Search Results (130)

Accurate prediction of drone motion within structured urban air corridors is essential for ensuring safe and efficient operations in Urban Air Mobility (UAM) systems. Although real-world Remote Identification (Remote ID) regulations require drones to broadcast critical flight information such as velocity, access to large-scale, high-quality broadcast data remains limited. To address this, this study leverages a Digital Twin (DT) framework to augment Remote ID spatio-temporal broadcasts, emulating the sensing environment of dense urban airspace. Using Remote ID data, we propose BiDGCNLLM, a hybrid prediction framework that integrates a Bidirectional Graph Convolutional Network (BiGCN) with Dynamic Edge Weighting and a reprogrammed Large Language Model (LLM, Qwen2.5–0.5B) to capture spatial dependencies and temporal patterns in drone speed trajectories. The model forecasts near-future speed variations in surrounding drones, supporting proactive conflict avoidance in constrained air corridors. Results from the AirSUMO co-simulation platform and a DT replica of the Cranfield University campus show that BiDGCNLLM outperforms state-of-the-art time series models in short-term velocity prediction. Compared to Transformer-LSTM, BiDGCNLLM marginally improves the $R^2$ by 11.59%. This study introduces the integration of LLMs into dynamic graph-based drone prediction. It shows the potential of Remote ID broadcasts to enable scalable, real-time airspace safety solutions in UAM. Full article

This paper proposes a global, predefined time control method based on a predefined time disturbance observer to address the issues of wide flight airspace, large aerodynamic deviations, and high precision requirements for the entire process of aerospace aircraft re-entry. Firstly, this method proposes an adjustable predefined time nonsingular sliding mode disturbance observer, which can not only accurately estimate the modeling uncertainty and external aerodynamic disturbances of the aerospace aircraft, but also quickly converge while suppressing chattering. Then, based on the disturbance observation results, combined with a new performance function and nonsingular predefined-time sliding mode, a global predefined-time controller suitable for any order system was designed. Unlike existing methods that can only ensure that the initial deviation converges to the deviation boundary within a predefined time and then remains within the deviation boundary, it can ensure that any deviation generated within the error boundary also converges within the predefined time. Finally, the effectiveness and superiority of the proposed control scheme were verified through comparative simulation. Full article

Urban Air Mobility (UAM) requires reliable communication and surveillance infrastructures to ensure safe Unmanned Aerial Vehicle (UAV) operations in dense metropolitan environments. However, urban infrastructure is inherently heterogeneous, leading to significant spatial variations in monitoring performance. This study proposes a unified framework that integrates infrastructure readiness assessment with Deep Reinforcement Learning (DRL)-based UAV path planning. Using Singapore as a representative case, we employ a data-driven methodology combining clustering analysis and in situ measurements to estimate the citywide distribution of surveillance quality. We then introduce an infrastructure-aware path planning algorithm based on a Double Deep Q-Network (DQN) with a convolutional architecture, which enables UAVs to learn efficient trajectories while avoiding surveillance blind zones. Extensive simulations demonstrate that the proposed approach significantly improves path success rates, reduces traversal through poorly monitored regions, and maintains high navigation efficiency. These results highlight the potential of combining infrastructure modeling with DRL to support performance-aware airspace operations and inform future UAM governance systems. Full article

The integration of unmanned aerial vehicles (UAVs) into urban air mobility (UAM) systems necessitates reliable and uninterrupted communication infrastructure to ensure safety, control, and data continuity within designated air corridors. This paper proposes and evaluates four radio repeater deployment strategies to support robust UAV communication in urban environments: Strategy 1 with non-overlapping radio coverage, Strategy 2 with fully overlapping coverage zones, Strategy 3 with alternating redundancy between repeater pairs, and Strategy 4 with full duplication of overlapping coverage. A continuous-time Markov modeling approach is employed to quantify communication availability under varying traffic loads and failure conditions. The strategies are assessed based on infrastructure requirements, reliability performance, and suitability for segmented and non-linear corridor geometries. The results show that increasing redundancy significantly improves reliability: for example, channel unavailability drops from 35% under Strategy 1 (no redundancy) to less than 0.5% under Strategy 4 (full duplication). Strategy 3 achieves a balanced performance, maintaining unavailability below 1% with approximately 50% fewer resources than Strategy 4. A case study in the Greenline district of Astana, Kazakhstan, illustrates the practical application of the framework, demonstrating how hybrid deployment strategies can address different operational and environmental demands. The results show that increasing redundancy significantly enhances availability, with Strategy 3 offering the most efficient balance between reliability and resource use. The proposed methodology provides a scalable foundation for designing resilient UAV communication systems to support future urban airspace operations. Full article

