In this study, a combination of well-established algorithms and real-world data was implemented for the forward-looking problem of future vertiport network design in a large metropolitan city. The locations of vertiports were selected to operate urban air mobility (UAM) in the Seoul metropolitan area based on the population of commuters, and a noise priority route was created to minimize the number of people affected by noise using Aviation Environmental Design Tool (AEDT) software. Demand data were analyzed using survey data from the commuting population and were marked on a map using MATLAB. To cluster the data, the K-means algorithm function built in MATLAB was used to select the center of the cluster as the location of the vertiports, and the accuracy and reliability of the clustering were evaluated using silhouette techniques. The locations of the selected vertiports were also identified using satellite image maps to ensure that the location of the selected vertiports were suitable for the actual vertiport location, and if the location was not appropriate, final vertiports were selected through the repositioning process. A helicopter model was then used to analyze the amount of noise reduction achieved by the noise priority route, which is the route between the selected K-UAM vertiports compared to the shortest distance route. As a result, it was shown that the noise priority route that minimized the amount of noise exposure was more efficient than the business priority routes.

The need for civilian use of Unmanned Aerial Vehicles (UAVs) has drastically increased in recent years. Their potential applications for civilian use include door-to-door package delivery, law enforcement, first aid, and emergency services in urban areas, which put the UAVs into obstacle collision risk. Therefore, UAVs are required to be equipped with sensors so as to acquire Artificial Intelligence (AI) to avoid potential risks during mission execution. The AI comes with intensive training of an on-board machine that is responsible to autonomously navigate the UAV. The training enables the UAV to develop humanoid perception of the environment it is to be navigating in. During the mission, this perception detects and localizes objects in the environment. It is based on this AI that this work proposes a real-time three-dimensional (3D) path planner that maneuvers the UAV towards destination through obstacle-free path. The proposed path planner has a heuristic sense of $A^{\star }$ algorithm, but requires no frontier nodes to be stored in a memory unlike $A^{\star }$. The planner relies on relative locations of detected objects (obstacles) and determines collision-free paths. This path planner is light-weight and hence a fast guidance method for real-time purposes. Its performance efficiency is proved through rigorous Software-In-The-Loop (SITL) simulations in constrained-environment and preliminary real flight tests.

This study establishes design criteria for touchdown and liftoff (TLOF) pads, final approach and takeoff (FATO), safety areas, gates, and taxiways, which are components necessary for the operation of vertiports for urban air mobility (UAM), and analyzed vertiport capacity compliant with the arrangement of the components in a limited space. We used new vertiport design regulations from the Federal Aviation Administration (FAA) and European Union Aviation Safety Agency (EASA) for the vertiport design criteria. Vertiport components were sized based on Hyundai Motor’s S-A1 aircraft, and the layouts were classified as linear, satellite, and pier according to the arrangement of the TLOF pad and gate. The characteristics of each layout were analyzed for the same area. Based on these layouts, the parking space of Gimpo Airport that will be used for operating airport shuttles in the Seoul metropolitan area was measured and each layout was arranged to validate the characteristics of the layouts. Using the MATLAB program, we selected the most efficient layout among linear, satellite, and pier layouts, and estimated the TLOF pad and gate utilization rate. In addition, we evaluated the capacity of the two-story vertiport proposed by the Korea Airports Corporation for efficient use of space.

Small-scale unmanned aerial vehicles are being deployed in urban areas for missions such as ground target tracking, crime scene monitoring, and traffic management. Aerial vehicles deployed in such cluttered environments are required to have robust autonomous navigation with both target tracking and obstacle avoidance capabilities. To this end, this work presents a simple-to-design but effective steerable sensor platform and its implementation techniques for both obstacle avoidance and target tracking. The proposed platform is a 2-axis gimbal system capable of roll and pitch/yaw. The mathematical model that governs the dynamics of this platform is developed. The performance of the platform is validated through a software-in-the-loop simulation. The simulation results show that the platform can be effectively steered to all regions of interest except backward. With its design layout and mount location, the platform can engage sensors for obstacle avoidance and target tracking as per requirements. Moreover, steering the platform in any direction does not induce aerodynamic instability on the unmanned aerial vehicle in mission.

An optimization study of an electric vertical takeoff and landing personal air vehicle (eVTOL PAV) was performed during the conceptual design stage using the design of experiments method. In defining the initial problem, a design target parameter was set. The PAV subsystem was based on a configuration tradeoff study matrix, which was used to effectively conduct configuration selection. Initial sizing was performed using the PAV sizing program developed by this research team using Microsoft Excel and Visual Basic for Application (VBA). A screening test was performed to find parameters with high sensitivity among independent design parameters. The response surface method was used to model design target parameters, and a regression equation was estimated using the experimental design method. A Monte Carlo simulation was performed to confirm the feasibility of the generated model. To optimize the design independent parameter, a satisfaction function was selected, and the appropriateness of the data was determined using a Pareto plot and p-value.