Search Results (155)

The rapid growth of the low-altitude economy and urban air mobility (UAM) is reshaping urban transport and infrastructure systems. However, current planning practices still tend to treat green spaces, stormwater facilities, and drone infrastructure as separate subsystems. This paper proposes a Garden Hydrology UAV collaborative infrastructure framework for resilient urban low-altitude logistics and inspection. Pocket parks and sponge city facilities (rain gardens, detention basins) are redesigned as multi-functional UAV bases that integrate take-off/landing and charging with stormwater retention and recreation. A SWMM-based hydrological model provides time-varying inundation and storage states, which are mapped into dynamic node availability constraints for UAV operations, using EPA SWMM 5.2. A multi-objective optimization model is formulated to minimize logistics operation cost, hydrological risk exposure and noise impact on sensitive receptors, while respecting airspace and battery constraints. A stylized 4 km² high-density district is used to evaluate three scenarios: depot-only operations, garden–UAV integration without hydrological coupling, and the full collaborative framework with SWMM-based node availability and high-precision navigation. Simulation results show that the integrated design reduces makespan by up to 19.7%, energy use by 22.3%, and hydrological risk exposure by 63.4%, while lowering noise exposure by 21.3%, relative to the baseline. The study suggests that garden and sponge city infrastructures can become key physical supports of smart low-altitude networks under the low-altitude economy. Full article

This article discusses a novel aircraft coordination algorithm for automated vertiport operation. New applications of Innovative Air Mobility (IAM) including inspection, logistics and security UAVs, Urban Air Mobility (UAM) or Regional Air Mobility (RAM) present a coordination challenge, especially near vertiports, as large numbers of vehicles with different characteristics share the airspace, and so avoiding collisions, optimizing resource usage and operating with low human intervention is important.In this paper, this problem is addressed by proposing a new formulation of the aircraft coordination problem that makes use of a discretized airspace to detect potential conflicts and collisions between cooperative and non-cooperative aircraft in the surroundings of a vertiport. The proposed algorithm not only considers the cells traversed by the aircraft, but also the set of adjacent cells, making the algorithm more conservative and robust than other algorithms found in the literature, and achieving a 100% conflict-detection rate. A mathematical model of aircraft dynamics is employed to turn high-level flight plans into detailed aircraft trajectories, using those trajectories to detect potential collisions. The deconfliction problem is formulated as a mixed-integer optimization program that computes orders of pass for every conflict while minimizing the divergence between requested time of arrival (RTA) and estimated time of arrival (ETA). This problem is implemented in OR-Tools to be solved by means of the CP-SAT solver. The validity of the solution is tested by extensive simulation, showing tactical coordination of up to 25 aircraft landing on a vertiport. Full article

Traffic congestion and air pollution caused by rapid urbanization have emerged as critical challenges in metropolitan areas worldwide. Urban air mobility (UAM), particularly electric propulsion-based systems, has gained attention as a promising solution. For the successful commercialization of UAM, a lightweight airframe design with ensured structural integrity is essential. This study proposes an optimized lightweight design process that integrates automated composite manufacturing with artificial intelligence (AI)-based material property prediction. Finite-element analysis (FEA) was performed on glass fiber-, basalt fiber-, and carbon fiber-reinforced polymers under identical deformation conditions to derive design material properties in terms of elastic modulus and weight reduction. A large-scale dataset of fiber-reinforced plastics was established through an automated manufacturing process, and a deep learning regression model was developed using Altair AI Studio to predict mechanical properties under untested material and process conditions. The predicted properties were applied to a UAM fuselage model, and FEA results demonstrated that composite structures achieved equivalent or superior stiffness with up to 50% weight reduction compared to aluminum. In addition, inverse reserve factor (IRF) analysis confirmed structural safety, with all configurations maintaining IRF values below 1. The proposed AI-driven framework provides a scalable and data-driven lightweight design methodology applicable to next-generation UAM and advanced air mobility structures. Full article

