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 Bezier 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. Comparison to flight on a holding pattern showed that the Bezier and Dubins route improved delay times and mitigated a cascading delay effect.

Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the sensor was performed using a Vector Network Analyzer (VNA) and a pair of interrogation antennas to capture resonance behavior under varying thermal and spatial conditions with sensitivities ranging from 0.052 to 0.20 MHz°C. Sensor calibration was conducted using a Long Short-Term Memory (LSTM) model, which leveraged temporal patterns to account for hysteresis effects. The calibration method demonstrated improved performance when combined with an LSTM model, achieving up to a 76% improvement in temperature estimation error when compared with Linear Regression (LR). The experiments highlighted an innovative solution for patch antenna-based non-contact temperature measurement, which addresses limitations with conventional methods such as RFID-based systems, infrared, and thermocouples.

Ground-based LiDAR sensing offers a promising approach for delivering short-range landing feedback to aerial vehicles operating near vertiports and in GNSS-degraded environments. This work introduces a detection system capable of classifying aerial vehicles and estimating their 3D positions with sub-meter accuracy. Using a simulated Gazebo environment, multiple LiDAR sensors and five vehicle classes, ranging from hobbyist drones to air taxis, were modeled to evaluate detection performance. RGB-encoded point clouds were processed using a modified YOLOv6 neural network with Slicing-Aided Hyper Inference (SAHI) to preserve high-resolution object features. Classification accuracy and position error were analyzed using mean Average Precision (mAP) and Mean Absolute Error (MAE) across varied sensor parameters, vehicle sizes, and distances. Within 40 m, the system consistently achieved over 95% classification accuracy and average position errors below 0.5 m. Results support the viability of high-density LiDAR as a complementary method for precision landing guidance in advanced air mobility applications.

Leader-follower formation flight is a technique used for teams of aircraft to increase area coverage of surveillance tasks, aerial refueling reliability, or fault-tolerance for multi-agent task hand-offs. Current approaches for a small Uncrewed Aerial System (sUAS) to intercept and track a leader vehicle lack explicit consideration of target interception heading or fixed-wing vehicle constraints. The current work utilized an adaptive cruise control system that included clothoid trajectory planning for a follower sUAS to intercept a target point behind a leader aircraft that requested heading and considered vehicle turn-rate constraints in a two-dimensional Dubins vehicle simulation. The impact is that sUAS adaptive cruise control can be used to improve area coverage and operating time in the required region for multi-agent systems which has direct applications in NASA's advanced air mobility mission that aims to develop air transportation systems in urban environments.

A passive wireless high-temperature sensor for far-field applications was developed for stable temperature sensing up to 1000 °C. The goal is to leverage the properties of electroceramic materials, including adequate electrical conductivity, high-temperature resilience, and chemical stability in harsh environments. Initial sensors were fabricated using Ag for operation to 600 °C to achieve a baseline understanding of temperature sensing principles using patch antenna designs. Fabrication then followed with higher temperature sensors made from (In, Sn) O2 (ITO) for evaluation up to 1000 °C. A patch antenna was modeled in ANSYS HFSS to operate in a high-frequency region (2.5–3.5 GHz) within a 50 × 50 mm2 confined geometric area using characteristic material properties. The sensor was fabricated on Al2O3 using screen printing methods and then sintered at 700 °C for Ag and 1200 °C for ITO in an ambient atmosphere. Sensors were evaluated at 600 °C for Ag and 1000 °C for ITO and analyzed at set interrogating distances up to 0.75 m using ultra-wideband slot antennas to collect scattering parameters. The sensitivity (average change in resonant frequency with respect to temperature) from 50 to 1000 °C was between 22 and 62 kHz/°C which decreased as interrogating distances reached 0.75 m.

