Browse Topic: Mobility
This paper presents a generalizable geometric framework for rapid on-demand generation of multi-UAV formations with arbitrary 2D geometries and user-specified scalable scales. First, vertices, edge intersections and edges are extracted from a user-defined formation template to enable parametric description of both simple and composite formation geometries. Second, boundary interpolation, edge expansion and recursive internal expansion are integrated to synthesize hierarchical multi-layer UAV deployment point sets under a controllable expansion ratio. Third, a geometric distortion metric is proposed to optimize UAV node indexing and formation reconstruction while preserving inter-node topological consistency. Algorithmic derivations, complexity analysis and simulation assumptions are further elaborated. Simulation results verify that the proposed method preserves geometric fidelity of target formations while delivering superior scalability and spatial coverage, rendering it well-suited for emergency transport, aerial surveying and low-altitude cooperative missions in dense urban environments.
Under the constraints of conventional chassis layouts, traditional wheeled vehicles struggle to maintain stable obstacle-crossing performance on complex terrain. This study aims to enhance both the obstacle-crossing capability and stability of such vehicles. First, a transformable wheel capable of varying its effective radius and actively adjusting the wheel–ground contact configuration is designed, and its degrees of freedom are analyzed using screw theory. Next, based on screw theory and Lie group theory, position-level and velocity-level kinematic models of the transformable wheel are established, and system-level performance indices—including workspace, singular configurations, and force-transmission characteristics—are formulated. Finally, taking these performance indices as optimization objectives, a constrained optimization model of the mechanism’s geometric parameters is constructed, from which an optimal dimension set for the transformable wheel is obtained. The results show that the optimized transformable wheel has significantly improved minimum singularity and dexterity. The designed transformable wheel can achieve changes in wheel radius and wheel rim inclination angle, improving the vehicle's passability in complex terrain.
For decades, hydraulic systems have been relied upon to do all the heavy lifting in aerospace. They are powerful, reliable, and deeply embedded in how aircraft are designed, to the extent that - for many engineers - they are simply part of the landscape. Now, however, things are beginning to change. From advanced air mobility platforms now entering certification to next-generation commercial aircraft on 10-year horizons, electric and electro-hydraulic actuation is steadily replacing the heavy, centralized hydraulic architectures that have defined flight control for decades. Understanding why means stepping back from the actuator itself and looking at the aircraft as a whole system - and, increasingly, as an integrated motion control challenge.
When quadrotor unmanned aerial vehicles (UAVs) operate in urban low-altitude airspace, especially within complex environments, their sensor perception signals are highly susceptible to blockages, deviations, and the inclusion of high-frequency noise. These factors, in turn, induce nonlinear variations in the UAVs’ flight mechanical properties, giving rise to abnormal flight stability issues such as attitude jitter, altitude fluctuations, and trajectory deviations. To address these challenges, this paper puts forward a method aimed at enhancing the positional accuracy of quadrotor UAVs, which is based on Extended Kalman Filter (EKF) multi-sensor fusion. In conjunction with the redundant configuration of sensors, a proportional-integral controller is specifically designed to allow optical flow sensors to compensate for the speed data generated by inertial sensors. Building on the EKF method, a comprehensive data fusion model is established, encompassing both position and speed states. Leveraging the MATLAB platform, trajectory flight simulations are conducted, utilizing multi-sensor data fused via EKF, with the sensor suite including GPS, IMU, Optical Flow sensors, and Barometers. The simulation results demonstrate that this proposed method can effectively mitigate the adverse impacts of environmental interference and sensor noise on the positional accuracy of quadrotors. By continuously correcting position information and accurately estimating position states, it significantly improves the UAVs’ flight position accuracy. This research outcome lays a robust and theoretically sound foundation for in-depth investigations on critical issues related to general aviation applications, such as the safe and efficient autonomous flight, adaptive and reliable intelligent navigation, and ultra-precise and mission-critical operations of quadrotor UAVs, thereby significantly contributing to the sustained and innovative advancement of the field.
There's a well-known video from San Francisco in 1906 that comes up repeatedly in mobility discussions here in the 21st Century. If you haven't seen A Trip Down Market Street, it depicts the absolute bonkers variety of transportation methods used on Market Street back then: cable cars, horsecars, streetcars, pedestrians, automobiles and more. Past is prologue in a world that is adding scooters, delivery robots and other last-minute delivery vehicles to our streets. At the 2026 New York International Auto Show in April, Honda displayed its latest option in the form of the Fastport eQuad Prototype. The eQuad was originally unveiled at Eurobike 2025 and technically comes from Fastport, a micromobility venture from the Honda New Business Innovation Lab that was established to work on projects with global logistics companies. Jamie Davies, chief of operations for Fastport, called the group a kind of startup within Honda. “Three years ago,” Davies told SAE Media in New York, “a small group of Honda associates [came] together and [said], Okay, how can we create a new value for the company, a new business vertical? And so we've run the project in an agile way, working with customers all along the way to understand what their needs are, what the requirements are, and to bring to market something that fits.”
