Search Results (100)

The increasing complexity of the electromagnetic environment poses an increasing risk to unmanned aerial vehicles (UAVs) operating in airspaces subject to adverse electromagnetic effects. This paper investigates the potential electromagnetic interference that UAVs may encounter during flight through the lens of electromagnetic compatibility (EMC), which defines the requirements for the proper operation of UAV electronics. According to existing EMC standards, the immunity threshold for typical commercial drones is 10 V/m. However, European standards for public exposure permit electromagnetic fields and suggest that it is possible for an electromagnetic field of a mobile base station antenna to be as strong as 61 V/m. To assess drone vulnerability to its electromagnetic environment, investigation was conducted in an anechoic chamber, which determined that commercially available drones typically experience uncontrolled descent when subjected to an electric field strength of 30 V/m or higher. The primary coupling path for this interference is through the UAV’s internal cables, as induced parasitic currents perturb the motor control signals. This disruption leads to flight instability as the propellers can no longer be reliably controlled, resulting in flight instabilities. Based on a maximum effective radiated power (ERP) of 40 dBW per sector for a base station antenna, a minimum safe operating distance of 20 m was calculated. Adherence to this safe distance is therefore strongly recommended for any commercial drone operator to avoid EMI-induced flight failure. Full article

The safe operation of low-altitude UAVs is crucial for the effective utilization of low-altitude airspace, necessitating the development of appropriate risk assessment methods to evaluate the associated operational risks. However, current research primarily focuses on two-dimensional risk assessments, with limited focus on assessing risks across different heights, thus constraining the ability to guide UAV operations within three-dimensional airspace. In this study, we propose a three-dimensional airspace risk assessment method that integrates multisource data to estimate risks at various altitudes. First, we assess ground impact risks by considering factors such as population density, obstacle environment, and socioeconomic characteristics. Next, we develop a network signal evaluation model to estimate signal loss at various altitudes. Finally, we apply machine learning methods to classify multiple features to determine airspace risks at varying altitudes, resulting in a comprehensive three-dimensional risk map. The results indicate that the majority of the urban area falls within the low-risk category, accounting for approximately 84–87% of the city. High-risk regions are concentrated in central urban areas, with their proportion increasing from 5.9% at 30 m to 9.1% at 300 m. Although the overall trend remains broadly consistent across altitudes, the local variations highlight the necessity of three-dimensional risk evaluation. This three-dimensional risk map can effectively guide safe UAV operations across different altitude layers and provide valuable decision support for flight route planning. Full article

Urban drone operations are exposed to unpredictable risks, including engine failure and deliberate signal interference. A recent and ongoing disruption in Jeddah, Saudi Arabia, has seen widespread GPS spoofing that misleads devices by hundreds of kilometers, illustrating how fragile unmanned aerial vehicle (UAV) operations can become when over-reliant on GNSS-based navigation. Such disruptions highlight the urgent need for contingency planning in drone traffic management systems. This study introduces a safety-aware pre-flight path planning framework that proactively integrates emergency landing and GPS fallback options into UAV trajectory pre-flight planning. The planner considers proximity to predesignated emergency landing zones, communication coverage, and airspace restrictions, enabling UAVs to safely complete their operations. The approach is evaluated across realistic mission profiles such as delivery, inspection, and surveillance. Results show that the planner successfully maintains mission feasibility while embedding emergency readiness throughout each flight. This work contributes toward safer, failure-resilient drone integration in urban airspace, ensuring that contingency plans are proactively incorporated into path planning before the failure even occurs. Full article

Unsafe events in civil aviation increasingly arise from multi-stakeholder interactions, motivating system-level methods to quantify event risk and coupling. This study analyzes 1551 airspace unsafe-operation reports and models each report as a node with four attributes; edges capture co-occurrence based on cosine similarity, and risk is scored via an entropy-weight TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) scheme. Risk scores range 0–0.858, with 7% of nodes above 0.8 forming a high-risk tail; entropy weights emphasize recovery time and hazard level. Community detection yields three modules aligned with Controller, Resource, and User stakeholders; key nodes occur predominantly in Controller and Resource groups, with Controller nodes showing the highest betweenness. Coupling analysis using an N–K perspective and edge-based inter-stakeholder strength further highlights controller-centric links. The proposed framework objectively ranks node risk, reveals cross-stakeholder coupling patterns, and isolates structurally influential events, providing evidence to prioritize monitoring and mitigation in airspace safety management. Full article

