jagtap and Kim-Doang Nguyen
Drones 2024, 8(6), 232; https://doi.org/10.3390/drones8060232 - 30 May 2024
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Landing a multi-rotor uncrewed aerial vehicle (UAV) on a moving target in the presence of partial observability, due to factors such as sensor failure or noise, represents an outstanding challenge that requires integrative techniques in robotics and machine learning. In this paper, we
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Landing a multi-rotor uncrewed aerial vehicle (UAV) on a moving target in the presence of partial observability, due to factors such as sensor failure or noise, represents an outstanding challenge that requires integrative techniques in robotics and machine learning. In this paper, we propose embedding a long short-term memory (LSTM) network into a variation of proximal policy optimization (PPO) architecture, termed robust policy optimization (RPO), to address this issue. The proposed algorithm is a deep reinforcement learning approach that utilizes recurrent neural networks (RNNs) as a memory component. Leveraging the end-to-end learning capability of deep reinforcement learning, the RPO-LSTM algorithm learns the optimal control policy without the need for feature engineering. Through a series of simulation-based studies, we demonstrate the superior effectiveness and practicality of our approach compared to the state-of-the-art proximal policy optimization (PPO) and the classical control method Lee-EKF, particularly in scenarios with partial observability. The empirical results reveal that RPO-LSTM significantly outperforms competing reinforcement learning algorithms, achieving up to 74% more successful landings than Lee-EKF and 50% more than PPO in flicker scenarios, maintaining robust performance in noisy environments and in the most challenging conditions that combine flicker and noise. These findings underscore the potential of RPO-LSTM in solving the problem of UAV landing on moving targets amid various degrees of sensor impairment and environmental interference.
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Open AccessArticle
Safety and Security-Specific Application of Multiple Drone Sensors at Movement Areas of an Aerodrome
by
Béla Kovács, Fanni Vörös, Tímea Vas, Krisztián Károly, Máté Gajdos and Zsófia Varga
Drones 2024, 8(6), 231; https://doi.org/10.3390/drones8060231 - 30 May 2024
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Nowadays, the public service practice applicability of drones and remote sensing sensors is being explored in almost all industrial and military areas. In the present research, in collaboration with different universities, we investigate the applicability of drones in airport procedures, assessing the various
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Nowadays, the public service practice applicability of drones and remote sensing sensors is being explored in almost all industrial and military areas. In the present research, in collaboration with different universities, we investigate the applicability of drones in airport procedures, assessing the various potential applications. By exploiting the data from remote sensing sensors, we aim to develop methodologies that can assist airport operations, including managing the risk of wildlife threats to runway safety, infrastructure maintenance, and foreign object debris (FOD) detection. Drones equipped with remote sensing sensors provide valuable insight into surface diagnostics, helping to assess aprons, taxiways, and runways. In addition, drones can enhance airport security with effective surveillance and threat detection capabilities, as well as provide data to support existing air traffic control models and systems. In this paper, we aim to present our experience with the potential airport applications of UAV high-resolution RGB, thermal, and LiDAR sensors. Through interdisciplinary collaboration and innovative methodologies, our research aims to revolutionize airport operations, safety, and security protocols, outlining a path toward a safer, more efficient airport ecosystem.
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Open AccessArticle
The Development of an Optimal Operation Algorithm for Food Delivery Using Drones Considering Time Interval between Deliveries
by
Young Kwan Ko, Hyeseon Han, Yonghui Oh and Young Dae Ko
Drones 2024, 8(6), 230; https://doi.org/10.3390/drones8060230 - 30 May 2024
Abstract
These days, many attempts are being made worldwide to use drones for food delivery. Especially in the case of food, fast delivery is required, while maintaining its temperature and taste to the maximum. Therefore, using drones is suitable for food delivery because they
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These days, many attempts are being made worldwide to use drones for food delivery. Especially in the case of food, fast delivery is required, while maintaining its temperature and taste to the maximum. Therefore, using drones is suitable for food delivery because they can move through the air without being affected by traffic congestion. In this study, the purpose is to develop an optimal algorithm that can complete the delivery of customer food orders in the shortest time using drones. We have applied mathematical-model-based optimization techniques to develop an algorithm that reflects the given problem situation. Since the delivery capacity of drones is limited, and especially small, multiple drones may be used to deliver the food ordered by a particular customer. What is important here is that the drones assigned to one customer must arrive consecutively within a short period of time. This fact is reflected in this mathematical model. In the numerical example, it can be confirmed that the proposed algorithm operates optimally by comparing a case where the arrival time of multiple drones assigned to one customer is limited to a certain time and a case when it is not.