Not every joke works in every location. This week, during an opening panel at SAE International's WCX 2026, Ford's director of motion tech strategy at Ford, Mazen Hammoud, started off with a pun. Hammoud thanked the moderator, Chris Atkinson, professor and director of the Advanced Mobility Initiative at Ohio State University, for the introduction, then he paused and said, “I love your engines.” Sure, that's kind of a groaner, but it also brought out some smiles, which was not exactly guaranteed as a session called “Engineering a resilient propulsion strategy in a volatile, uncertain, complex, and ambiguous world.” Panelists discussed what our collective automotive future might look like in an industry where the powertrain of choice keeps changing these days.
The aging of the population has been a key issue worldwide, with mobility and fall of the elderly an important problem to be solved. In this paper, we propose an elderly mobility assist system based on the intelligent power-assisted device consisting of an assistive cane and an intelligent companion. It has the functions of standing support after falling, daily support and on-site rest. The assistive cane adopts a two-stage expansion mechanism of crank and slider structure, which forms a stable triangular support after unfolding, so that the patient can stand safely. The intelligent companion platform is driven by drive wheels, equipped with pushrod motors and vacuum suction devices, it can automatically approach the user and form an stable support column when the cane is in the out-of reach range; the control system is designed by combining microcontroller, camera object recognition, wristband remote control, to realize automatic steering and autonomous navigation at differential speed. The overall design satisfies the requirements of safety and strength through mechanical verification and stress analysis. The proposed system can help the elderly people to recover from falls better and enhance their independence and safety in their daily walks.
Urban Air Mobility (UAM) vertical takeoff and landing (VTOL) air taxis might exhibit flight motions that are unfamiliar to many passengers. Researchers at the NASA Armstrong Flight Research Center performed a study to identify relationships between rotational flight motion and passenger comfort and acceptance. Fifty test subjects each completed a 20-min passenger experience in a virtual air taxi simulation. Subjects evaluated flight maneuvers with varying levels of rotational motion and indicated their comfort level and willingness to take a real flight with the motion they experienced. Study factors included yaw rate, pitch rate, and roll rate. Participants evaluated four levels of each study factor. The study found linear relationships for each study factor showing a decrease in passenger comfort and acceptance with increased motion. Statistical significance of the results, the possible influence of participants’ backgrounds and experiences, and other potential sources of bias in the study population are discussed.
Autonomous Inspection via small Unmanned Aircraft Systems (sUAS) is increasingly utilized across industrial use cases such as inspection of bridges, buildings, construction sites, roadways, transmission lines, pipes, wind turbines and power systems (1). In principle, the system workflow of inspection; identification and characterization of defects; and mapping in space is very similar across industries. Boeing and Proxim (A Near Earth Autonomy Company) have partnered to pursue this technology in the Aerospace and Defense industry for General Visual Inspection (GVI) of airframes predominantly in a maintenance setting. This activity began by deploying Proxim’s Autonomous Aircraft Inspection (AAI) technology and Boeing's Automated Damage Detection Software (ADDS) on Boeing C-17 Globemaster III at Joint Base Pearl Harbor-Hickam. It has expanded to offer U.S. Department of War (DoW) and Commercial customers aircraft-agnostic enhanced exterior GVI capability at point of need by leveraging unique ADDS AI algorithm in support of both home station and deployed operations. This paper gives an overview to industry developments in Autonomous Inspection, and the development AAI/ADDS technologies.
This paper develops an engineering concept and research framework showing how Cherokee MC2 (Mobile Command Center) and MVP (Mobile Vertipad Platform) can individually, and then as a combined system, resolve key operational and infrastructure challenges facing rotorcraft, eVTOL, VTOL, and UAS missions across civil, commercial, and military contexts. The investigation synthesizes current vertiport / vertipad design guidance, UAM and UTM operational architectures, and recent research on rotor downwash and degraded visual environment hazards to derive a deployable "vertiport node" architecture for austere and time-critical operations. MC2 is treated as the digital and procedural core enabling command, control, communications, data fusion, and manned–unmanned teaming, while MVP is treated as the physical landing interface enabling rapid, load-bearing, illuminated vertical-lift operations without fixed infrastructure. The primary contribution is a traceable topic-to-capability mapping supported by standards and research, plus a modeling, simulation, and optimization workflow to validate safety zones, capacity, scheduling, and resilience. Conclusions identify practical deployment pathways and research gaps for certification-aligned operations.