Urban Air Mobility (UAM) has emerged as a promising solution to alleviate urban congestion and support low-carbon transportation by utilizing low-altitude airspace. However, its large-scale deployment requires governance mechanisms that simultaneously address environmental impacts, social acceptance, and institutional coordination. Existing studies have not yet provided an operational Environmental, Social, and Governance (ESG)-based decision framework for UAM governance. This study develops and empirically validates an ESG-oriented governance model for UAM integration into urban development. A mixed-method approach was adopted, including literature and policy analysis to identify 22 execution-level factors, a questionnaire survey of industry practitioners and experts (N = 307), and the Analytic Hierarchy Process (AHP) combined with expert consultation to determine priority weights. The results show that the Governance dimension has the highest importance (38.72%), followed by Social (32.15%) and Environmental (29.13%). Laws and regulations, standard certification, and digital management constitute the core institutional foundations for UAM deployment. Privacy protection and social acceptance are the dominant social concerns, while noise pollution represents the most critical environmental constraint. Across all dimensions, standard certification, privacy, noise control, management framework, and digital management are the highest-weighted factors. The proposed framework provides a practical ESG-based decision tool to support policy prioritization and sustainable UAM implementation in rapidly urbanizing regions. Full article

This paper offers a critical review of cutting-edge research on multi-energy microgrids (MEMs), with a novel exploration of their potential role in supporting urban air mobility (UAM), specifically electric vertical takeoff and landing (eVTOL) aircraft. While extensive research has focused on improving the economic performance and emission reductions of MEMs, particularly in the context of electric vehicle (EV) charging, there remains a significant gap in understanding how microgrids can support the decarbonization of UAM. The paper examines the opportunities and challenges of integrating microgrids with UAM operations, highlighting the need for more research to optimize energy management systems that balance renewable energy use with the growing demand for aerial transport. Thermal energy storage systems are emphasized as a critical component for addressing transportation energy needs, offering a promising solution to reduce carbon emissions while enhancing system efficiency. This review aims to provide new insights into how the coupling of microgrids and UAM can contribute to the development of economically and environmentally sustainable smart cities. Full article

Drone-based Urban Air Mobility (UAM) shows immense potential in urban logistics and emergency response; however, evidence regarding its systemic sustainability remains fragmented. In a systematic review using the PRISMA methodology, this study analyzes 301 core articles to construct an evaluation framework spanning environmental, economic, social, and systemic effectiveness dimensions. Given technical similarities, electric Vertical Take-off and Landing (eVTOL) findings are integrated to anticipate operational challenges. Results highlight a clear consensus: drone delivery is time-efficient in high-sensitivity scenarios, though noise, equity, and safety remain critical bottlenecks. Meanwhile, deep controversies persist across some dimensions. Environmental benefits are highly context-dependent, contingent on operating models, battery life cycles, and clean energy proportions from a Life Cycle Assessment (LCA) perspective. Economically, a mismatch between high costs and low willingness to pay (WTP) necessitates optimized pricing strategies. Socially, public acceptance is sensitive to the balance between perceived benefits and risks. Furthermore, systemic effectiveness depends on the coupling between vertiports and ground infrastructure. Concluding that sustainable drone-based UAM is a multistakeholder systemic endeavor, we urge future research to prioritize LCA, pricing strategies, public acceptance surveys, and integrated air-ground coordination to resolve controversies and foster sustainable systems. Full article

Urban Air Mobility (UAM) is increasingly recognized as one of the promising methods for future urban transportation, offering higher average speeds than conventional means of transportation. This study investigates the sustainable and efficient production of hollow propeller blades (837 × 85 × 40 mm) using high pressure resin transfer molding (HP-RTM), driven by high demand for UAM, particularly for wingless multicopters. Unlike conventional monolithic or sandwich structures, the propeller blade in this project features a hollow design using a lost core made from water soluble salt. Full article

Autonomous trajectory planning for electric Vertical Takeoff and Landing (eVTOL) Unmanned Aerial Vehicles (UAVs) faces the dual challenges of low-altitude environmental interference and limited onboard energy, which affects the reliability and safety of unmanned missions. To address these challenges, this paper develops the EA-TD3 autonomous trajectory planning framework for eVTOL UAV systems. First, a stochastic urban wind field model is established to simulate low-altitude interference. Then, by integrating eVTOL UAV battery discharge data from Carnegie Mellon University (CMU), a mapping relationship between maneuvers and energy consumption is identified to construct a nonlinear energy consumption model. Finally, an energy boundary penalty function is introduced into the TD3 algorithm to ensure that trajectory planning remains within battery safety margins. Experiments based on the parameters of the EH216-S platform show that EA-TD3 achieves a near 100.00% success rate under ideal conditions and outperforms benchmark algorithms while reducing average energy consumption by 11.6%. Under an energy constraint of 120 J, its success rate remains at 87.80%, which exceeds the performance of the DDPG, SAC, and standard TD3 algorithms. This study optimizes the autonomous trajectory planning of eVTOL UAV platforms in urban air mobility (UAM) to improve the energy perception and power management of the autonomous system. Full article