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(This article belongs to the Special Issue Advances of Drones in Logistics)
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Open AccessArticle
High-Efficiency Data Fusion Aerodynamic Performance Modeling Method for High-Altitude Propellers
by
Miao Zhang, Jun Jiao, Jian Zhang and Zijian Zhang
Drones 2024, 8(6), 229; https://doi.org/10.3390/drones8060229 - 30 May 2024
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During the overall design phase of solar-powered unmanned aerial vehicles (UAVs), a large amount of high-fidelity (HF) propeller aerodynamic performance data is required to enhance design performance, but the acquisition cost is prohibitively expensive. To improve model accuracy and reduce modeling costs, this
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During the overall design phase of solar-powered unmanned aerial vehicles (UAVs), a large amount of high-fidelity (HF) propeller aerodynamic performance data is required to enhance design performance, but the acquisition cost is prohibitively expensive. To improve model accuracy and reduce modeling costs, this paper constructs a multi-fidelity aerodynamic data fusion model by associating data with different fidelity. This model utilizes a low-fidelity computational method to quickly determine the design space. The constrained Latin hypercube sampling based on the successive local enumeration (SLE-CLHS) method and the expected improvement (EI) criterion were adopted to achieve the efficient initialization and fastest convergence of the Co-Kriging surrogate model within the design space. This modeling framework was applied to acquire the aerodynamic performance of high-altitude propellers, and the model was evaluated using various performance indicators. The results demonstrate that the proposed model has excellent predictive performance. Specifically, when the surrogate model was constructed using 350 high-fidelity samples, there were improvements of 13.727%, 12.241%, and 5.484% for thrust, torque, and efficiency compared with the surrogate model constructed from low-fidelity samples.
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Open AccessArticle
CooPercept: Cooperative Perception for 3D Object Detection of Autonomous Vehicles
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Yuxuan Zhang, Bing Chen, Jie Qin, Feng Hu and Jie Hao
Drones 2024, 8(6), 228; https://doi.org/10.3390/drones8060228 - 29 May 2024
Abstract
Autonomous vehicles rely extensively on onboard sensors to perceive their surrounding environments for motion planning and vehicle control. Despite recent advancements, prevalent perception algorithms typically utilize data acquired from the single host vehicle, which can lead to challenges such as sensor data sparsity,
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Autonomous vehicles rely extensively on onboard sensors to perceive their surrounding environments for motion planning and vehicle control. Despite recent advancements, prevalent perception algorithms typically utilize data acquired from the single host vehicle, which can lead to challenges such as sensor data sparsity, field-of-view limitations, and occlusion. To address these issues and enhance the perception capabilities of autonomous driving systems, we explore the concept of multi-vehicle multimedia cooperative perception by investigating the fusion of LiDAR point clouds and camera images from multiple interconnected vehicles with different positions and viewing angles. Specifically, we introduce a semantic point cloud feature-level cooperative perception framework, termed CooPercept, designed to mitigate computing complexity and reduce turnaround time. This is crucial, as the volume of raw sensor data traffic generally far exceeds the bandwidth of existing vehicular networks. Our approach is validated through experiments conducted on synthetic datasets from KITTI and OPV2V. The results demonstrate that our proposed CooPercept model surpasses comparable perception models, achieving enhanced detection accuracy and greater detection robustness.
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(This article belongs to the Special Issue Advances in Modeling, Estimation, and Control of Intelligent Transportation Systems)
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Open AccessFeature PaperArticle
Ensemble Learning for Pea Yield Estimation Using Unmanned Aerial Vehicles, Red Green Blue, and Multispectral Imagery
by
Zehao Liu, Yishan Ji, Xiuxiu Ya, Rong Liu, Zhenxing Liu, Xuxiao Zong and Tao Yang
Drones 2024, 8(6), 227; https://doi.org/10.3390/drones8060227 - 29 May 2024
Abstract
Peas are one of the most important cultivated legumes worldwide, for which early yield estimations are helpful for agricultural planning. The unmanned aerial vehicles (UAVs) have become widely used for crop yield estimations, owing to their operational convenience. In this study, three types
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Peas are one of the most important cultivated legumes worldwide, for which early yield estimations are helpful for agricultural planning. The unmanned aerial vehicles (UAVs) have become widely used for crop yield estimations, owing to their operational convenience. In this study, three types of sensor data (red green blue [RGB], multispectral [MS], and a fusion of RGB and MS) across five growth stages were applied to estimate pea yield using ensemble learning (EL) and four base learners (Cubist, elastic net [EN], K nearest neighbor [KNN], and random forest [RF]). The results showed the following: (1) the use of fusion data effectively improved the estimation accuracy in all five growth stages compared to the estimations obtained using a single sensor; (2) the mid filling growth stage provided the highest estimation accuracy, with coefficients of determination (R2) reaching up to 0.81, 0.8, 0.58, and 0.77 for the Cubist, EN, KNN, and RF algorithms, respectively; (3) the EL algorithm achieved the best performance in estimating pea yield than base learners; and (4) the different models were satisfactory and applicable for both investigated pea types. These results indicated that the combination of dual-sensor data (RGB + MS) from UAVs and appropriate algorithms can be used to obtain sufficiently accurate pea yield estimations, which could provide valuable insights for agricultural remote sensing research.
Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)
Open AccessArticle
UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic
by
Bingze Xia, Iraj Mantegh and Wenfang Xie
Drones 2024, 8(6), 226; https://doi.org/10.3390/drones8060226 - 29 May 2024
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With the rapid development of Artificial Intelligence, AI-enabled Uncrewed Aerial Vehicles have garnered extensive attention since they offer an accessible and cost-effective solution for executing tasks in unknown or complex environments. However, developing secure and effective AI-based algorithms that empower agents to learn,
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With the rapid development of Artificial Intelligence, AI-enabled Uncrewed Aerial Vehicles have garnered extensive attention since they offer an accessible and cost-effective solution for executing tasks in unknown or complex environments. However, developing secure and effective AI-based algorithms that empower agents to learn, adapt, and make precise decisions in dynamic situations continues to be an intriguing area of study. This paper proposes a hybrid intelligent control framework that integrates an enhanced Soft Actor–Critic method with a fuzzy inference system, incorporating pre-defined expert experience to streamline the learning process. Additionally, several practical algorithms and approaches within this control system are developed. With the synergy of these innovations, the proposed method achieves effective real-time path planning in unpredictable environments under a model-free setting. Crucially, it addresses two significant challenges in RL: dynamic-environment problems and multi-target problems. Diverse scenarios incorporating actual UAV dynamics were designed and simulated to validate the performance in tracking multiple mobile intruder aircraft. A comprehensive analysis and comparison of methods relying solely on RL and other influencing factors, as well as a controller feasibility assessment for real-world flight tests, are conducted, highlighting the advantages of the proposed hybrid architecture. Overall, this research advances the development of AI-driven approaches for UAV safe autonomous navigation under demanding airspace conditions and provides a viable learning-based control solution for different types of robots.
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Open AccessArticle
Next-Gen Remote Airport Maintenance: UAV-Guided Inspection and Maintenance Using Computer Vision
by
Zhiyuan Yang, Sujit Nashik, Cuiting Huang, Michal Aibin and Lino Coria
Drones 2024, 8(6), 225; https://doi.org/10.3390/drones8060225 - 29 May 2024
Abstract
This paper presents a novel system for the automated monitoring and maintenance of gravel runways in remote airports, particularly in Northern Canada, using Unmanned Aerial Vehicles (UAVs) and computer vision technologies. Due to the geographic isolation and harsh weather conditions, these airports face
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This paper presents a novel system for the automated monitoring and maintenance of gravel runways in remote airports, particularly in Northern Canada, using Unmanned Aerial Vehicles (UAVs) and computer vision technologies. Due to the geographic isolation and harsh weather conditions, these airports face unique challenges in runway maintenance. Our approach integrates advanced deep learning algorithms and UAV technology to provide a cost-effective, efficient, and accurate means of detecting runway defects, such as water pooling, vegetation encroachment, and surface irregularities. We developed a hybrid approach combining the vision transformer model with image filtering and thresholding algorithms, applied on high-resolution UAV imagery. This system not only identifies various types of defects but also evaluates runway smoothness, contributing significantly to the safety and reliability of air transport in these areas. Our experiments, conducted across multiple remote airports, demonstrate the effectiveness of our approach in real-world scenarios, offering significant improvements over traditional manual inspection methods.
Full article
(This article belongs to the Special Issue Applications of UAVs in Civil Infrastructure)
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Open AccessArticle
Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images Considering Turbid Water Distribution in a Reservoir
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Mitsuteru Irie, Yugen Manabe and Masafumi Yamashita
Drones 2024, 8(6), 224; https://doi.org/10.3390/drones8060224 - 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 - 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
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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.
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(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.
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(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
by
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.
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(This article belongs to the Section Drone Design and Development)
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