Search Results (1,438)

This paper presents a strategy for collision detection and recovery control of drones using an onboard Inertial Measurement Unit (IMU). The collision detection algorithm compares the expected response of the drone with the measurements from the IMU to identify and characterize collisions. The recovery controller implements a gain scheduling approach, adjusting its parameters based on the characteristics of the collision and the drone’s attitude. Simulations were conducted to compare the proposed collision detection strategy with a popular detection method with fixed thresholds, and the simulation results showed that the proposed approach outperformed the existing method in terms of detection accuracy. Furthermore, the proposed collision detection and recovery control approaches were tested with physical experiments using a custom-built drone. The experimental results confirmed that the proposed collision detection algorithm was able to distinguish between actual collisions and aggressive flight maneuvers, and the recovery controller can recover the drone within 0.8 s. Full article

Unmanned aerial vehicle (UAV) swarms are widely applied in various fields, including military and civilian domains. However, due to the scarcity of spectrum resources, UAV swarm clustering technology has emerged as an effective method for achieving spectrum sharing among UAV swarms. This paper introduces a distributed heterogeneous multi-agent deep reinforcement learning algorithm, named HMDRL-UC, which is specifically designed to address the cluster-based spectrum sharing problem in heterogeneous UAV swarms. Heterogeneous UAV swarms consist of two types of UAVs: cluster head (CH) and cluster member (CM). Each UAV is equipped with an intelligent agent to execute the deep reinforcement learning (DRL) algorithm. Correspondingly, the HMDRL-UC consists of two parts: multi-agent proximal policy optimization for cluster head (MAPPO-H) and independent proximal policy optimization for cluster member (IPPO-M). The MAPPO-H enables the CHs to decide cluster selection and moving position, while CMs utilize IPPO-M to cluster autonomously under the condition of certain partial channel distribution information (CDI). Adequate experimental evidence has confirmed that the HMDRL-UC algorithm proposed in this paper is not only capable of managing dynamic drone swarm scenarios in the presence of partial CDI, but also has a clear advantage over the other existing three algorithms in terms of average throughput, intra-cluster communication delay, and minimum signal-to-noise ratio (SNR). Full article

The aim of this work is the design of a 200 W transmitting coil for a high-power wireless power transfer (WPT) system based on magnetic resonant coupling (MRC) to charge the battery of a drone in 1 h equipped with a WPT receiving coil integrated into the landing gear. This innovative solution is based on the use of the landing gear as the receiving coil, thereby obviating the need for an additional component (e.g., separate receiving coil). The proposed landing gear is fabricated from aluminum, to reduce weight, and to improve mechanical robustness and electrical performance. Consequently, the design reduces overall weight and system complexity while minimizing potential destabilization of the drone’s flight dynamics. However, a specific design of the primary coil is required to ensure high efficiency even in case of an inaccurate landing of the drone on a ground pad. To this aim, a double-D configuration is here proposed and optimized for the transmitting coil, while a double coil receiver in combination with a charge controller that uses a maximum power point tracking (MPPT) algorithm is integrated into the landing gear. The results obtained from the simulations demonstrate that the proposed WPT system has excellent electrical efficiency and very high tolerance to coil misalignment in terms of the coupling coefficient due to imprecise landing. The transmission efficiency of the final test prototype can reach 95% with a coupling coefficient of k = 0.16, and it can drop to a minimum of 85% when misalignment occurs resulting in k = 0.06. Full article

Unmanned aerial vehicles (UAVs) have revolutionized fields such as monitoring, cargo delivery, precision farming, and emergency response, demonstrating remarkable flexibility and operational effectiveness. A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, safety, and power consumption. This article presents an extensive overview of methodologies for UAV route planning, including deterministic models, stochastic sampling techniques, biologically inspired methods, and integrated algorithmic frameworks. The discussion extends to their performance in various operational contexts, including stationary, moving, and three-dimensional settings. Innovative methods utilizing artificial intelligence, particularly machine learning and neural networks, are emphasized for their promise in facilitating adaptive responses to intricate, evolving environments. Furthermore, strategies focused on reducing energy usage and enabling coordinated operations among multiple drones are analyzed, addressing issues such as prolonged operation, distribution of assignments, and navigation around obstacles. Although notable advancements have been achieved, challenges like high computational demands and the need for immediate responsiveness persist. By consolidating the latest progress, this survey provides meaningful perspectives and guidance for the ongoing evolution of UAV route planning solutions. Full article

