Drones

Beach nourishment is a soft engineering technique that is used to combat coastal erosion. To assess the efficacy of a beach nourishment program on the northwest coast of Lord Howe Island, remotely coordinated drone-based monitoring was undertaken at Lagoon Beach. Specifically, hypotheses were tested that beach nourishment could increase the dune height and the width of the beach where the sand was translocated but would not have any long-term impacts on other parts of the beach. During the beach nourishment program, sand was translocated from the north end to the south end of Lagoon Beach, where it was deposited over 2800 m². Lagoon Beach was monitored using a time series of 3D orthomosaics (2019–2021) based on orthorectified drone imagery. The data were then analysed using a robust before-after-control-impact (BACI) experimental design. Initially, a fully automated drone mapping program and permanent ground control points were set up. After this, a local drone pilot facilitated automated drone mapping for the subsequent times of sampling and transferred data to mainland researchers. As well as being more cost-effective, this approach allowed data collection to continue during Island closures due to the COVID-19 pandemic. After sand translocation, the south end of Lagoon Beach had a lower dune with more vegetation and a more expansive beach with a gentler slope than the prior arrangement. Overall, drone monitoring demonstrated the efficacy of the beach nourishment program on Lord Howe Island and highlighted the capacity for drones to deliver cost-effective data in locations that were difficult for researchers to access. Full article

Biodiversity regulates agroecosystem processes, ensuring stability. Preserving and restoring biodiversity is vital for sustainable agricultural production. Species identification and classification in plant communities are key in biodiversity studies. Remote sensing supports species identification. However, accurately identifying plant species in heterogeneous plant areas presents challenges in dataset acquisition, preparation, and model selection for image classification. This study presents a method that combines object-based supervised machine learning for dataset preparation and a pre-trained transfer learning model (EfficientNetV2) for precise plant species classification in heterogeneous areas. The methodology is based on the multi-resolution segmentation of the UAV RGB orthophoto of the plant community into multiple canopy objects, and on the classification of the plants in the orthophoto using the K-nearest neighbor (KNN) supervised machine learning algorithm. Individual plant species canopies are extracted with the ArcGIS training dataset. A pre-trained transfer learning model is then applied for classification. Test results show that the EfficientNetV2 achieves an impressive 99% classification accuracy for seven plant species. A comparative study contrasts the EfficientNetV2 model with other widely used transfer learning models: ResNet50, Xception, DenseNet121, InceptionV3, and MobileNetV2. Full article

The problem of safe navigation of a human-multi-robot system is addressed in this paper. More precisely, we propose a novel distributed algorithm to control a swarm of unmanned ground robots interacting with human operators in presence of obstacles. Contrary to many existing algorithms that consider formation control, the proposed approach results in non-rigid motion for the swarm, which more easily enables interactions with human operators and navigation in cluttered environments. Each vehicle calculates distributively and dynamically its own safety zone in which it generates a reference point to be tracked. The algorithm relies on purely geometric reasoning through the use of Voronoi partitioning and collision cones, which allows to naturally account for inter-robot, human-robot and robot-obstacle interactions. Different interaction modes have been defined from this common basis to address the following practical problems: autonomous waypoint navigation, velocity-guided motion, and follow a localized operator. The effectiveness of the algorithm is illustrated by outdoor and indoor field experiments. Full article

Conformance monitoring supports UTM safety by observing if unmanned aircraft (UA) are adhering to declared operational intent. As a supporting system, robust cooperative tracking is critical. Nevertheless, tracking systems for UAS traffic management (UTM) are in an early stage and under-standardized, and existing literature hardly addresses the problem. To bridge this gap, this study aims to probabilistically evaluate the impact of the change in tracking performances on the effectiveness of conformance monitoring. We propose a Monte Carlo simulation-based method. To ensure a realistic simulation environment, we use a hybrid software-in-the-loop (SITL) scheme. The major uncertainties contributing to the stochastic evaluation are measured separately and are integrated into the final Monte Carlo simulation. Latency tests were conducted to assess the performance of different communication technologies for cooperative tracking. Flight technical error generation via SITL simulations and navigational system error generation based on flight experiments were employed to model UA trajectory uncertainty. Based on these tests, further Monte Carlo simulations were used to study the overall impacts of various tracking key performance indicators in UTM conformance monitoring. Results suggest that the extrapolation of UA position enables quicker non-conformance detection, but introduces greater variability in detection delay, and exacerbates the incidence of nuisance alerts and missed detections, particularly when latencies are high and velocity errors are severe. Recommendations for UA position update rates of ≥1 Hz remain consistent with previous studies, as investments in increasing the update rate do not lead to corresponding improvements in conformance monitoring performance according to simulation results. Full article

Nowadays, automatic modulation classification (AMC) has become a key component of next-generation drone communication systems, which are crucial for improving communication efficiency in non-cooperative environments. The contradiction between the accuracy and efficiency of current methods hinders the practical application of AMC in drone communication systems. In this paper, we propose a real-time AMC method based on the lightweight mobile radio transformer (MobileRaT). The constructed radio transformer is trained iteratively, accompanied by pruning redundant weights based on information entropy, so it can learn robust modulation knowledge from multimodal signal representations for the AMC task. To the best of our knowledge, this is the first attempt in which the pruning technique and a lightweight transformer model are integrated and applied to processing temporal signals, ensuring AMC accuracy while also improving its inference efficiency. Finally, the experimental results—by comparing MobileRaT with a series of state-of-the-art methods based on two public datasets—have verified its superiority. Two models, MobileRaT-A and MobileRaT-B, were used to process RadioML 2018.01A and RadioML 2016.10A to achieve average AMC accuracies of 65.9% and 62.3% and the highest AMC accuracies of 98.4% and 99.2% at +18 dB and +14 dB, respectively. Ablation studies were conducted to demonstrate the robustness of MobileRaT to hyper-parameters and signal representations. All the experimental results indicate the adaptability of MobileRaT to communication conditions and that MobileRaT can be deployed on the receivers of drones to achieve air-to-air and air-to-ground cognitive communication in less demanding communication scenarios. Full article

The flight formation of unmanned aerial vehicles (UAVs) needs to be reconfigured whenever necessary to cope with complex environments and varying tasks. However, the continuity, nonlinearity and high dimensionality of the UAV formation control parameters bring significant challenges to the efficiency and safety of UAV formation reconfiguration. To this end, this paper proposes a reconfiguration strategy of the UAV formation based on a modified Artificial Bee Colony (ABC) algorithm, which ensures superior efficiency and safety level simultaneously. Specifically, we first formulate the formation reconfiguration problem minimizing the time consumed for reconfiguration under the constraints of safety and connection. Then the continuous optimization problem is discretized by using the control parameterization and time discretization (CPTD) method. Finally, we use a modified ABC algorithm to find the solution of formation reconfiguration. Extensive performance evaluations are conducted to verify the superiority of the proposed method. It is concluded that the proposed algorithm achieves a better performance than the existing approaches in literature in solving the problem of 3-D formation reconfiguration. Full article

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