The growth of air transportation volume and increasing requirements for efficiency require the improvement of algorithms for planning optimal aircraft flight routes. Traditional methods, such as the A*, B*, D* and Dijkstra algorithms, are widely used in navigation systems, but they have a number of limitations when applied in a dynamically changing environment, in particular due to the need to take into account weather conditions, air traffic, economic factors, and aircraft characteristics. This article provides a comprehensive analysis of existing approaches to optimizing airline routes, the advantages and disadvantages of each, and possible areas for their improvement. Particular attention is paid to multi-criteria parameters that affect routing efficiency, such as fuel consumption, safety aspects, forecasting accuracy, and adaptation to changing flight conditions. A methodological solution is proposed to improve route construction algorithms, which involves taking into account variable parameters in real time and integrating them into modern navigation systems. In addition, optimal flight paths were modeled using the improved algorithms, which allow for increasing the efficiency of decision-making in the field of air traffic control. The results of the study can be useful for airline companies, airspace authorities, and navigation software developers. Full article

With increasing air traffic, the workload of air traffic controllers (ATCOs) and their limited number is again a restricting factor for the evolution of airspace management. Currently, possibilities to apply artificial intelligence for improving the support of ATCOs are widely discussed. By introducing a digital ATCO as a team partner for a human ATCO, we can expand capabilities. It can be trained to manage traffic across various airspace sectors without the limitations imposed by required licenses. This way, shortages of human ATCOs may be absorbed, and flexible assignment to sectors is facilitated with a digital ATCO partner. To be effective, the digital ATCO needs an understanding of current and future traffic situations to share the situation awareness of the human ATCO. The goal is to equip the digital ATCO with a comparable understanding—referred as a “mental model”—of the traffic situation and human actions, thereby improving decision-making and build up adequate trust with humans. In this work, decisive factors of traffic and management for the creation of digital situation awareness are identified and examined for their relevance and applicability for digital ATCOs. Within this study, a data-driven process of building up digital situation awareness including the influencing factors are suggested, and the usability of factors like the airspace complexity for indicating digital situation awareness are proposed. Finally, an example is presented and discussed to showcase our approach with focus on the integration of digital and human ATCOs through shared situation awareness. Full article

This paper introduces ACCORD, a support platform designed to digitalize and automate the coordination processes required by the current drone regulatory framework. Drone operators must complete several coordination actions with both aeronautical and non-aeronautical entities. Traditional aeronautical coordination actions relate to the need to access protected airspace volumes around airports. Additional coordination should be established with smaller aeronautical infrastructures, like small aerodromes and heliports, which are not surrounded by any type of pre-defined airspace. Therefore, drone-specific protection volumes have been created. ACCORD enables a single entry point for all the necessary coordination processes for drone operators and infrastructure managers. The objective is to minimize the number of required actions, guarantee full traceability of the process, maximize access to the relevant information, automate the processes as much as possible, and maintain a high level of flexibility to support all coordination processes. After coordination is established, it moves from the strategic/planning phase to the actual execution phase. ACCORD also enables a communication mechanism between the drone operators and the aeronautical infrastructures to extend the coordination to the actual mission execution. ACCORD is currently being tested by some of the most relevant actors in the Catalan drone ecosystem. The current version of the system provides support for all types of aeronautical infrastructures (heliports, aerodromes, and airports) and management duality for situations in which the infrastructure manager and the aeronautical service provider coexist. 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

Congested airspace conflict resolution during terminal operations is a common air traffic management issue that may produce cascading delays. Vehicles needing emergency clearance to land, at either traditional airports or vertiports, would require others on approach to move out of the way and, in some instances, cause a wave of delay to propagate through all vehicles on approach. Specifically, uncrewed aerial systems utilizing near-maximum arrival rates would be greatly impacted when requested to move off their approach path and may interfere with others. Vertiports further complicate crowded approaches because vehicles can arrive from many different angles at the same time to maximize landing area usage. Traditional air traffic management techniques were studied for vertiport applications specific to high-capacity operations. This work investigated methods of uniformly re-directing vehicles on approach to a vertiport that would be impacted by an emergency vehicle to minimize or avoid cascading delays. A route of time-optimal Bézier curves as well as Dubins paths optimized for interception heading was generated and flown on as an alternate maneuver when an unaccounted-for emergency vehicle initiated a bypass of an air traffic fleet. A comparison to flight on a holding pattern showed that the Bézier and Dubins route improved delay times and mitigated a cascading delay effect. Full article

