Search Results (9,031)

The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing is limited by the penetration of sunlight into the canopy depth, and thus directly estimating winter wheat stem biomass via canopy spectra remains a difficult task. There is a stable linear relationship between the stem dry biomass (SDB) and leaf dry biomass (LDB) of winter wheat during the entire growth stage. Therefore, this study comprehensively considered remote sensing and crop phenology, as well as biomass allocation laws, to establish a novel two-component (LDB, SDB) and two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) to estimate SDB across the growth stages of winter wheat. The core of the Tc/Tp-SDB model employed phenological variables (e.g., effective accumulative temperature, EAT) to correct the SDB estimations determined from the LDB. In particular, LDB was estimated using spectral vegetation indices (e.g., red-edge chlorophyll index, CI_{red edge}). The results revealed that the coefficient values (β₀ and β₁) of ordinary least squares regression (OLSR) of SDB with LDB had a strong relationship with phenological variables. These coefficient (β₀ and β₁) relationships were used to correct the OLSR model parameters based on the calculated phenological variables. The EAT and CI_{red edge} were determined as the optimal parameters for predicting SDB with the novel Tc/Tp-SDB model, with r, RMSE, MAE, and distance between indices of simulation and observation (DISO) values of 0.85, 1.28 t/ha, 0.95 t/ha, and 0.31, respectively. The estimation error of SDB showed an increasing trend from the jointing to flowering stages. Moreover, the proposed model showed good potential for estimating SDB from UAV hyperspectral imagery. This study demonstrates the ability of the Tc/Tp-SDB model to accurately estimate SDB across different growing seasons and growth stages of winter wheat. Full article

Unmanned aerial vehicles (UAVs) play pivotal roles in various applications, from surveillance to delivery services. Efficient path planning for UAVs in dynamic environments with obstacles and moving landing stations is essential to ensure safe and reliable operations. In this study, we propose a novel approach that combines the A* algorithm with the grey wolf optimizer (GWO) for path planning, referred to as GW-A*. Our approach enhances the traditional A algorithm by incorporating weighted nodes, where the weights are determined based on the distance from obstacles and further optimized using GWO. A simulation using dynamic factors such as wind direction and wind speed, which affect the quadrotor UAV in the presence of obstacles, was used to test the new approach, and we compared it with the A* algorithm using various heuristics. The results showed that GW-A* outperformed A* in most scenarios with high and low wind speeds, offering more efficient paths and greater adaptability. Full article

This review discusses the challenges of integrating emerging transportation technologies into existing urban environments, considering their impact on equity, sustainability, and urban design. The aim is to provide readers with strategic insights and policy recommendations for incorporating aerospace innovations into transportation systems. This narrative review draws on a wide range of publications, including books, journal articles, and industry reports, to examine the multifaceted aspects of urban aviation. The review explores the scales of aerospace transport, detailing the technologies enabling urban aviation, the necessary urban adaptations to support such a system, and the social and regulatory challenges of integrating urban air mobility into existing transportation networks. The research suggests that for urban air mobility to be successfully integrated into existing transportation systems, further research is needed on the social and regulatory implications, particularly regarding equitable access, sustainable practices, and community engagement. Full article

Asa result of the time lag in transmission, the data obtained by the sensor is delayed and does not reflect the state at the current moment. The effects of input delay are often overlooked in active inference (AIF), which may lead to significant deviations in state estimation and increased prediction errors, particularly when the system is subjected to a sudden external stimulus. In this paper, a theoretical framework of delayed feedback active inference (DAIF) is proposed to enhance the applicability of AIF to real systems. The probability model of DAIF is defined by incorporating a control distribution into that of AIF. The free energy of DAIF is defined as the sum of the quadratic state, sense, and control prediction error. A predicted state derived from previous states is defined and introduced as the expectation of the prior distribution of the real-time state. A proportional-integral (PI)-like control based on the predicted state is taken to be the expectation of DAIF preference control, whose gain coefficient is inversely proportional to the measurement accuracy variance. To adaptively compensate for external disturbances, a second-order inverse variance accuracy replaces the fixed sensory accuracy of preference control. The simulation results of the trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV) show that DAIF performs better than AIF in state estimation and disturbance resistance. Full article