UAV accident investigation is essential for safeguarding the fast-growing low-altitude airspace. While near-daily incidents are reported, they were rarely analyzed in depth as current inquiries remain expert-dependent and time-consuming. Because most jurisdictions mandate formal reporting only for serious injury or substantial property damage, a large proportion of minor occurrences receive no systematic investigation, resulting in persistent data gaps and hindering proactive risk management. This study explores the potential of using large language models (LLMs) to expedite UAV accident investigations by extracting human-factor insights from unstructured narrative incident reports. Despite their promise, the off-the-shelf LLMs still struggle with domain-specific reasoning in the UAV context. To address this, we developed a human factors analysis and classification system (HFACS)-guided analytical framework, which blends structured prompting with lightweight post-processing. This framework systematically guides the model through a two-stage procedure to infer operators’ unsafe acts, their latent preconditions, and the associated organizational influences and regulatory risk factors. A HFACS-labelled UAV accident corpus comprising 200 abnormal event reports with 3600 coded instances has been compiled to support evaluation. Across seven LLMs and 18 HFACS categories, macro-F1 ranged 0.58–0.76; our best configuration achieved macro-F1 0.76 (precision 0.71, recall 0.82), with representative category accuracies > 93%. Comparative assessments indicate that the prompted LLM can match, and in certain tasks surpass, human experts. The findings highlight the promise of automated human factor analysis for conducting rapid and systematic UAV accident investigations. Full article

With the continuous growth of air transportation demand, air traffic congestion in the Terminal Area has become increasingly serious. In order to assist controllers in efficiently alleviating the traffic congestion situation in the Terminal Area, this paper takes aircraft trajectory adjustment and flow control from the perspective of the Terminal Area as a starting point and proposes a congestion relief strategy based on a complex network and multi-objective optimization theory. First, a Terminal Area traffic network model is established with the approach point, departure point, waypoint, and navigation station as nodes and the flight path as edges. Next, a multi-objective optimization model that takes into account both congestion relief and reduced operating costs is constructed. Finally, an improved ant colony optimization is proposed to solve this optimization model and provide a unified approach to path planning for multiple aircraft. Finally, simulation experiments were conducted based on the airspace structure and operation of the Beijing Terminal Area. At the same time, ablation experiments were designed to compare the method in this paper with other ant colony optimizations. The experimental results show that the path planning results of the improved ant colony optimization can better alleviate the traffic congestion situation in the Terminal Area, converge faster, and reduce the risk of falling into a local optimum. Full article

Integrating Unmanned Aerial Vehicles (UAVs) into urban airspace requires a risk-aware approach to strategic flight planning and trajectory optimization, particularly for beyond-visual-line-of-sight operations. Existing regulatory frameworks impose strict restrictions and lack dynamic, trajectory-based risk assessments. This study presents a methodology to compute efficient UAV flight paths that comply with a predefined Target Level of Safety (TLS) for ground risk. An A* algorithm with an adaptive, risk-weighted cost function optimizes trajectories by balancing flight efficiency and ground risk exposure. The risk model incorporates key urban factors, including population exposure, road-traffic density and flow, sheltering effects, UAV-specific parameters, and wind conditions. The approach is validated through a large-scale simulation study using synthetic urban environments, systematically analyzing TLS compliance and the impact of UAV parameters on optimal trajectories. In a real-world case study using open urban GIS data, the method achieved a 72.2% reduction in induced ground risk compared to the direct path, while increasing the detour factor only to $1.06$ and maintaining full TLS compliance, demonstrating its practical relevance for strategic, risk-aware UAV flight planning. Full article

Precise trajectory prediction in the airspace of a high-density terminal area (TMA) is crucial for Trajectory Based Operations (TBO), but frequent aircraft interactions and maneuvering behaviors can introduce significant uncertainties. Most existing approaches use deterministic deep learning models that lack uncertainty quantification and explicit spatial awareness. To address this gap, we propose the BST-Transformer, a Bayesian spatio-temporal deep learning framework that produces probabilistic multi-step trajectory forecasts and supports probabilistic conflict alerting. The framework first extracts temporal and spatial interaction features via spatio-temporal attention encoders and then uses a Bayesian decoder with variational inference to yield trajectory distributions. Potential conflicts are evaluated by Monte Carlo sampling of the predictive distributions to produce conflict probabilities and alarm decisions. Experiments based on real SSR data from the Guangzhou TMA show that this model performs exceptionally well in improving prediction accuracy by reducing MADE 60.3% relative to a deterministic ST-Transformer with analogous reductions in horizontal and vertical errors (MADHE and MADVE), quantifying uncertainty and significantly enhancing the system’s ability to identify safety risks, and providing strong support for intelligent air traffic management with uncertainty perception capabilities. Full article

