Drones, Volume 7, Issue 9 (September 2023) – 53 articles

In order to overcome the influence of complex environmental disturbance factors such as nonlinear time-varying characteristics on the dynamic control performance of small fixed-wing UAVs, the nonlinear expression relationship of neural networks (NNs) is combined with the recursive least squares (RLSs) identification algorithm. This paper proposes a hybrid aerodynamic parameter identification method based on NN-RLS offline network training and online learning correction. The simulation results show that compared with the real value of the identification value obtained by this algorithm, the residual error of the moment coefficient is reduced by 69%, and the residual error of the force coefficient is reduced by 89%. Under the same identification accuracy, the identification time is shortened from the original 0.1 s to 0.01 s. Compared with traditional identification algorithms, better estimation results can be obtained. By using this algorithm to continuously update the NN model and iterate repeatedly, iterative learning for complex dynamic models can be realized, providing support for the optimization of UAV control schemes. Full article

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate moving target tracking in the presence of unknown background wind, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on a Lyapunov guidance vector field is proposed. The convergence analysis shows that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through a rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on the heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller meets the requirements of the airspeed constraint. Furthermore, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit, which is defined by the Lyapunov guidance vector field, to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results indicate that the temporal-phase-based speed controller can achieve a fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits a fast response speed and high estimation accuracy. Full article

Significant progress has been made in object tracking tasks thanks to the application of deep learning. However, current deep neural network-based object tracking methods often rely on stacking sub-modules and introducing complex structures to improve tracking accuracy. Unfortunately, these approaches are inefficient and limit the feasibility of deploying efficient trackers on drone AI devices. To address these challenges, this paper introduces ConcatTrk, a high-speed object tracking method designed specifically for drone AI devices. ConcatTrk utilizes a lightweight network architecture, enabling real-time tracking on edge devices. Specifically, the proposed method primarily uses the concatenation operation to construct its core tracking steps, including multi-scale feature fusion, intra-frame feature matching, and dynamic template updating, which aim to reduce the computational overhead of the tracker. To ensure tracking performance in UAV tracking scenarios, ConcatTrk implements a learnable feature matching operator along with a simple and efficient template constraint branch, which enables accurate tracking by discriminatively matching features and incorporating periodic template updates. Results of comprehensive experiments on popular benchmarks, including UAV123, OTB100, and LaSOT, show that ConcatTrk has achieved promising accuracy and attained a tracking speed of 41 FPS on an edge AI device, Nvidia AGX Xavier. ConcatTrk runs 8× faster than the SOTA tracker TransT while using 4.9× fewer FLOPs. Real-world tests on the drone platform have strongly validated its practicability, including real-time tracking speed, reliable accuracy, and low power consumption. Full article

Recently, unmanned aerial vehicles (UAVs) have gained significant popularity and have been extensively utilized in wireless communications. Due to the susceptibility of wireless channels to eavesdropping, interference and other security attacks, UAV communication security faces serious challenges. Therefore, novel solutions need to be investigated for handling corresponding issues. Note that the UAV with full-duplex (FD) mode can actively improve spectral efficiency, and reconfigurable intelligent surface (RIS) can enable the intelligent control of signal reflection for improving transmission quality. Accordingly, the security of UAV communications may be considerably improved by combining the two techniques mentioned above. In this paper, we investigate the performance of secure communication in urban areas, assisted by a FD UAV and an RIS, where the UAV receives sensitive information from the ground users and sends jamming signals to the ground eavesdroppers. Particularly, we propose an approach to jointly optimize the user scheduling, user transmit power, UAV jamming power, RIS phase shift, and UAV trajectory for maximizing the worst-case secrecy rate. However, the non-convexity of the problem makes it difficult to solve. Combining alternating optimization (AO), slack variable techniques, successive convex approximation (SCA), and semi-definite relaxation (SDR), we propose an effective algorithm to obtain a suboptimal solution. According to the simulation results, in contrast to other benchmark schemes, we show that our proposed algorithm can significantly improve the overall secrecy rate. Full article