Massive multi-input multi-output (massive MIMO) technology offers significant multiplexing gains and enhances transmission rates by efficiently utilizing available airspace resources. However, it requires each antenna to be paired with a separate radio frequency (RF) chain, which leads to the need for numerous RF chains in the system, resulting in high hardware costs, increased computational complexity, and elevated power consumption. To address this, antenna selection technology reduces the number of RF chains required, activating only the antennas that correspond to the available RF chains. Moreover, user scheduling provides multi-user diversity in multi-user massive MIMO systems. Therefore, this paper introduces a joint antenna selection and orthogonality-based user scheduling (JAS-OUS) algorithm aimed at maximizing the system sum rate. Furthermore, to tackle the issue of fairness, which is often overlooked by traditional user scheduling algorithms, a proportional fairness user scheduling (PFUS) approach is proposed. In this scheme, user weights are updated based on proportional fairness, ensuring a fair selection of users for communication in each time slot. Simulation results demonstrate that the JAS-OUS algorithm achieves robust performance across various configurations of transmitting antennas and users. Additionally, when combined with PFUS, the joint algorithm ensures more equitable user participation in communication without compromising the system sum rate. Full article

Real-time 3D object detection is a cornerstone for the safe operation of drones and autonomous vehicles (AVs)—drones must avoid millimeter-scale power lines in cluttered airspace, while AVs require instantaneous recognition of pedestrians and vehicles in dynamic urban environments. Although significant progress has been made in detection methods based on point clouds, cameras, and multimodal fusion, the computational complexity of existing high-precision models struggles to meet the real-time requirements of vehicular edge devices. Additionally, during the model lightweighting process, issues such as multimodal feature coupling failure and the imbalance between classification and localization performance often arise. To address these challenges, this paper proposes a knowledge distillation framework for multimodal 3D object detection, incorporating attention guidance, rank-aware learning, and interactive feature supervision to achieve efficient model compression and performance optimization. Specifically: To enhance the student model’s ability to focus on key channel and spatial features, we introduce attention-guided feature distillation, leveraging a bird’s-eye view foreground mask and a dual-attention mechanism. To mitigate the degradation of classification performance when transitioning from two-stage to single-stage detectors, we propose ranking-aware category distillation by modeling anchor-level distribution. To address the insufficient cross-modal feature extraction capability, we enhance the student network’s image features using the teacher network’s point cloud spatial priors, thereby constructing a LiDAR-image cross-modal feature alignment mechanism. Experimental results demonstrate the effectiveness of the proposed approach in multimodal 3D object detection. On the KITTI dataset, our method improves network performance by 4.89% even after reducing the number of channels by half. Full article

Pulse detonation engines (PDEs) have become a transformative technology in the field of aerospace propulsion due to the high thermal efficiency of detonation combustion. However, initiating detonation waves within a limited space and time is key to their engineering application. Direct initiation, though theoretically feasible, requires very high critical energy, making it almost impossible to achieve in engineering applications. Therefore, indirect initiation methods are more practical for triggering detonation waves that produce a deflagration wave through a low-energy ignition source and realizing deflagration to detonation transition (DDT) through flame acceleration and the interaction between flames and shock waves. This review systematically summarizes recent advancements in DDT methods in pulse detonation engines, focusing on the basic principles, influencing factors, technical bottlenecks, and optimization paths of the following: hot jet ignition initiation, obstacle-induced detonation, shock wave focusing initiation, and plasma ignition initiation. The results indicate that hot jet ignition enhances turbulent mixing and energy deposition by injecting energy through high-energy jets using high temperature and high pressure; this can reduce the DDT distance of hydrocarbon fuels by 30–50%. However, this approach faces challenges such as significant jet energy dissipation, flow field instability, and the complexity of the energy supply system. Solid obstacle-induced detonation passively generates turbulence and shock wave reflection through geometric structures to accelerate flame propagation, which has the advantages of having a simple structure and high reliability. However, the problem of large pressure loss and thermal fatigue restricts its long-term application. Fluidic obstacle-induced detonation enhances mixing uniformity through dynamic disturbance to reduce pressure loss. However, its engineering application is constrained by high energy consumption requirements and jet–mainstream coupling instability. Shock wave focusing utilizes concave cavities or annular structures to concentrate shock wave energy, which directly triggers detonation under high ignition efficiency and controllability. However, it is extremely sensitive to geometric parameters and incident shock wave conditions, and the structural thermal load issue is prominent. Plasma ignition generates active particles and instantaneous high temperatures through high-energy discharge, which chemically activates fuel and precisely controls the initiation sequence, especially for low-reactivity fuels. However, critical challenges, such as high energy consumption, electrode ablation, and decreased discharge efficiency under high-pressure environments, need to be addressed urgently. In order to overcome the bottlenecks in energy efficiency, thermal management, and dynamic stability, future research should focus on multi-modal synergistic initiation strategies, the development of high-temperature-resistant materials, and intelligent dynamic control technologies. Additionally, establishing a standardized testing system to quantify DDT distance, energy thresholds, and dynamic stability indicators is essential to promote its transition to engineering applications. Furthermore, exploring the DDT mechanisms of low-carbon fuels is imperative to advance carbon neutrality goals. By summarizing the existing DDT methods and technical bottlenecks, this paper provides theoretical support for the engineering design and application of PDEs, contributing to breakthroughs in the fields of hypersonic propulsion, airspace shuttle systems, and other fields. Full article