To address challenges such as large-scale variations, high density of small targets, and the large number of parameters in deep learning-based target detection models, which limit their deployment on UAV platforms with fixed performance and limited computational resources, a lightweight UAV target detection algorithm, YOLO-LSM, is proposed. First, to mitigate the loss of small target information, an Efficient Small Target Detection Layer (ESTDL) is developed, alongside structural improvements to the baseline model to reduce parameters. Second, a Multiscale Lightweight Convolution (MLConv) is designed, and a lightweight feature extraction module, MLCSP, is constructed to enhance the extraction of detailed information. Focaler inner IoU is incorporated to improve bounding box matching and localization, thereby accelerating model convergence. Finally, a novel feature fusion network, DFSPP, is proposed to enhance accuracy by optimizing the selection and adjustment of target scale ranges. Validations on the VisDrone2019 and Tiny Person datasets demonstrate that compared to the benchmark network, the YOLO-LSM achieves a mAP0.5 improvement of 6.9 and 3.5 percentage points, respectively, with a parameter count of 1.9 M, representing a reduction of approximately 72%. Different from previous work on medical detection, this study tailors YOLO-LSM for UAV-based small object detection by introducing targeted improvements in feature extraction, detection heads, and loss functions, achieving better adaptation to aerial scenarios. Full article

Synergetic transportation schemes are extensively used in package delivery operations, exploiting the best features of different modes. This paper proposes a methodology to solve the mode assignment and routing problem for the case of combined conventional vehicle and unmanned aerial vehicle (CV–UAV) parcel deliveries under uncertainty for next-day operations. This research incorporates ground and air uncertainties: travel times are assumed for conventional vehicles, while UAV paths are affected by weather conditions and restricted flying zones. A nested genetic algorithm is initially used to solve the problem under fixed conditions. Then, a robust optimization approach is employed to propose the best solution that will perform well in a stochastic environment. The framework is applied to a case study of realistic urban–suburban size, and results are discussed. The entire platform is useful for strategic decisions on infrastructure and for operation planning with satisfactory performance and less risk. Full article

The growing use of unmanned aerial vehicles (UAVs) in diverse fields such as disaster recovery, rural regions, and smart cities necessitates effective dynamic drone network establishment techniques. Conventional optimization techniques like genetic algorithms (GAs) and particle swarm optimization (PSO) are weak when it comes to real-time adjustment to the environment and multi-objective constraints. This paper proposes a hybrid optimization framework combining genetic algorithms and reinforcement learning (RL) to improve the deployment of drone networks. We integrate Q-learning into the GA mutation process to allow drones to adaptively adjust locations in real time under coverage, connectivity, and energy constraints. In the scenario of large-scale simulations for wildfire tracking, disaster response, and urban monitoring tasks, the hybrid approach performs better than GA and PSO. The greatest enhancements are 6.7% greater coverage, 7.5% less average link distance, and faster convergence to optimal deployment. The proposed framework allows drones to establish strong and stable networks that are dynamic in nature and adapt to dynamic mission demands with efficient real-time coordination. This research has important applications in autonomous UAV systems for mission-critical applications where adaptability and robustness are essential. Full article

Accurate and efficient detection of road damage is crucial in traffic safety and maintenance management. Traditional road detection methods have problems such as low efficiency and insufficient accuracy, making it difficult to meet the needs of large-scale road health assessments. With the development of drone technology and computer vision, new ideas have been provided for the automatic detection of road diseases. The existing drone-based road detection methods have poor performance in dealing with complex road scenes such as vehicle occlusion, and there is still room for improvement in 3D modeling accuracy and disease detection accuracy, lacking a comprehensive and efficient solution. This paper proposes a UAV (Unmanned Aerial Vehicle)-based 3D reconstruction and large-scale disease detection method for roads. By capturing aerial images with UAVs and utilizing an improved YOLOv8 model, vehicles in the images are identified and removed. Apply MVSNet (Multi-View Stereo Network) 3D reconstruction algorithm for road surface modeling, and finally use point cloud processing and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering for disease detection. The experimental results show that this method performs excellently in terms of 3D modeling accuracy and speed. Compared with the traditional colmap method, the reconstruction speed is greatly improved, and the reconstruction density is three times that of colmap. Meanwhile, the reconstructed point cloud can effectively detect road smoothness and settlement. This study provides a new method for effective disease detection under complex road conditions, suitable for large-scale road health assessment tasks. Full article