Background: In congenital diaphragmatic hernia (CDH), pulmonary hypoplasia and pulmonary hypertension are major causes of morbidity and mortality. Antenatal treatment with sildenafil has shown some promising protective effects in experimental CDH, but no systematic review has yet evaluated the preclinical evidence on this topic. Methods: PubMed and EMBASE databases were searched for studies using antenatal sildenafil in animal models of CDH. Bayesian model-averaged (BMA) meta-analysis was used to calculate Bayes factors (BFs). The BF₁₀ is the ratio of the probability of the data under the alternative hypothesis (presence of effect) over the probability of the data under the null hypothesis (absence of effect). Risk of bias was assessed by the SYRCLE tool. Results: We included 18 studies (14 nitrofen and 4 surgical CDH). The BMA analysis showed inconclusive evidence (BF₁₀ between 0.33 and 3) for the presence of an effect of sildenafil in fetal survival (7 studies, BF₁₀ = 1.25) or in lung hypoplasia as assessed by the lung-to-body weight ratio (16 studies, BF₁₀ = 2.04). In contrast, the BMA analysis showed conclusive evidence (BF₁₀ > 3) in favor of a positive effect of sildenafil on small pulmonary arteries medial wall thickness (12 studies, BF₁₀ = 1499), radial alveolar count (6 studies, BF₁₀ = 167.57), interalveolar septa thickness (4 studies, BF₁₀ = 56.86), distal airway complexity (3 studies, BF₁₀ = 7.95), mean saccular airspace diameter (2 studies, BF₁₀ = 7.61), total lung capacity (2 studies, BF₁₀ = 6.91), lung compliance (2 studies, BF₁₀ = 5.19), and VEGF expression (5 studies, BF₁₀ = 10.62). Conclusions: In preclinical models of CDH, antenatal sildenafil rescues pulmonary vascular remodeling and airway/airspace morphometric alterations. Full article

With the rapid development of urban air mobility, achieving safe and segregated flight for unmanned aerial vehicles amid the surging demand for low-altitude logistics has become a critical issue. This paper proposes a method for planning the public route network of urban low-altitude terminal logistics while considering environmental risks in the digital airspace. First, based on parallel system theory, we develop a digital airspace environment model that supports public route network planning by mapping physical and social elements to an artificial system. Furthermore, we establish a digital airspace grid partitioning system, develop grid access rules, and create a quantification model for urban low-altitude airspace environmental risks. Utilizing a layered airspace approach, this paper configures approach–departure grids, develops methods for initial public route network planning, and facilitates orderly re-planning of routes, ultimately establishing a hub-and-spoke public route network with segregation. This study conducts detailed case simulation studies based on realistic constraints, focusing on environmental risk, accurate grid configuration, comprehensive cost, algorithm complexity, and network scale. Simulation results demonstrate that the proposed method effectively constructs conflict-free networks, while maintaining low risks and inflection points. The findings align with the current development stage of urban air mobility characterized by the principle of ‘isolation first, then integration.’ This approach enables a gradual transition from route isolation to future integrated flight, thereby providing technical support for advancing low-altitude logistics operations. Full article

Construction safety is critical, and unmanned aerial vehicles (UAVs) have emerged as a transformative tool to enhance safety management in the sector. While UAVs are widely recognized for their efficacy, limited research has specifically addressed the barriers to their integration into construction safety management systems. This study aims to identify, prioritize, and analyze the interrelationships among these barriers to aid in their effective resolution. Using a mixed-methods approach, this research combines a systematic literature review (SLR) to identify barriers and a questionnaire survey to prioritize and examine their interconnections. The findings reveal significant barriers, including restricted airspace, inadequate safety regulations, limited flight durations, collision risks, insufficient piloting skills, lack of UAV awareness, resistance to new technologies, human errors, training needs, and legal constraints. Restricted airspace emerged as the most critical barrier, strongly linked to flight duration limitations and piloting proficiency. This study also highlights regional disparities: respondents from developed nations emphasized collision risks, legal restrictions, and resistance to new technologies, while those from developing countries focused on restricted areas, limited flight time, and piloting expertise. These findings emphasize the importance of addressing region-specific challenges and tailoring strategies to facilitate UAV integration, paving the way for safer and more efficient construction practices. Full article