Uncrewed Aerial Vehicle (UAV) remote and autonomous operation is a growing industry with applications in defense and the commercial space. Operational environments for UAVs such as urban canyons, forested areas, and indoor spaces can deny GPS signals and prevent a receiver from determining a position, which can hinder the vehicle's ability to safely follow flight paths. Unreliable positioning from GPS requires UAVs to have an alternative means of localization, which can be accomplished by utilizing ultra-wideband ranging to landmarks or other aerial systems. Machine learning can be used to train models to utilize unfiltered data from inter-vehicle distance sensors on the agent and anchors to determine the location of the agent without access to GPS. This work explores using machine learning as an adaptable localization system using artificial neural networks and gradient descent. Fully trained artificial neural networks will be an alternate localization method capable of learning specific noise models and performing cooperative localization using unfiltered intervehicle ranges and anchor positions to provide an alternative means of localization on a global scale.

Uncrewed vehicles require recovery routes to return autonomously to a mission after collision avoidance. The leading techniques for crewed aircraft such as ACAS do not directly translate to the explicit path definitions required for autonomous systems. Processes for automated route recovery such as the Path Recovery Automated Collision Avoidance System (PRACAS) are capable of returning a vehicle to an original mission, but has only been investigated for straight paths and the effects of interception point placement on recovery time has not been identified. Minimal deviation and recovery time is desired for maximum time and coverage in continuous monitoring tasks while ensuring safe operation in known airspace. The present work investigated the effects of intercept point placement on return time to mission routes in three simulation environments including a straight path, holding pattern, and a path generated from an exponential function to explore performance in common and curved paths and identified the first viable interception point. Clothoid return using the first viable interception point generated more cross track error and time to recover compared to non-linear guidance and pure pursuit, but had control on vehicle heading at path acquisition for smoother path following after interception. The general trend was that interception points for clothoid return farther along a path generated more cross track error and recover time, but had tighter control on acquisition heading. Impact of this work is an investigation of curved path interception within the PRACAS process to identify the first viable point meeting fixed wing turn rate limits to minimize path deviation so time spent in the desired operating region can be maximized where only straight paths had been previously considered.

Navigation in Global Positioning System-denied environments is notoriously difficult for small unmanned aerial vehicles due to reduction of visible satellites and urban canyon multipath interference. Several existing methods can be used for navigating in a constrained environment, but they often require additional specific sensing hardware for a localization solution or only provide local frame navigation. Autonomous systems often include LiDAR and RGB cameras for mapping, sensing, or obstacle avoidance. Utilizing these sensors for navigation could provide the only or complimentary localization solutions to other Global Positioning System-denied localization methods in a global or local frame, especially in urban canyons where unique landmarks can be identified. Information from scanning LiDAR can be correlated with camera pixel coordinates and used to range unique visual landmarks that have known locations. The present work included surface function fitting to reduce ranging error to spherical landmarks since multiple lasers were able to range each landmark. Simulation and experimental validation of the unique camera-LiDAR modified trilateration process was undertaken using colored light orbs as landmarks with a 16-laser scanning LiDAR and known positions. Position error was computed and verified that the position estimate process was successful at varying landmark configurations and viewing angles in simulation. Experimental results verified the process while also providing higher accuracy than a previous method of using a single point on landmark surfaces, for the tested setup.

Continuous curvature recovery paths are needed to accurately return a fixed wing autonomous vehicle with turn rate constraints back to a missions path in the correct direction after collision avoidance. Clothoid paths where curvature is linearly dependent to arc length can be used to make multi-segment splines with continuous curvature, but require optimization to ensure that the path is of minimal length while meeting curvature and sharpness limits. The present work considers the problem of returning a fixed wing aircraft back to its original path facing the correct direction after a leaving it during collision avoidance by presenting a method of optimizing a three segment clothoid spline to be of minimal length while meeting fixed wing turn rate constraints and targeting a path function. The impact of this work is enabling accurate path recovery after collision avoidance with minimal length paths that minimize the time spent off a missions planned route, giving better control over time of arrival for the planned route and more time to complete mission objectives.