Unmanned aerial vehicles (UAVs) are emerging as powerful tools for transporting temperature-sensitive payloads, including medical supplies, biological samples, and research materials, to remote or hard-to-reach locations. Effective thermal management is essential for maintaining payload integrity, especially during extended flights or harsh environmental conditions. This review presents a comprehensive analysis of temperature control mechanisms for UAV payloads, covering both passive and active strategies. Passive systems, such as phase-change materials and high-performance insulation, provide energy-efficient solutions for short-duration flights. In contrast, active systems, including thermoelectric cooling modules and Joule heating elements, offer precise temperature regulation for more demanding applications. We examined case studies that highlight the integration of these technologies in real-world UAV applications, such as vaccine delivery, blood sample transport, and in-flight polymerase chain reaction diagnostics. Additionally, we discussed critical design considerations, including power efficiency, payload capacity, and the impact of thermal management on flight endurance. We then presented an outlook on emerging technologies, such as hybrid power systems and smart feedback control loops, which promise to enhance UAV-based thermal management. This work aimed to guide researchers and practitioners in advancing thermal control technologies, enabling reliable, efficient, and scalable solutions for temperature-sensitive deliveries using UAVs. Full article

Forest fires have become one of the most destructive natural disasters worldwide, causing catastrophic losses, sometimes with the loss of lives. Therefore, some countries have created legislation to enforce mandatory fuel management within buffer zones in the vicinity of buildings and roads. The purpose of this study is to investigate whether inexpensive off-the-shelf drones equipped with standard RGB cameras could be used to detect the excess of trees and vegetation within those buffer zones. The methodology used in this study was the development and evaluation of a complete system, which uses AI to detect the contours of buildings and the services provided by the CHAMELEON bundles to detect trees and vegetation within buffer zones. The developed AI model is effective at detecting the building contours, with a mAP50 of 0.888. The article analyses the results obtained from two use cases: a road surrounded by dense forest and an isolated building with dense vegetation nearby. The main conclusion of this study is that off-the-shelf drones equipped with standard RGB cameras can be effective at detecting non-compliant vegetation and trees within buffer zones. This can be used to manage biomass within buffer zones, thus helping to reduce the risk of wildfire propagation in wildland–urban interfaces. Full article

In recent decades, forests have experienced an increasing trend in the number of pest outbreaks worldwide, apparently driven by strong annual variability in precipitation, higher air temperatures, and strong winds. Pest outbreaks have negative ecological, economic, and environmental impacts on forest ecosystems, such as reduced biodiversity, carbon sequestration, and overall forest health. Traditional monitoring methods of these disturbances, while accurate, are time-consuming and limited in scope. Remote sensing, particularly UAV (Unmanned Aerial Vehicle)-based technologies, offers a precise and cost effective alternative for monitoring forest health. This study evaluates the temporal and spatial progression of bark beetle damage in a fir-dominated forest in the Zao Mountains, Japan, using UAV RGB imagery and DL (Deep Learning) models (YOLO - You Only Look Ones), over a four-year period (2021–2024). Trees were classified into six health categories: Healthy, Light Damage, Medium Damage, Heavy Damage, Dead, and Fallen. The results revealed a significant decline in healthy trees, from 67.4% in 2021 to 25.6% in 2024, with a corresponding increase in damaged and dead trees. Light damage emerged as a potential early indicator of forest health decline. The DL model achieved an accuracy of 74.9% to 82.8%. The results showed the effectiveness of DL in detecting severe damage but highlighted that challenges in distinguishing between healthy and lightly damaged trees still remain. The study highlights the potential of UAV-based remote sensing and DL for monitoring forest health, providing valuable insights for targeted management interventions. However, further refinement of the classification methods is needed to improve accuracy, particularly in the precise detection of tree health categories. This approach offers a scalable solution for monitoring forest health in similar ecosystems in other subalpine areas of Japan and the world. Full article