The proliferation of low-cost, small radar cross-section UAVs (unmanned aerial vehicles) necessitates innovative solutions for countering them. Since these UAVs typically operate with a radio control link, a promising defense technique involves passive scanning of the radio frequency (RF) spectrum to detect UAV control signals. This approach is enhanced when integrated with machine-learning (ML) and deep-learning (DL) methods. Currently, this field is actively researched, with various studies proposing different ML/DL architectures competing for optimal accuracy. However, there is a notable gap regarding robustness, which refers to a UAV detector’s ability to maintain high accuracy across diverse scenarios, rather than excelling in just one specific test scenario and failing in others. This aspect is critical, as inaccuracies in UAV detection could lead to severe consequences. In this work, we introduce a new dataset specifically designed to test for robustness. Instead of the existing approach of extracting the test data from the same pool as the training data, we allowed for multiple categories of test data based on channel conditions. Utilizing existing UAV detectors, we found that although coefficient classifiers have outperformed CNNs in previous works, our findings indicate that image classifiers exhibit approximately 40% greater robustness than coefficient classifiers under low signal-to-noise ratio (SNR) conditions. Specifically, the CNN classifier demonstrated sustained accuracy in various RF channel conditions not included in the training set, whereas the coefficient classifier exhibited partial or complete failure depending on channel characteristics. Full article

As a novel mode of urban air mobility (UAM), unmanned aerial vehicles (UAVs) pose a great amount of risk to ground people. Assessing ground risk and mitigation effects correctly is a focused issue. This paper proposes a grid-based risk matrix framework for assessing the ground risk associated with two types of UAVs, namely fixed-wing and quadrotor. The framework has a three-stage structure of “intrinsic risk assessment—mitigation effect—final map generation”. First, the intrinsic risk to ground populations caused by potential UAV crashes is quantified. Second, the mitigation effects are measured by establishing a mathematical model with a focus on the ground sheltering and parachute systems. Finally, a modular approach is presented for generating a ground risk map of UAVs, aiming to effectively characterize the effects of each influencing factor on the failure process of UAVs. The framework facilitates the modular analysis and quantification of the impact of diverse risk factors on UAV ground risk. It also provides a new perspective for analyzing ground risk mitigation measures, such as ground sheltering and UAV parachute systems. A case study experiment on a realistic urban environment in Shenzhen shows that the risk map generated by the presented framework can accurately characterize the distribution of ground risk posed by various UAVs. Full article

A key aspect in evaluating the performance of a UAV or its swarm is reliability. The reliability is calculated based on various mathematical models. Traditionally, Binary-State System (BSS) models, which assess two states—operational and faulty—are employed. However, some studies suggest using a Multi-State System (MSS) model, which allows for a detailed analysis by considering multiple states beyond just operational and faulty. Both mathematical models allow for the evaluation of Unmanned Aerial Vehicle (UAV) swarms based on availability, which is considered as a probability of swarm mission implementation. There is one more similar assessment computed based on MSS, which is named the probabilities of the performance level. There are not any recommendations for applications of these mathematical models and assessments for reliability analyses of UAV swarms. This paper introduces a comparative study on the availability of UAV swarms using both BSS and MSS models and the probability of performance levels of UAV swarms. This study provides quantitative and qualitative recommendations to exploit these mathematical models and assessments for UAV swarms according to computational complexity and informativeness. The comparative analysis shows that the evaluation of UAV swarm failure should be based on BSS, and the analysis of operation states should be implemented based on probabilities’ performance levels instead of swarm availability. These results are confirmed by quantitative and statistical examinations of UAV swarms of different types based on both BSS and MSS. The number of UAVs is changed from 2 to 20 in these examinations. Full article