Drone noise-induced human annoyance is emerging as one of the main barriers to socially acceptable large-scale urban air mobility (UAM) operations, which have the potential to revolutionize urban transportation systems in the next few decades. This paper investigates the state-of-the-art technology in the assessment of drone noise and its impact on individuals, focusing on measurement and evaluation methodologies, as well as subjective evaluations. Various acoustic metrics are reviewed to characterize drone noise, including sound pressure levels, spectral analysis, and psychoacoustic parameters such as loudness and annoyance. Preliminary experimental investigations to identify key frequencies and tonal components that significantly contribute to drone noise-induced public annoyance are also discussed. Interdisciplinary approaches integrating pure technical acoustics, human perception, and subjectivity emerge as promising solutions for a comprehensive understanding of drone noise effects. Finally, a preliminary framework for drone noise assessment towards noise-aware UAM operations is proposed. Full article

This study proposes a heterogeneous fusion path planning framework for unmanned aerial vehicles (UAVs) operating in complex emergency rescue and civil environments. Existing single-mechanism metaheuristics—including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GAs)—suffer from fundamental limitations in three-dimensional kinematic path planning: PSO converges rapidly but stagnates at local optima due to population variance collapse; ACO offers robust local exploitation but incurs prohibitive cold-start overhead; GAs maintain diversity at the cost of expensive crossover operations. To address these complementary deficiencies simultaneously, the proposed framework introduces a spherical coordinate representation that reduces computational complexity and naturally enforces UAV kinematic constraints, combined with adaptive weight factors and a serial PSO-ACO fusion strategy, and subsequently incorporates adaptive weight factors. A serial fusion strategy is then introduced, wherein the sub-optimal trajectory generated by the Spherical PSO phase is mapped into the ACO pheromone field via a Gaussian Kernel Density Mapping (GKDM) mechanism, enabling the ACO phase to perform fine-grained local exploitation within a kinematically feasible corridor. Various constraints along the flight path are formulated into distinct cost functions, which cover aircraft track length, pitch angle variation, altitude difference variation, obstacle avoidance, and smoothness; the core task of the algorithm is to find the flight path with the minimum total cost. The proposed algorithm is dedicated to UAV path planning in complex emergency rescue environments (disaster-stricken areas, hazardous zones) and is further applicable to civil low-altitude logistics delivery, industrial facility inspection, ecological environment monitoring and urban air mobility (UAM) scenarios with complex obstacle constraints. It can effectively improve the safety and efficiency of UAVs in reaching rescue points, delivering emergency supplies, conducting disaster surveys, and completing various civil low-altitude operation tasks. Full article

Advanced Air Mobility (AAM) includes innovative aviation technologies and services that could alter how people and goods are transported. However, future AAM growth and potential regional integration are uncertain and influenced by a range of factors. In this paper, we report findings from expert interviews (n = 35) and a scenario planning workshop (n = 32 stakeholders), conducted between August 2024 and July 2025, to explore potential alternative futures for AAM at the San Francisco International Airport (SFO) and the greater San Francisco Bay Area. We applied a two-axis framework: regulatory environment (supportive vs. restrictive) and economic conditions (vibrant vs. stagnant). Building on this, we developed four plausible scenarios for the 2025 to 2030 and post-2030 time horizons. We apply the SPELT (social, political, economic, legal, technological) framework to assess cross-cutting drivers, tensions, and indicators across the four scenarios based on two timeframes, i.e., 2025 to 2030 and post-2030. Our analysis of the scenarios reveals that regulatory clarity and macroeconomic conditions are key influencers that define the pace and scale of AAM growth, while community impacts (e.g., noise), public acceptance, and infrastructure availability are constraints. These factors largely determine whether technical readiness can translate into scaled deployment. Cross-cutting themes across all of the scenarios consistently shape the outcomes: (1) equity and community acceptance strongly influence political feasibility; (2) SFO and other airports can serve dual roles as conveners and practical enablers but face risks of stranded assets; and (3) flexible, modular infrastructure and incremental investment strategies reduce uncertainty for SFO and other Bay Area airports and public agencies. Together, the findings suggest that while the future of AAM is uncertain, policy and planning responses can assist airports, local governments, and other public agencies in preparing for potential developments. Full article