Distinguishing ship identities is critical in ensuring the safety and supervision of the marine agriculture and transportation industry. In this paper, we present a comprehensive investigation and validation of the progression of ship re-identification technology within a cooperative framework predominantly governed by UAVs. Our research revolves around the creation of a ship ReID dataset, the creation of a ship ReID dataset, the development of a feature extraction network, ranking optimization, and the establishment of a ship identity re-identification system built upon the collaboration of unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). We introduce a ship ReID dataset named VesselID-700, comprising 56,069 images covering seven classes of typical ships. We also simulated the multi-angle acquisition state of UAVs to categorize the ship orientations within this dataset. To address the challenge of distinguishing between ships with small inter-class differences and large intra-class variations, we propose a fine-grained feature extraction network called FGFN. FGFN enhances the ResNet architecture with a self-attentive mechanism and generalized mean pooling. We also introduce a multi-task loss function that combines classification and triplet loss, incorporating hard sample mining. Ablation experiments on the VesselID-700 dataset demonstrate that the FGFN network achieves outstanding performance, with a Rank-1 accuracy of 89.78% and mAP of 65.72% at a state-of-the-art level. Generalization experiments on pedestrian and vehicle ReID datasets reveal that FGFN excels in recognizing other rigid body targets and diverse viewpoints. Furthermore, to further enhance the advantages of UAV-USV synergy in ship ReID performance, we propose a ranking optimization method based on the homologous fusion of multi-angle UAVs and heterologous fusion of USV-UAV collaborative architecture. This optimization leads to a significant 3% improvement in Rank-1 performance, accompanied by a 73% reduction in retrieval time cost. Full article

Relay-aided Device-to-Device (D2D) communication combining visible light communication (VLC) with radio frequency (RF) is a promising paradigm in the internet of things (IoT). Static relay limits the flexibility and maintaining connectivity of relays in Hybrid VLC/RF IoT systems. By using a drone as a relay station, it is possible to avoid obstacles such as buildings and to communicate in a line-of-sight (LoS) environment, which naturally aligns with the requirement of VLC Systems. To further support the application of VLC in the IoT, subject to the challenges imposed by the constrained coverage, the lack of flexibility, poor reliability, and connectivity, drone relay-aided D2D communication appears on the horizon and can be cost-effectively deployed for the large-scale IoT. This paper proposes a joint resource allocation and drones relay selection scheme, aiming to maximize the D2D system sum rate while ensuring the quality of service (QoS) requirements for cellular users (CUs) and D2D users (DUs). First, we construct a two-phase coalitional game to tackle the resource allocation problem, which exploits the combination of VLC and RF, as well as incorporates a greedy strategy. After that, a distributed cooperative multi-agent reinforcement learning (MARL) algorithm, called WoLF policy hill-climbing (WoLF-PHC), is proposed to address the drones relay selection problem. Moreover, to further reduce the computational complexity, we propose a lightweight neighbor–agent-based WoLF-PHC algorithm, which only utilizes historical information of neighboring DUs. Finally, we provide an in-depth theoretical analysis of the proposed schemes in terms of complexity and signaling overhead. Simulation results illustrate that the proposed schemes can effectively improve the system performance in terms of the sum rate and outage probability with respect to other outstanding algorithms. Full article

UAVs flying in complex low-altitude environments often require real-time sensing to avoid environmental obstacles. In previous approaches, UAVs have usually carried out motion planning based on primitive navigation maps such as point clouds and raster maps to achieve autonomous obstacle avoidance. However, due to the huge amount of data in these raw navigation maps and the highly discrete map information, the efficiency of solving the UAV’s real-time trajectory optimization is low, making it difficult to meet the demand for efficient online motion planning. A flight corridor is a series of unobstructed continuous areas and has convex properties. The flight corridor can be used as a simple parametric representation to characterize the safe flight space in the environment, and used as the cost of the collision term in the trajectory back-end optimization for trajectory solving, which can improve the efficiency of real-time trajectory solving and ensure flight safety. Therefore, this paper focuses on the construction of safe flight corridors for UAVs and autonomous obstacle avoidance algorithms for UAVs based on safe flight corridors, based on a rotary-wing UAV platform, and proposes a polyhedral flight corridor construction algorithm and realizes autonomous obstacle avoidance for UAVs based on the constructed flight corridors. Full article