(1) Background: This study aims to analyze the defoliation and boll opening performance of 123 upland cotton germplasm resources after spraying defoliant, using multispectral data to select relevant vegetation indices and identify germplasm resources sensitive to defoliants, providing methods for cotton variety improvement and high-quality parental resources. (2) Methods: 123 historical upland cotton germplasm resources from Xinjiang were selected, and the defoliation and boll opening of cotton leaves were investigated at 0, 4, 8, 12, 16, and 20 days after defoliant application. Simultaneously, multispectral digital images were collected using drones to obtain 12 vegetation indices. Based on defoliation rate, the optimal vegetation index was selected, and defoliant-sensitive germplasm resources were identified. (3) Results: The most significant difference in defoliation rate of cotton germplasm resources occurred 16 days after application. Cluster analysis grouped the 123 breeding materials into three categories, with Class I showing the best defoliation effect. Among the 12 vegetation indices, the Plant Senescence Reflectance Index (PSRI) has the highest correlation coefficient with the defoliation rate; and when the PSRI value is higher, the defoliation effect of the material is better. By comparing the traditional investigation method with the unmanned aerial vehicle multispectral technology, 15 cotton materials sensitive to defoliants were determined, with a defoliation rate of over 85%, a lint percentage ranging from 76.67% to 98.04%, and a PSRI value ranging from 0.1607 to 0.1984. (4) Conclusions: The study found that the vegetation index with sensitive response can be used as an effective indicator to evaluate the sensitivity of cotton breeding materials to defoliants. Using an unmanned aerial vehicle (UAV) equipped with vegetation indices for screening shows a high consistency with the manual investigation and screening method in screening excellent defoliation materials; it proves that it is feasible to screen cotton breeding materials with excellent defoliation effects using UAV multispectral technology. Full article

Object detection in unmanned aerial vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, this paper proposes AD-Det, a novel framework employing a coherent coarse-to-fine strategy that seamlessly integrates two pivotal components: adaptive small object enhancement (ASOE) and dynamic class-balanced copy–paste (DCC). ASOE utilizes a high-resolution feature map to identify and cluster regions containing small objects. These regions are subsequently enlarged and processed by a fine-grained detector. On the other hand, DCC conducts object-level resampling by dynamically pasting tail classes around the cluster centers obtained by ASOE, maintaining a dynamic memory bank for each tail class. This approach enables AD-Det to not only extract regions with small objects for precise detection but also dynamically perform reasonable resampling for tail-class objects. Consequently, AD-Det enhances the overall detection performance by addressing the challenges of scale variations and class imbalance in UAV images through a synergistic and adaptive framework. We extensively evaluate our approach on two public datasets, i.e., VisDrone and UAVDT, and demonstrate that AD-Det significantly outperforms existing competitive alternatives. Notably, AD-Det achieves a $37.5\%$ average precision (AP) on the VisDrone dataset, surpassing its counterparts by at least $3.1\%$. Full article

As unmanned aerial vehicles (UAVs) are increasingly employed across various industries, the demand for robust and accurate detection has become crucial. Light detection and ranging (LiDAR) has developed as a vital sensor technology due to its ability to provide rich 3D spatial information, particularly in applications such as security and airspace monitoring. This review systematically explores recent innovations in LiDAR-based drone detection, deeply focusing on the principles and components of LiDAR sensors, their classifications based on different parameters and scanning mechanisms, and the approaches for processing LiDAR data. The review briefly compares recent research works in LiDAR-based only and its fusion with other sensor modalities, the real-world applications of LiDAR with deep learning, as well as the major challenges in sensor fusion-based UAV detection. Full article

Traditional pesticide application methods pose systemic threats to sustainable agriculture due to inefficient spraying practices and ecological contamination. Although agricultural drones demonstrate potential to address these challenges, they face critical limitations in energy-constrained complete coverage path planning for field operations. This study proposes a novel BiLG-D3QN algorithm by integrating deep reinforcement learning with Bi-LSTM and Bi-GRU architectures, specifically designed to optimize segmented coverage path planning under payload-dependent energy consumption constraints. The methodology encompasses four components: payload-energy consumption modeling, soybean cultivation area identification using Google Earth Engine-derived spatial distribution data, raster map construction, and enhanced segmented coverage path planning implementation. Through simulation experiments, the BiLG-D3QN algorithm demonstrated superior coverage efficiency, outperforming DDQN by 13.45%, D3QN by 12.27%, Dueling DQN by 14.62%, A-Star by 15.59%, and PPO by 22.15%. Additionally, the algorithm achieved an average redundancy rate of only 2.45%, which is significantly lower than that of DDQN (18.89%), D3QN (17.59%), Dueling DQN (17.59%), A-Star (21.54%), and PPO (25.12%). These results highlight the notable advantages of the BiLG-D3QN algorithm in addressing the challenges of pesticide spraying tasks in agricultural UAV applications. Full article