Urban Air Mobility (UAM) introduces new safety challenges as small unmanned aircrafts begin to operate at high density in complex urban environments. Traditional air traffic management (ATM) systems developed for manned aviation are unable to accommodate the autonomy, mission diversity, and dynamic obstacle conditions typical of low-altitude operations. This review examines recent research on mid-air collision risk and airspace safety modeling for UAM and identifies key challenges in adapting existing safety concepts to small-scale and autonomous flight. The study compares international management frameworks of the United States, Europe, and China. Then analyzes representative airspace structures such as Free, Layered, Zoned, and Pipeline configurations. It further reviews deterministic and probabilistic separation models, geometric and optimization-based avoidance strategies, and structured airspace approaches such as the virtual-tube concept for coordinated swarm navigation. The findings highlight the lack of integrated models that couple human, energy, and communication factors into quantitative risk assessment. The paper concludes by outlining future research needs in uncertainty modeling, digital-twin simulation, and interoperability to support safe and scalable UAM development. Full article

Navigating large-scale Unmanned Aerial Vehicle (UAV) swarms through restricted airspaces requires a delicate balance between rigorous safety guarantees and high mission throughput. To address the limitations of rigid tube structures and over-constrained optimization problems, we present a Dynamic Tube-based Distributed Model Predictive Control (DMPC) approach. Unlike traditional methods, our framework introduces an elastic tube reconstruction mechanism that adaptively shifts boundaries to accommodate dynamic obstacles while maintaining a connected navigable tube. By integrating a predictive risk-triggered constraint activation policy, the proposed controller avoids the curse of over-conservativeness, ensuring recursive feasibility in high-density scenarios. Quantitative results indicate that our method reduces redundant computations while maximizing workspace utilization, providing a scalable solution for future Urban Air Mobility infrastructures (UAM). Full article

Rapid urban air mobility (UAM) developments and new classes of vertical takeoff and landing (eVTOL) aircraft have changed the safety paradigm in urban airspace. eVTOL aircraft operations in dense urban environments are characterized by increased variability of external factors, highly dynamic flight scenarios, and an increased likelihood of rare but potentially critical events. Traditional safety assessment approaches do not capture the specific features of eVTOL designs, power plants, autonomy algorithms, and urban air traffic characteristics; this results in low threat prediction accuracy and limited development of modern incident prevention systems. Herein, the risk profile of eVTOL aircraft is analyzed, accounting for the multifactorial nature of urban environments and the complexity of integrating such vehicles into existing UAM infrastructure. The need for quantitative methods for assessing the probability of critical situation risks is also substantiated. These methods provide a statistically accurate description of extreme events and enable the identification of hidden dependencies in complex technical and organizational systems. Approaches based on probabilistic models, extreme value analysis, and systemic processing of operational data are considered, providing increased risk assessment accuracy and a deeper understanding of mechanisms underlying hazardous events. Results demonstrate the importance of applying the extreme value theory (EVT)–Copula model, which enables the quantitative assessment of the probability of extreme situations and loss of stability of eVTOL vehicles in the context of developing UAM. This model can be employed to obtain realistic predictions of flight processes, reduce uncertainty, and create scientifically valid tools for developing effective measures to minimize the risks of extreme events—a key factor in ensuring the safety of eVTOL flights in urban airspace. Full article

This paper applies the recently developed Prediction-Based Control Barrier Functions (PB-CBFs) to the obstacle avoidance problem for multirotor air taxis in Urban Air Mobility (UAM). Unlike conventional Control Barrier Functions (CBFs), PB-CBFs incorporate escape path predictions into the formulation, facilitating safe controller design for dynamical systems with high relative degree and enabling safety under strict control constraints. We first review the PB-CBF framework, then formulate the safety requirements specific to the collision avoidance problem and derive the corresponding invariance conditions. Finally, we validate our approach through simulation of the obstacle avoidance scenario, demonstrating the efficacy of PB-CBFs in ensuring safety in UAM operations and providing additional insight into the mechanism by which predictions are leveraged to enforce safety. Full article