Augmented Reality (AR) technology visualizes virtual objects in the real environment, offering users an immersive experience that enhances their spatial perception of virtual objects. This makes AR an important tool for visualization in engineering, education, and gaming. The Unmanned Aerial Vehicles’ (UAVs’) low-altitude public air route (Skyroad) is a forward-looking virtual transportation infrastructure flying over complex terrain, presenting challenges for user perception due to its invisibility. In order to achieve a 3D and intuitive visualization of Skyroad, this paper proposes an AR visualization framework based on a physical sandbox. The framework consists of four processes: reconstructing and 3D-printing a sandbox model, producing virtual scenes for UAVs Skyroad, implementing a markerless registration and tracking method, and displaying Skyroad scenes on the sandbox with GPU-based occlusion handling. With the support of the framework, a mobile application called SkyroadAR was developed. System performance tests and user questionnaires were conducted on SkyroadAR; the results showed that our approachs to tracking and occlusion provided an efficient and stable AR effect for Skyroad. This intuitive visualization is recognized by both professional and non-professional users. Full article

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

In the field of drone-based object tracking, utilization of the infrared modality can improve the robustness of the tracker in scenes with severe illumination change and occlusions and expand the applicable scene of the drone object tracking task. Inspired by the great achievements of Transformer structure in the field of RGB object tracking, we design a dual-modality object tracking network based on Transformer. To better address the problem of visible-infrared information fusion, we propose a Dual-Feature Aggregation Network that utilizes attention mechanisms in both spatial and channel dimensions to aggregate heterogeneous modality feature information. The proposed algorithm has achieved better performance by comparing with the mainstream algorithms in the drone-based dual-modality object tracking dataset VTUAV. Additionally, the algorithm is lightweight and can be easily deployed and executed on a drone edge computing platform. In summary, the proposed algorithm is mainly applicable to the field of drone dual-modality object tracking and the algorithm is optimized so that it can be deployed on the drone edge computing platform. The effectiveness of the algorithm is proved by experiments and the scope of drone object tracking is extended effectively. Full article

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

Communication using millimeter wave (mmWave) and terahertz bands between unmanned aerial vehicles (UAVs) is a crucial technology for the realization of non-terrestrial networks envisioned for Beyond 5G. While these frequency bands offer remarkably high-speed transmission capabilities of tens of Gbps and above, they possess strong directivity and limited communication range due to the requirement of high-gain antennas to compensate for substantial propagation loss. When a UAV employs radio of such a high-frequency band, the available communication time can be less than one second, and the feasibility of leveraging this ultra-narrow zone, which is only accessible for a short duration in a confined space, has not been investigated. This paper presents the theory behind the ultra-narrow zone in frequencies beyond mmWave and explores the data transfer characteristics at 60 GHz between two UAVs. We demonstrate the transmission of 120 MB of data within approximately 500 milliseconds utilizing the instantaneous proximity opportunity created as the UAVs pass each other. Additionally, we evaluate data transfer while the UAVs maintain a fixed distance, to sustain the 60 GHz link, successfully transmitting over 10 GB of data in the air with a throughput of approximately 5 Gbps. Full article