A passive flow control technique in the form of microfiber coatings with a diverging pillar cross-section area was applied to the wing suction surface of a small tailless unmanned aerial vehicle (UAV). The coatings are inspired from ‘gecko feet’ surfaces, and their impact on steady and unsteady aerodynamics is assessed through wind tunnel testing. Angles of attack from −2° to 17° were used for static experiments, and for some cases, the elevon control surface was deflected to study its effectiveness. In forced oscillation, various combinations of mean angle of attack, frequency and amplitude were explored. The aerodynamic coefficients were calculated from load cell measurements for experimental variables such as microfiber size, the region of the wing coated with microfibers, Reynolds number and angle of attack. Microfibers with a 140 µm pillar height reduce drag by a maximum of 24.7% in a high-lift condition and cruise regime, while 70 µm microfibers work best in the stall flow regime, reducing the drag by 24.2% for the same high-lift condition. Elevon deflection experiments showed that pitch moment authority is significantly improved near stall when microfibers cover the control surface and upstream, with an increase in CM magnitude of up to 22.4%. Dynamic experiments showed that microfibers marginally increase dynamic damping in pitch, improving load factor production in response to control surface actuation at low angles of attack, but reducing it at higher angles. In general, the microfiber pillars are within the laminar boundary layer, and they create a periodic slip condition on the top surface of the pillars, which increases the near-wall momentum over the wing surface. This mechanism is particularly effective in mitigating flow separation at high angles of attack, reducing pressure drag and restoring pitching moment authority provided by control surfaces. Full article

Constructing high-precision subsidence basins is of paramount importance for mining subsidence monitoring. Traditional unmanned aerial vehicle (UAV) photogrammetry techniques typically construct subsidence basins by directly differencing digital elevation models (DEMs) from different monitoring periods. However, this method often neglects the influence of horizontal displacement on the accuracy of the subsidence basin. Taking a mining area in Ordos, Inner Mongolia, as an example, this study employed the normalized cross-correlation (NCC) matching algorithm to extract horizontal displacement information between two epochs of a digital orthophoto map (DOM) and subsequently corrected the horizontal position of the second-epoch DEM. This ensured that the planar positions of ground feature points remained consistent in the DEM before and after subsidence. Based on this, the vertical displacement in the subsidence area (the subsidence basin) was obtained via DEM differencing, and the parameters of the post-correction subsidence basin were inverted using the probability integral method (PIM). The experimental results indicate that (1) the horizontal displacement was influenced by the gully topography, causing the displacement within the working face to be segmented on both sides of the gully; (2) the influence of the terrain on the subsidence basin was significantly reduced after correction; (3) the post-correction surface subsidence curve was smoother than the pre-correction curve, with abrupt error effects markedly diminished; (4) the accuracy of the post-correction subsidence basin increased by 43.12% compared with the total station data; and (5) comparing the measured horizontal displacement curve with that derived using the probability integral method revealed that the horizontal displacement on the side of an old goaf adjacent to the newly excavated working face shifted toward the advancing direction of the new working face as mining progressed. This study provides a novel approach and insights for using low-cost UAVs to construct high-precision subsidence basins. Full article

This paper presents LEVIOSA, a novel framework for text- and speech-based uncrewed aerial vehicle (UAV) trajectory generation. By leveraging multimodal large language models (LLMs) to interpret natural language commands, the system converts text and audio inputs into executable flight paths for UAV swarms. The approach aims to simplify the complex task of multi-UAV trajectory generation, which has significant applications in fields such as search and rescue, agriculture, infrastructure inspection, and entertainment. The framework involves two key innovations: a multi-critic consensus mechanism to evaluate trajectory quality and a hierarchical prompt structuring for improved task execution. The innovations ensure fidelity to user goals. The framework integrates several multimodal LLMs for high-level planning, converting natural language inputs into 3D waypoints that guide UAV movements and per-UAV low-level controllers to control each UAV in executing its assigned 3D waypoint path based on the high-level plan. The methodology was tested on various trajectory types with promising accuracy, synchronization, and collision avoidance results. The findings pave the way for more intuitive human–robot interactions and advanced multi-UAV coordination. Full article