The proliferation of Autonomous Aerial Vehicles (AAVs) necessitates robust solutions for dynamic obstacle avoidance, particularly against non-cooperative intruders whose trajectories are unpredictable. While traditional path-planning algorithms excel in static environments, they struggle with dynamic obstacles due to the inherent difficulty in accurately estimating and registering their real-time depth and velocity into a world model. This paper presents a novel two-stage vision-based framework that leverages deep learning for reactive avoidance of non-cooperative dynamic intruders. Our approach decouples the perception and decision-making processes: an object detection deep neural network first processes monocular camera images to detect and track the 2D pixel coordinates of intruders. This perceptual output is then fed into a deep reinforcement learning agent, which learns a mapping from the intruder's image-space location to a high-level avoidance maneuver. This leads to more efficient learning, as the RL agent focuses solely on the policy without the burden of learning visual features. The advantage of using RL lies in its ability to handle partially observable situations—because reliable depth or full 3-D position information is not always readily available from monocular imagery, the RL agent learns to act based on the observable visual cues. Simulation results confirm that our proposed framework provides an effective solution for vision-based, non-cooperative intruder avoidance.
The aerospace industry is undergoing a profound transformation driven by emerging aviation technologies, including Advanced Air Mobility (AAM), electric vertical takeoff and landing (eVTOL) aircraft, and highly automated flight control systems. These complex systems often feature tightly coupled flight controls and power plants where traditional methods of compliance — relying heavily on physical ground and flight testing — are becoming increasingly impractical due to the vast number of potential interaction cases. To address this challenge, the SAE G-35 Modeling, Simulation, and Training for Emerging Aviation Technologies and Concepts Committee was formed to develop industry consensus standards. This presentation discusses the landmark release of SAE ARP7094, "Recommended Practice for Using Modeling and Simulation for Certification of Aircraft, Products, and Systems" and its role in establishing a standardized, simulation-based path to certification. The SAE G35C group is responsible for developing standards and procedures for using modeling and simulation as a method of compliance for the certification of AAM aircraft similar to the RCbS project conducted in collaboration between EASA, academia and industry in Europe.
Electric vertical takeoff and landing aircraft impose significantly higher electrochemical and thermal demands on Li-ion batteries than conventional electric vehicles, yet publicly available aging datasets for this application remain limited in applicability, cell technology, and statistical robustness. This study experimentally characterizes the degradation behavior of state-of-the-art Molicel P45B 21700 cells under realistic Urban Air Mobility operating conditions involving high power demand, rapid turnaround, and repeated cycling. Eight cells are subjected to over 3,000 cycles using a fast constant-current charging protocol and a multi-segment constant-power discharge profile. The discharge profile is derived from a representative 7000-lb winged eVTOL with a 20-mile range, requiring normalized power rates of 6.4E during takeoff and landing and 1.6E during cruise. Periodic Reference Performance Tests are conducted to track capacity fade, internal resistance evolution, and energy efficiency. The cells retained over 90% of their initial capacity after 3,270 cycles, while total energy efficiency remained stable at 91%, comprising impedance and hysteresis-driven components of approximately 94% and 97%, respectively. Direct current internal resistance exhibited an initial decrease before stabilizing, yet total discharged capacity increased from 1.81 Ah to 1.84 Ah, indicating aging-driven polarization effects not captured by DCIR. A zero-order equivalent circuit model underpredicts discharged capacity by approximately 4% for fresh cells, increasing to nearly 6% at cycle 3,270 due to unmodeled time-dependent polarization effects. These results demonstrate that while modern Li-ion cells exhibit strong durability under repetitive high-power usage, the accuracy of battery performance prediction is strongly dependent on dynamic impedance effects beyond conventional DCIR-based models.
For Urban Air Mobility taxis, passengers will experience different levels of heave motion during flight. Researchers at NASA Armstrong Flight Research Center conducted two studies in which passengers were exposed to varying levels of heave motion in the Armstrong Virtual Reality Passenger Ride Quality Laboratory. In the first study, twenty-three volunteers from the Armstrong workforce evaluated the motions on a five-point rating scale and a binary comfort scale; in the second study, fifty volunteers evaluated a flight experience with varying levels of stimuli using a five-point comfort scale and a five-point passenger acceptance scale. This paper combines the results of these two studies to observe the relationship between heave motion and passenger rider quality and acceptance. Both passenger comfort and acceptance were found to decrease with increasing heave acceleration The statistically significant relationship between the magnitude of heave acceleration and passenger comfort for individual studies and combined results are discussed.