The Federal Aviation Administration introduced the concept of urban air mobility (UAM), a new three-dimensional transport system that operates with a fusion of manned/unmanned aerial vehicles on an urban or intercity scale. The rapid development of UAM has brought innovation and dynamism to many industries, especially in the field of logistics. Various types of unmanned aerial vehicles (UAVs) for use in transport logistics are being designed and produced. UAV logistics refers to the use of UAVs, usually carrying goods and parcels, to achieve route planning, identify risk perception, facilitate parcel delivery, and carry out other functions. This research provides a method for assessing the operational capacity of a UAV logistics route network. The concept of “logistics UAV route network operation capacity” is defined, and a bi-objective optimization model for assessing the route network’s operating capacity is developed. The first objective is to maximize the number of UAV logistics delivery plans that can be executed in a fixed operation time. The second objective is to minimize the total operational impedance value in a fixed operation time. To solve the bi-objective optimization model, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is utilized. A UAV logistics route network with 62 nodes is developed to assess the rationale and validity of the proposed concept. The experiments show that with an increase in operation time, the route network’s optimal operational capacity gradually increases, the convergence speed of the algorithm slows down, and the optimization magnitude gradually reduces. Two key parameters—operational safety interval and flight speed—are further analyzed in the experiments. According to the experiments, as the safety interval increases, the route network’s average operational capacity steadily diminishes, as does its sensitivity to the safety interval. The average operational capacity steadily rose with the rise in flight speed, especially when the UAV logistics flight speed was between 10 m/s and 10.5 m/s. In that range, the operational capacity of the route network was substantially impacted by the flight speed. Full article

Aedes aegypti is widely distributed worldwide and is the main vector mosquito for dengue, one of the most important infectious diseases in middle- and low-income countries. The landscape composition and vegetation cover determine appropriate environments for this mosquito to breed, and it is fundamental to define the most affordable methodology to understand these landscape variables in urban environments. The proposed methodology integrated drone technologies and traditional entomological surveillance to strengthen our knowledge about areas suitable for Ae. aegypti infestation. We included an analysis using the vegetation indexes, NDVI and NDVIRe, and their association with Ae. aegypti larvae and adults in houses from the El Vergel neighborhood Tapachula, Chiapas, Mexico. We used drone technology to obtain high-resolution photos and performed multispectral orthomosaic constructions for the data of vegetation indexes with a kernel density analysis. A negative binomial regression was performed to determine the association between the numbers of Ae. aegypti larvae and adults with the kernel density based on NDVI and NDVIRe. Medium and high values of kernel density of NDVIRe (both p-value < 0.05) and NDVI (both p-value < 0.05) were associated with a higher amount of mosquito adults per houses. The density of Ae. aegypti larvae per house did not show an association with medium and high values of NDVIRe (both p-value > 0.05) and NDVI (both p-value > 0.05). The vegetation indexes, NDVI and NDVIRe, have potential as precise predictors of Ae. aegypti adult mosquito circulation in urban environments. Drone technology can be used to map and obtain landscape characteristics associated with mosquito abundance in urban environments. Full article

The purpose of this paper is to establish a transition strategy for tilt-rotor vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) based on an optimal design method. Firstly, The flyable transition corridor was calculated based on both the UAV’s dynamic equations and its aerodynamic and dynamic characteristics. The dynamic equations of the UAV were organized into state equation forms. The initial and final value constraints of the control and state variables in the transition process were recorded, as were the constraints of the transition process. The transition strategy design problem was transformed into an optimal control problem with constraints, while the Gauss pseudospectral method (GPM) was employed to transform and solve the problem. In addition, performance indicators were designed based on the transition quality requirements for transition time, attitude stability, and control continuity. Sensitivity was analyzed according to different index terms with different dimensions and effects. Finally, the rationality of the transition strategy designed in this paper was verified according to different simulation scenarios. Full article

With the rapid development of communication technology, unmanned aerial vehicle–mobile edge computing (UAV-MEC) networks have emerged with powerful capabilities. However, existing research studies have neglected the issues involving user grouping and relay selection structures under UAV cluster-assisted communication. Therefore, in this article, we present a comprehensive communication–computing resource allocation for UAV-MEC networks. In particular, ground users make stable user groups first, and then multiple UAVs act as relays in order to assist these user groups in simultaneously uploading their tasks to the terrestrial base station at the edge server. Moreover, in order to maximize the system’s overall throughput, a more flexible and hierarchical matching relay selection algorithm is proposed in terms of matching the ground user groups and corresponding UAVs. For vulnerable users, we also propose a weighted relay selection algorithm to maximize the system performance. Furthermore, simulation results show that the proposed relay selection algorithm achieves a significant gain in comparison with the other benchmarks, and the stability of the proposed algorithms could be verified. Full article