Background: Collapse of the lateral pharyngeal wall (LPW) in the pathogenesis of OSA appears to be the only independent risk factor for OSA. Therefore, since 2003, when Cahali first published the technique of lateral pharyngoplasty, several surgical techniques targeting the LPW have been described. Central to these is the concept of widening and stabilizing the pharyngeal airspace by treating the collapse of the LPW rather than removing the redundant pharyngeal soft tissue. The advent of robotic surgery has led to the development of new techniques in OSA surgery, the main target of which is the base of the tongue. Pharyngoplasty using robotic technology can be beneficial when this procedure is combined with tongue base reduction, which is known to be best performed with robotic surgery. Methods: This article presents a new technique for functional expansion pharyngoplasty (FEP), which is a modification of the functional expansion pharyngoplasty previously described by Sorrenti and Piccin and is performed using robotic surgery with a Da Vinci system. Results and Conclusions: Transoral robotic functional expansion pharyngoplasty is an effective, standardizable technique for treating OSA, notable for its ease of learning and performing. Full article

Urban wind, and particularly turbulence present in the roughness zone near structures, poses a critical challenge for next-generation drones. Complex flow patterns induced by large buildings produce significant disturbances that the vehicle must reject at low altitudes. Traditional turbulence models, such as the von Kármán model, underestimate these localized effects, compromising flight safety. To address this gap, we integrate high-resolution time and spatially varying urban wind fields from Large Eddy Simulations into a flight dynamics simulation framework using vehicle plant models based on configuration geometry and commonly deployed Ardupilot control laws, enabling a detailed analysis of drone responses in urban environments. Our results reveal that high-risk flight zones can be systematically identified by correlating drone response metrics with the spatial distribution of Turbulent Kinetic Energy (TKE). Notably, maximum g-loads coincide with abrupt TKE transitions, underscoring the critical impact of even short-lived wind fluctuations. By coupling advanced computational fluid dynamics with a real-time vehicle dynamics model, this work establishes a foundational methodology for designing safer and more reliable advanced air mobility platforms in complex urban airspaces. This work distinguishes itself from the existing literature by incorporating an efficient vortex lattice aerodynamic solver that supports arbitrary fixed-wing drone platforms through the simple specification of planform geometry and mass properties, and operating full-flights throughout a time and spatially varying urban wind field. This framework enables a robust assessment of stability and control for a wide range of fixed-wing drone platforms operating in urban environments, with delivery drones serving as a representative and practical use case. Full article

The vigorous development of urban air mobility (UAM) is reshaping the urban travel landscape, but it also poses severe challenges to the safe and efficient operation of dense and complex airspace. Potential conflicts between flight plans have become a core bottleneck restricting its development. Traditional flight plan adjustment and management methods often rely on deterministic trajectory predictions, ignoring the inherent temporal uncertainties in actual operations, which may lead to the underestimation of potential risks. Meanwhile, existing global optimization strategies often face issues of inefficiency and overly broad adjustment scopes when dealing with large-scale plan conflicts. To address these challenges, this study proposes an innovative flight plan conflict management framework. First, by introducing a probabilistic model of flight time errors, a new conflict detection mechanism based on confidence intervals is constructed, significantly enhancing the ability to foresee non-obvious conflict risks. Furthermore, based on complex network theory, the framework accurately identifies a small number of “critical flight plans” that play a core role in the conflict network, revealing their key impact on chain reactions of conflicts. On this basis, a phased optimization strategy is adopted, prioritizing the adjustment of spatiotemporal parameters (departure time and speed) for these critical plans to systematically resolve most conflicts. Subsequently, only fine-tuning the speeds of non-critical plans is required to address remaining local conflicts, thereby minimizing interference with the overall operational order. Simulation results demonstrate that this framework not only significantly improves the comprehensiveness of conflict detection but also effectively reduces the total number of conflicts. Additionally, the proposed phased artificial lemming algorithm (ALA) outperforms traditional optimization algorithms in terms of solution quality. This work provides an important theoretical foundation and a practically valuable solution for developing robust and efficient UAM dynamic scheduling systems, holding promise to support the safe and orderly operation of large-scale urban air traffic in the future. Full article

This study aimed to assess the potential operational implications of integrating Advanced Air Mobility (AAM) traffic at the Orlando International Airport (MCO) Class Bravo airspace. Researchers developed corridor prototypes within MCO’s airspace to analyze potential traffic conflicts and wake turbulence risks between MCO’s commercial and AAM traffic. Furthermore, an AAM ecosystem at MCO was developed to enable the simultaneous integration of realistic MCO and AAM traffic paths. The ecosystem was created on a series of operational assumptions derived from the FAA’s AAM implementation plans and concepts of operation. The findings of this study revealed that the AAM ecosystem (corridor designs and operational schedule) had little to no impact on existing commercial air traffic operations based on the assumptions made for this analysis. Additionally, the assessment revealed that integrating 22 aircraft/airframes could result in an efficient operational infrastructure with no traffic or wake turbulence conflicts with existing commercial air traffic at MCO. This groundbreaking study marks one of the initial evaluations of AAM integration at a major international airport in the United States. Full article