Electric Vertical Take-Off and Landing (eVTOL) vehicles are emerging as solutions for urban air mobility, but their operation can encounter hazardous aerodynamic conditions such as the Vortex Ring State (VRS), which causes thrust loss and intense vibrations. This study investigates VRS for the Archer Maker tilter propeller by combining numerical simulations using the mid-fidelity solver DUST and the high-fidelity solver OVERFLOW with prior experimental observations. Propeller performance is evaluated through thrust and torque evolution under various descent conditions, while flow fields in the propeller wake at different descent ratios around VRS conditions are evaluated and compared via 2D visualizations. A comparison of results reveals that both numerical approaches are capable of evaluating the performance degradation in correlation with vortex ring formation within specific descent regimes, showing slight discrepancies particularly regarding the descent ratio regime where VRS occurs. A flow field comparison with experimental data validates the multi-fidelity numerical approach, showing the capabilities of both numerical approaches to capture the flow physics mechanisms that lead to the generation of the vortex ring around the propeller disk.
Urban air mobility with electric Vertical Take-Off and Landing (eVTOL) aircraft faces critical micro-weather and infrastructure readiness challenges. This paper proposes a novel socio-technical solution: a tokenized gamification platform that crowdsources hyper-local wind and weather data to enhance operational resilience. We outline the safety gap left by traditional aviation weather systems (METAR, AWOS, ASOS) in urban environments, and leverage community engagement to fill it. The proposed system integrates with Unmanned Traffic Management (UTM) and Safety Management Systems (SMS) to validate user-contributed micro-weather observations, incentivize accurate reporting through tokens and skill-level progression, and feed data into AI-driven forecasts. Early proof-of-concept results indicate improved wind hazard detection and robust user participation. By aligning with emerging regulations (FAA, EASA, DGCA) and test frameworks, this crowdsourced micro-weather ecosystem shows potential to uplift eVTOL safety, build public trust, and support city-scale planning for advanced air mobility.
Rainwater accumulation and management are critical to the safety and reliability of drones and emerging eVTOL aircraft. Current industry practice relies on physical rain testing, such as RTCA DO-160, which defines rainfall conditions for environmental qualification but is costly and difficult to apply during early design stages. This work presents a virtual rainwater assessment framework using Smoothed Particle Hydrodynamics (SPH) simulation in PreonLab. Using an early-stage APELEON cargo drone as a reference case, the method predicts rain impingement, surface runoff, pooling, and ingress under representative rainfall conditions. The meshless SPH approach enables direct simulation of complex geometries and transient interactions without mesh generation, while also supporting rotating components and arbitrary orientations. Results identify key mechanisms governing water transport, including geometry-driven runoff, hinge-related ingress, and droplet deflection from nearby structures. While the total water accumulated on the aircraft can reach on the order of several hundred grams, the amount entering the cargo space remain small. Localized moisture exposure highlights potential durability risks. The framework enables early design evaluation, parametric studies, and rapid assessment of mitigation strategies, supporting simulation-driven development and certification preparation for advanced air mobility systems.
Advanced air mobility (AAM) seeks to develop a large-scale transportation system to revolutionize how people live and work, with electric vertical take-off and landing (eVTOL) aircraft serving a central role due to reduced emissions and noise impact. An important aspect for eVTOL aircraft certification is safe urban operations, which require understanding of the response due to aerodynamic disturbances. Experimental data are required to support eVTOL aircraft development with respect to flight dynamics and controllability, as well as design specification development. While flight testing of the full-sized air vehicle will be necessary as part of the certification process, subscale testing offers many advantages with respect to cost and flexibility, in addition to examining operational conditions that one would be reluctant to test in flight at full scale such as emergency conditions. These advantages only may be seen if the underlying scaling principles of flight dynamics / control, aerodynamic interactions, and propulsion-airframe integration are understood. This paper describes initial work towards development of a general subscale testing methodology for eVTOL aircraft flight dynamics and disturbance response characteristics including limited degree of freedom (DOF) and free flight testing. An overview of the initial development work is provided, including discussion of scaling relationships, subscale air vehicle model development, and testing activities focusing on flying qualities and stability / control characteristics.