Recent improvements in low-altitude remote sensors and image processing analysis can be utilised to support archaeological research. Over the last decade, the increased use of remote sensing sensors and their products for archaeological science and cultural heritage studies has been reported in the literature. Therefore, different spatial and spectral analysis datasets have been applied to recognise archaeological remains or map environmental changes over time. Recently, more thorough object detection approaches have been adopted by researchers for the automated detection of surface ceramics. In this study, we applied several supervised machine learning classifiers using red-green-blue (RGB) and multispectral high-resolution drone imageries over a simulated archaeological area to evaluate their performance towards semi-automatic surface ceramic detection. The overall results indicated that low-altitude remote sensing sensors and advanced image processing techniques can be innovative in archaeological research. Nevertheless, the study results also pointed out existing research limitations in the detection of surface ceramics, which affect the detection accuracy. The development of a novel, robust methodology aimed to address the “accuracy paradox” of imbalanced data samples for optimising archaeological surface ceramic detection. At the same time, this study attempted to fill a gap in the literature by blending AI methodologies for non-uniformly distributed classes. Indeed, detecting surface ceramics using RGB or multi-spectral drone imageries should be reconsidered as an ‘imbalanced data distribution’ problem. To address this paradox, novel approaches need to be developed. Full article

Benefiting from its structural simplicity and low cost, the inertial/ranging integrated navigation system is widely utilized in multi-agent applications, particularly in unmanned aerial vehicles (UAVs). As the deployment of UAVs in complex environments becomes more prevalent, accurate positioning in sparse observation scenarios has become increasingly important. In satellite-denied environments with few anchors, traditional filtering methods for positioning suffer from poor effectiveness due to the lack of constraints. This article proposes a method to enhance positioning accuracy in such environments by optimizing the inertial outputs of each UAV. The optimization process is based on the range measurements between the UAVs and a single anchor. By solving the optimization function derived using Bayesian theory, the optimized inertial outputs of the UAVs can be obtained. These optimized inertial data are then used in place of the original measurements for position estimation in the filter, resulting in improved performance. Simulation and real-world experiments validate that the proposed method can enhance UAVs’ positioning accuracy in single-anchor environments, surpassing the performance of a single optimizer or filter. Furthermore, the positions estimated by cooperative agents demonstrate higher accuracy than those estimated by individual agents, as more ranging measurements are incorporated. Full article

This paper focuses on a research project for the acquisition and post-production of digital data to create informative virtual representations and digital twins of different European Cultural Heritage sites. The goal was to establish a reliable database for a multi-scalar web platform, also accessible through extended reality (XR) tools. This initiative aims to support the promotion and management of cultural and historical monuments within the context of European Cultural Routes supported by the Council of Europe. The project involves different case studies spanning European geographic regions, such as the Upper Kama in Russia, the Valencian Routes of Jaime I in Spain, and the Gdańsk fortresses in Poland. The methodology employed in this effort primarily relies on integrated rapid survey techniques. Unmanned aerial vehicles (UAVs) and simultaneous localization and mapping (SLAM) technologies were used for data collection. These methods contribute to the creation of accurate 3D databases and models that transform the cultural routes into a digital format accessible via an informative platform. The actions presented in this paper are part of the European project “PROMETHEUS”, which is funded by the Horizon 2020 program of the European Union. The project involves collaboration between universities and enterprises, fostering inter-sectoral cooperation. Various techniques such as photographic archives, census analysis, and scan-to-BIM (building information modeling) processes are employed to develop this method further. In fact, the ultimate goal of the project is to establish a framework that can be replicated in other cultural contexts, enhancing the digital documentation and valorization of heritage sites. Full article