The air traffic growth expected for future years will likely cause an imbalance between traffic demand and available capacity. This could lead to increased airspace congestion, heightened complexity, and a higher workload for controllers attempting to manage the situation. Nowadays, available tools can identify 4D Area of Relatively High Air Traffic Control Complexity (4DARHAC) events up to 20 min before they occur. Nonetheless, state-of-the-art Artificial Intelligence applications can significantly increase this prediction horizon. Powered by a combination of different Machine Learning models, the ASTRA solution aims to both detect and provide resolution strategies for 4DARHACs up to 1 h before onset. To validate ASTRA’s operational concept, a series of workshops and interviews with Flow Management Position operators were conducted, focusing on assessing the initial concept and identifying end user needs. The feedback collected was validated by a board of Subject Matter Experts (SMEs) and transformed into a concrete set of functional and non-functional requirements. Overall, ASTRA’s operational concept was endorsed as a promising solution for reducing airspace complexity while alleviating operator workload during the tactical phase of operations. Experts further highlighted the importance of integrating ASTRA with existing Flow Management Position software tools to maximize its operational impact and facilitate adoption. Full article

High payload capacity multi-rotor aerial vehicles are typically configured with multiple propellers to achieve the required aerodynamic lift. However, this design approach often results in an increased overall dimensional envelope, which introduces significant operational limitations in confined spatial environments such as urban airspace. By utilizing a limited overlap rotor configuration, the spatial utilization rate of an aircraft can be greatly improved, ensuring a sufficient thrust of rotor while simultaneously reducing the size of the aircraft. However, the slipstreams of two rotors overlap, which may create a significant aerodynamic interface. This paper utilizes numerical simulation based on the unsteady RANS (Reynolds-averaged Navier–Stokes) method to analyze the influence of parameters such as distance, blade distance, and rotation direction on the interference flow field of overlapping rotors. Research indicates that aerodynamic interference only affects the overlapping area between two rotors at the inner blade, leading to the offset of loading distribution on the blade, which can be explained by the slipstream effect, suction effect, and induced effects generated by two rotors. As the axis distance between two rotors decreases, the strengthening of the slipstream and suction effects leads to a rapid decrease in the aerodynamic efficiency of the two rotors. When the blade between the two rotors increases, the weakening of the suction effect and induced effects causes the load on the lower rotor to translate to the upper rotor. Moreover, the variation in the spatial distribution of the blade tip–vortex leads to blade–vortex interaction, which causes a change in the spanwise distribution of the load on the lower blade. Full article

This article addresses the issue of loss of separation incidents and discusses currently implemented technological solutions designed to minimize the risk of such occurrences. An evaluation of these solutions is conducted, highlighting their key advantages and disadvantages. Additionally, a literature review of proposed new solutions is presented, emphasizing the necessity of introducing a new system to address previously identified shortcomings. This work proposes an early warning system for potential airspace collisions based on an artificial neural network. Drawing from the literature analysis, five fundamental assumptions for an early conflict warning system to support air traffic control are formulated. Each assumption is justified, with some addressing the weaknesses of existing solutions. The contributions of this paper, in relation to previously analyzed works, are as follows: (1) the system does not rely on the dynamics model of a specific aircraft type, (2) the possibility of radar vectoring (vectors to final) is considered, (3) the input data are not limited to the horizontal plane and time differences, (4) the system does not require identifying the most similar historical trajectories to assess minimum separation values and potential conflicts, and (5) the system is expected to perform better in airspace where radar vectoring prevails compared to flight along standard routes. The research methodology is discussed in detail, including the operational environment of the system and the applied algorithms. A feedforward neural network was selected, featuring 32 neurons in the first hidden layer and 16 neurons in the second hidden layer. The training process was conducted using the Levenberg–Marquardt algorithm, chosen for its fast convergence. The presented analyses confirm that the developed system meets the established assumptions. Full article