This paper introduces a novel concept for an AI-powered system designed to manage vertiport takeoffs and landings by proactively addressing the safety-critical issues of downwash and outwash. The proposed system utilizes a stream of live feedback from on-site sensors, combined with a robust predictive modeling engine, to generate optimal, aircraft-specific approach and landing trajectories in real-time. By leveraging a comprehensive database of pre-computed downwash/outwash scenarios for a multitude of UAM aircraft configurations, the AI can accurately predict the unique outwash operational footprint for each individual landing operation, based on the approaching aircraft and under the prevailing conditions. This powerful predictive capability allows the system to calculate and assign an optimal approach vector that actively minimizes risk by directing hazardous airflows away from personnel, active walkways, and other sensitive ground assets. This represents a paradigm shift from static, reactive safety measures to a proactive, intelligent, and performance-based operational model; thereby paving the way for the safe, efficient, and large-scale implementation of UAM aircraft and air taxi operations.
This paper presents an adaptive model predictive control (MPC) framework for nonlinear urban air mobility (UAM) vehicles operating across the full flight envelope. The proposed approach leverages a linear parameter-varying (LPV) representation to update the predictive model online, enabling accurate capture of strongly nonlinear and time-varying dynamics associated with distributed electric propulsion (DEP) eVTOL aircraft. To systematically address the highdimensional and coupled nature of MPC tuning, a multi-objective evolutionary optimization strategy based on NSGAII is employed, incorporating proper normalization of states and control inputs to ensure balanced weighting and meaningful exploration of the design space. The resulting controller explicitly accounts for actuator constraints and enables reconfigurable control allocation for fault-tolerant operation. The framework is evaluated in nonlinear simulations using NASA's Generic Urban Air Mobility (GUAM) model and benchmarked against a robust servomechanism linear quadratic regulator (RSLQR). Results demonstrate that the proposed adaptive MPC achieves improved trajectory tracking and enhanced robustness under both nominal conditions and actuator degradation scenarios, including partial motor failure, while maintaining constraint satisfaction throughout all flight regimes.
Electric Vertical Take-Off and Landing (eVTOL) aircraft are poised to transform urban and regional mobility by offering zero-emission, congestion-free transportation. As regulatory frameworks evolve and advanced air mobility (AAM) gains traction, manufacturers are exploring propulsion strategies that improve range, power delivery, and overall system efficiency. A key challenge in eVTOL development is balancing range with payload capacity. While larger battery packs can extend range, they also increase system weight, reduce payload, and prolong charging times, limiting operational flexibility and turnaround time. Hydrogen fuel cells, supported by liquid hydrogen (LH₂) present a promising alternative for eVTOL propulsion. This study proposes a methodology for optimizing fuel cell propulsion systems tailored to eVTOL applications. A multi-physics modeling framework for eVTOL flight dynamics and propulsion system was developed, representing the target eVTOL configuration. For a defined flight path including vertical takeoff, hover, cruise, and landing, a Genetic Algorithm (GA) based optimization was conducted on propulsion system. The algorithm down-selected battery size, fuel cell stack specifications, and hydrogen tank capacity to meet mission requirements while minimizing propulsion system weight. The modeling framework was also used to evaluate trade-offs between payload and performance as functions of component sizing, battery chemistry and energy distribution strategy.
This study evaluates the impact of range extension on gross takeoff weight (GTOW) and energy cost for the NASA Lift+Cruise eVTOL configuration under present and near-term battery technology limitations. A baseline 8,210 lb, 6-passenger vehicle, originally designed for a 75-mile mission at 400 Wh/kg battery energy density, is shown to achieve only 15 miles at a more realistic 200 Wh/kg, largely due to the 20-minute SFAR reserve, which accounts for 64% of total onboard energy. To quantify the penalties of range extension, three sizing strategies are examined: fixed GTOW with payload trade-offs, fixed-geometry overloading, and fully co-scaled vehicle resizing. The co-scaled configurations reveal a strong nonlinear GTOW growth driven by an "adding battery to carry battery" effect, in which increases in GTOW necessitate heavier structure and propulsion, leading to a practical feasibility ceiling near 45 miles. Energy cost per payload-mile is found to be non-monotonic, reaching a minimum near 20-25 miles before increasing due to compounding weight penalties, contrary to trends predicted by fixed-weight analyses. Significant off-design penalties are also observed; operating a 40-mile aircraft on a 10-mile mission incurs a 72% energy cost increase relative to a co-scaled vehicle for 10 miles. Increasing battery energy density by 22.5% (to 245 Wh/kg) reduces the 6-passenger 40-mile design GTOW by 33% (from 13,712 lb to 9,187 lb) and shifts the optimal energy cost point outward to 30 miles. Ultimately, battery energy density is identified as the dominant parameter dictating the vehicle size, weight, performance, and the fundamental operational feasibility of UAM eVTOLs.
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