In recent years, the low-Earth-orbit (LEO) satellite network has achieved considerable development. Moreover, it is necessary to introduce edge computing into LEO networks, which can provide high-quality services, such as worldwide seamless low-delay computation offloading for unmanned aerial vehicles (UAVs) or user terminals and nearby remote-sensing data processing for UAVs or satellites. However, because the computation resource of the satellite is relatively scarce compared to the ground server, it is hard for a single satellite to complete massive deep neural network (DNN) inference tasks in a short time. Consequently, in this paper, we focus on the multi-satellite collaborative inference problem and propose a novel COllaborative INference algorithm for LEO edge computing called COIN-LEO. COIN-LEO manages to split the complete DNN model into several submodels consisting of some consecutive layers and deploy these submodels to several satellites for inference. We innovatively leverage deep reinforcement learning (DRL) to efficiently split the model and use a neural network (NN) to predict the time required for inference tasks of a specific submodel on a specific satellite. By implementing COIN-LEO and evaluating its performance in a highly realistic satellite-network-emulation platform, we find that our COIN-LEO outperforms baseline algorithms in terms of inference throughput, time consumed and network traffic overhead. Full article

Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which causes sluggish response and huge communication burden. According to the above challenges and requirements, this paper proposes a novel method for detecting broken strands on power lines in images captured by UAVs. The method features a multi-stage light-weight pipeline that includes power line segmentation, power line local image patch cropping, and patch classification. A power line segmentation network is designed to segment power lines from the background; thus, local image patches can be cropped along the power lines which preserve the detailed features of power lines. Subsequently, the patch classification network recognizes broken strands in the image patches. Both the power line segmentation network and the patch classification network are designed to be light-weight, enabling efficient online processing. Since the power line segmentation network can be trained with normal power line images that are easy to obtain and the compact patch classification network can be trained with relatively few positive samples using a multi-task learning strategy, the proposed method is relatively data efficient. Experimental results show that, trained on limited sample data, the proposed method can achieve an F1-score of 0.8, which is superior to current state-of-the-art object detectors. The average inference speed on an embedded computer is about 11.5 images per second. Therefore, the proposed method offers a promising solution for conducting real-time on-site power line defect detection with computing sources carried by UAVs. Full article

Drone swarms have gained a lot of popularity in recent times because, as a group, drones can perform highly intelligent tasks. Drone swarms are strongly inspired by the flocking behavior of birds, insects, and schools of fish, where all the members work in a coordinated manner to achieve a common goal. Since each drone is an independent entity, automating the control of a swarm is difficult. Previous works propose various swarming models with either centralized or distributed control. With distributed control, each drone makes its own decisions based on a small set of rules to accomplish swarm behavior, whereas in centralized control, one drone acts as the leader, who knows the final destination and the path to follow; it specifies the trajectories and velocities for the rest of the drones. Almost all the work in the area of swarming models follows Reynolds’ model, which has three basic rules. For GPS-aided settings, state-of-the-art proposals are not mature enough to handle complex environments with obstacles where primarily local decisions are taken. We propose a new set of rules and a game-theoretic method to set the values of the hyperparameters to design robust swarming algorithms for such scenarios. Similarly, the area of realistic swarming in GPS-denied environments is very sparse, and no work simultaneously handles obstacles and ensures that the drones stay in a confined zone and move along with the swarm. Our proposed solution $SmrtSwarm$ solves all of these problems. It is the first comprehensive model that enables swarming in all kinds of decentralized environments regardless of GPS signal availability and obstacles. We achieve this by using a stereo camera and a novel algorithm that quickly identifies drones in depth maps and infers their velocities and identities with reference to itself. We implement our algorithms on the Unity gaming engine and study them using exhaustive simulations. We simulate 15-node swarms and observe cohesive swarming behavior without seeing any collisions or drones drifting apart. We also implement our algorithms on a Beaglebone Black board and show that even in a GPS-denied setting, we can sustain a frame rate of 75 FPS, much more than what is required in practical settings. Full article