This paper introduces the mixed-strategy Harris hawk optimization (MSHHO) algorithm as an enhancement to address the limitations of the conventional Harris hawk optimization (HHO) algorithm in solving complex optimization problems. HHO often faces challenges such as susceptibility to local optima, slow convergence, and inadequate precision in global solution-seeking. MSHHO integrates four innovative strategies to bolster HHO’s effectiveness in both local exploitation and global exploration. These include a positive charge repulsion strategy for diverse population initialization, a nonlinear decreasing parameter to heighten competitiveness, the introduction of Gaussian random walk, and mutual benefit-based position updates to enhance mobility and escape local optima. Empirical validation on 12 benchmark functions from CEC2005 and comparison with 10 established algorithms affirm MSHHO’s superior performance. Applications to three real-world engineering problems and UAV flight trajectory optimization further demonstrate MSHHO’s efficacy in overcoming complex optimization challenges. This study underscores MSHHO as a robust framework with enhanced global exploration capabilities, significantly improving convergence accuracy and speed in engineering applications. Full article

Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate of the system by using interference technology to enhance it. To this end, the system not only deploys multiple UAV base stations (BSs) to provide services to legitimate users but also assigns dedicated UAV jammers to send interference signals to active or potential eavesdroppers to disrupt their eavesdropping effectiveness. Based on this configuration, we formulate the optimization process of parameters such as the user association variables, UAV trajectory, and output power as a sequential decision-making problem and use the single-agent soft actor-critic (SAC) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm to achieve joint optimization of the core parameters. In addition, for specific scenarios, we also use the multi-agent soft actor-critic (MASAC) algorithm to solve the joint optimization problem mentioned above. The numerical results show that the normalized average secrecy rate of the MASAC algorithm increased by more than 6.6% and 14.2% compared with that of the SAC and TD3 algorithms, respectively. Full article

Unmanned aerial vehicles (UAVs) have become essential tools with diverse applications across multiple sectors, including remote sensing. This paper presents a trajectory planning method for a team of UAVs aimed at enhancing covert video sensing in uneven terrains and urban environments. The approach establishes a feasible flight zone, which dynamically adjusts to accommodate line of sight (LoS) occlusions caused by elevated terrains and structures between the UAVs’ sensors and the target. By avoiding ‘shadows’—projections of realistic shapes on the UAVs’ operational plane that represent buildings and other obstacles—the method ensures continuous target visibility. This strategy optimizes UAV trajectories, maintaining covertness while adapting to the changing environment, thereby improving overall video sensing performance. The method’s effectiveness is validated through comprehensive MATLAB simulations at both single and multiple UAV levels, demonstrating its ability to prevent LoS occlusions while preserving a high level of camouflage. Full article

Weeds in paddy fields compete with rice for nutrients and cause pests and diseases, greatly affecting rice yield. Accurate weed detection is vital for implementing variable spraying with unmanned aerial vehicles (UAV) for weed control. Therefore, this paper presents an improved weed detection algorithm, YOLOv10n-FCDS (YOLOv10n with FasterNet, CGBlock, Dysample, and Structure of Lightweight Detection Head), using UAV images of Sagittaria trifolia in rice fields as the research object, to address challenges like the detection of small targets, obscured weeds and weeds similar to rice. We enhanced the YOLOv10n model by incorporating FasterNet as the backbone for better small target detection. CGBlock replaced standard convolution and SCDown modules to improve the detection ability of obscured weeds, while DySample enhanced discrimination between weeds and rice. Additionally, we proposed a lightweight detection head based on shared convolution and scale scaling, maintaining accuracy while reducing model parameters. Ablation studies revealed that YOLOv10n-FCDS achieved a 2.6% increase in mean average precision at intersection over union 50% for weed detection, reaching 87.4%. The model also improved small target detection (increasing mAP50 by 2.5%), obscured weed detection (increasing mAP50 by 2.8%), and similar weed detection (increasing mAP50 by 3.0%). In conclusion, YOLOv10n-FCDS enables effective weed detection, supporting variable spraying applications by UAVs in rice fields. Full article

Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection. Full article