In emergency rescue missions, rescue teams can use UAVs and efficient path planning strategies to provide flexible rescue services for trapped people, which can improve rescue efficiency and reduce personnel risks. However, since the task environment of UAVs is usually complex, uncertain, and communication-limited, traditional path planning methods may not be able to meet practical needs. In this paper, we introduce a whale optimization algorithm into a deep Q-network and propose a path planning algorithm based on a whale-inspired deep Q-network, which enables UAVs to search for targets faster and safer in uncertain and complex environments. In particular, we first transform the UAV path planning problem into a Markov decision process. Then, we design a comprehensive reward function considering the three factors of path length, obstacle avoidance, and energy consumption. Next, we use the main framework of the deep Q-network to approximate the Q-value function by training a deep neural network. During the training phase, the whale optimization algorithm is introduced for path exploration to generate a richer action decision experience. Finally, experiments show that the proposed algorithm can enable the UAV to autonomously plan a collision-free feasible path in an uncertain environment. And compared with classic reinforcement learning algorithms, the proposed algorithm has a better performance in learning efficiency, path planning success rate, and path length. Full article

In terms of manned aircraft, pilots usually detect icing conditions by visual cues or by means of ice detector systems. If one of these cues is seen by the crew or systems detect icing conditions, they have to apply the evasive procedure as defined within the aircraft flight manual (AFM). However, as regards unmanned aircraft, there are not pilots on board and, consequently, nobody can act immediately when icing conditions occur. This article aims to propose new techniques of sending information to ground which make possible to know the aircraft performance correctly in icing conditions. For this goal, three contributions have been developed for the unmanned aircraft Milano. Since icing conditions are characterized quantitatively by the droplet size, the liquid water content, and the total air temperature, when these parameters are between certain limits ice formation on aircraft may occur. As a result of these contributions, in that moment, high-quality images of the wing leading edge, tail leading edge and meteorological probes will be captured and sent to ground making possible that remote pilots or artificial intelligent (AI) systems can follow the appropriate procedures, avoid encounters with severe icing conditions and perform real-time decision making. What is more, as information security is becoming an inseparable part of data communication, it is proposed how to embed relevant information within an image. Among the improvements included are image compression techniques and steganography methods. Full article

Measuring the surface deformation of asphalt pavement and acquiring the rutting condition is of great importance to transportation agencies. This paper proposes a rutting measuring method based on an unmanned aerial vehicle (UAV) mounted with Light Detection and Ranging (LiDAR). Firstly, an airborne LiDAR system is assembled and the data acquisition method is presented. Then, the method for point cloud processing and rut depth computation is presented and the results of field testing are discussed. Thirdly, to investigate error factors, the laser footprint positioning model is established and sensitivity analysis is conducted. Factors including flight height, LiDAR instantaneous angel, and ground inclination angle are discussed. The model was then implemented to obtain the virtual rut depth and to verify the accuracy of the field test results. The main conclusions include that the measurement error increases with the flight height, instantaneous angle, and angular resolution of the LiDAR. The inclination angle of the pavement surface has adverse impact on the measuring accuracy. The field test results show that the assembled airborne LiDAR system is more accurate when the rut depth is significant. The findings of this study pave the way for future exploration of rutting measurement with airborne LiDAR. Full article

The state-of-the-art geopositioning is the Global Navigation Satellite System (GNSS), which operates based on the satellite constellation providing positioning, navigation, and timing services. While the Global Positioning System (GPS) is widely used to position an Unmanned Aerial System (UAS), it is not always available and can be jammed, introducing operational liabilities. When the GPS signal is degraded or denied, the UAS navigation solution cannot rely on incorrect positions GPS provides, resulting in potential loss of control. This paper presents a real-time pipeline for geopositioning functionality using a down-facing monocular camera. The proposed approach is deployable using only a few initialization parameters, the most important of which is the map of the area covered by the UAS flight plan. Our pipeline consists of an offline geospatial quad-tree generation for fast information retrieval, a choice from a selection of landmark detection and matching schemes, and an attitude control mechanism that improves reference to acquired image matching. To evaluate our method, we collected several image sequences using various flight patterns with seasonal changes. The experiments demonstrate high accuracy and robustness to seasonal changes. Full article

Nowadays, the study and digitization of historical, architectural, and archaeological heritage are extremely important, covering the creation of digital twins—virtual replicas of real spaces and environments. Such reconstructions can be achieved using technologies with passive or active light sensors: laser scanners as light emitters, or photogrammetry through the creation of photographic images. As for the latter case, a distinction must be made between terrestrial and aerial shots, increasingly facilitated by the spread of UAV systems. Point clouds are aligned using georeferenced points measured with a total station. To create a faithful virtual model of the subjects, dense point clouds from a laser scanner are used to generate meshes, which are textured in high resolution from aerial and terrestrial photographs. All techniques can be integrated with each other, as demonstrated through the experiences of two case studies, each serving different purposes. The first is a detailed survey conducted for CAD representation of certain areas of Rocca Farnese in Capodimonte. The second is an instrumental survey for the creation of a realistic digital twin, aimed at providing an immersive VR experience of the archaeological area of Santa Croce in Gerusalemme in Rome. Full article

Unmanned aircraft systems (UASs) with autonomous maneuvering decision capabilities are expected to play a key role in future unmanned systems applications. While reinforcement learning has proven successful in solving UAS path planning problems in simple urban environments, it remains under-researched for some complex mountain environments. In this paper, the path planning of UAS swarm for the low-altitude rapid traverse in diverse environments is studied when facing the threats of complex terrain, radars and swarm failure. First, a UAS swarm radar detection probability is built up for evaluating the radar detection threat by a networked radar system, where the detection probability of a UAS swarm is equated to a single UAS with appropriate position and radar cross section named as the swarm virtual leader. Second, a reinforcement learning based path planning method is proposed to seek the optimal path for the swarm virtual leader which balances instantaneous reward, including detection probability and path constraints with terminal reward, including normal rate. Third, a formation optimization strategy is designed to further reduce the threat of radar detection through dynamically adjusting the formation geometry. Final, simulations in the complex environment have been carried out to evaluate the performance of the proposed method, where the path quality, task success rate and normal rate are counted as the performance indicators. Full article

Distributed electric propulsion technology has great potential and advantages in the development of drones. In this paper, to study the slipstream effect of distributed propellers, the actuator disk method was used to verify a single propeller, and the calculated thrust was in good agreement with the test results. Then, based on the actuator disk method, the influence of different installation positions on the slipstream effect was studied, and the distributed propeller layout was optimized by a genetic algorithm to improve the low-speed performance of the unmanned aerial vehicle (UAV) during the take-off phase and increase the cruise duration. The analysis results showed that the lift of the wing will be larger when the propellers are higher than the wing. The wing lift and drag of the counter-rotating are less than those of the co-rotating. Compared with the original layout, the lift coefficient of the optimized distributed propeller layout is significantly increased by 30.97%, while the lift/drag ratio is increased by 7.34%. Finally, we designed the test platform and qualitatively verified the calculated results without quantitative verification. Full article

To carry out required aid operations efficiently and effectively after an occurrence of a disaster such as an earthquake, emergency control centers must determine the effect of disasters precisely and and in a timely manner. Different kinds of data-gathering techniques can be used to collect data from disaster areas, such as sensors, cameras, and unmanned aerial vehicles (UAVs). Furthermore, data-fusion techniques can be adopted to combine the data gathered from different sources to enhance the situation awareness. Recent research and development activities on advanced air mobility (AAM) and related unmanned aerial systems (UASs) provide new opportunities. Unfortunately, designing these systems for disaster situation analysis is a challenging task due to the topological complexity of urban areas, and multiplicity and variability of the available data sources. Although there are a considerable number of research publications on data fusion, almost none of them deal with estimating the optimal set of heterogeneous data sources that provide the best effectiveness and efficiency value in determining the effect of disasters. Moreover, existing publications are generally problem- and system-specific. This article proposes a model-based novel analysis and synthesis framework to determine the optimal data fusion set among possibly many alternatives, before expensive implementation and installation activities are carried out. Full article