View Video Presentation: https://doi.org/10.2514/6.2022-0275.vid Automated collision avoidance for small unmanned vehicles operating at low altitude can be challenging as a system may need to autonomously make decisions about safe path deviation to avoid aerial obstacles. Historical avoidance alert systems, such as TCAS and ACAS, specific to manned aircraft were designed with human operation in mind and have proven reliability. These collision avoidance methods may not be appropriate for autonomous systems with control and decision delays since human intervention can be immediately required. Additionally, a single operator may be monitoring and controlling several unmanned aerial systems at the same time and may not have the same level of situational awareness as a manned aircraft's pilot. This effort examined a commonly used path planning algorithm to generate collision avoidance paths along with a method for path recovery. Since the autonomous vehicle may be performing aerial data collection at specific pre-defined locations or may need to operate in known locations, returning to the original mission path in a direct and efficient manner becomes essential. Clothoids that consider vehicle turn rate were used to identify a return trajectory after avoiding a collision to provide less mission path deviation when compared to picking a single waypoint and following a straight line segment in two dimensional simulations. Collisions were detected using forward facing cones projected on two separate fixed-wing aircraft representing possible future flight paths. Cross track error with and without clothoid recovery paths was computed to evaluate the collision avoidance process with recovery and identify improvements. Results indicate that the addition of a recovery planning phase in a collision avoidance system provides less cross track error resulting in less time spent off the original planned path and tighter coverage of mission areas requiring continuous monitoring.

Through the Air Force’s Propulsion Outreach Program, undergraduate mechanical engineering students from Ohio University were tasked to complete the theoretical and technical work to solve an aerospace problem. The project incorporated multi-disciplinary design techniques to research and modify a small, gas turbine engine to allow operation where the airframe blocks the engine face requiring a duct to reach six inches from center. A conceptual design was developed from optimized parameters from literature, and analysis of functional prototypes was used to further optimize the S-duct’s performance. The final design utilized modular 3D printed sections to create the S-duct geometry. The final design was tested using the Ohio University engine test stand, and at a static, ground level test, the design achieved a total pressure recovery of 99.39%, a maximum thrust decrement of 0.49%, and an improved thrust specific fuel consumption while satisfying the six-inch offset requirement from the engine’s centerline.

Through the Air Force’s Propulsion Outreach Program, Ohio University mechanical engineering students were challenged to complete theoretical and technical work to solve an aerospace problem. The project incorporates multi-disciplinary design techniques to research and modify a small gas turbine engine to produce thrust vectoring and prevent windmilling for a specified drop-launch scenario. The team developed a conceptual design that incorporates the project requirements while also balancing the system for size, weight, and simplicity. The design implements advanced mechanical systems with electrical components by using one actuator to operate an extended nozzle to +/- 10 degrees of thrust vectored and a fairing that blocks air from entering the engine. The design was analyzed and tested evaluating thermal, material, and mechanical properties to ensure it would operate under the extreme conditions of the engine. After completing multiple iterations and design reviews, a prototype was created utilizing 3D printing and then assembled to demonstrate that the design accomplishes the project goals.

Anthropogenic noise is a ubiquitous feature of the American landscape, and is a known stressor for many bird species, leading to negative effects in behavior, physiology, reproduction, and ultimately fitness. While a number of studies have examined how anthropogenic noise affects avian fitness, there are few that simultaneously examine how anthropogenic noise impacts the relationship between parental care behavior and nestling fitness. We conducted Brownian noise playbacks for 6 h a day during the nesting cycle on Eastern Bluebird (Sialia sialis) nest boxes to investigate if experimentally elevated noise affected parental care behavior, nestling body conditions, and nestling stress indices. We documented nest attendance by adult females using radio frequency identification (RFID), and we assessed nestling stress by measuring baseline corticosterone levels and telomere lengths. Based on the RFID data collected during individual brood cycles, adult bluebirds exposed to noise had significantly higher feeding rates earlier in the brood cycle than adults in the control group, but reduced feeding rates later in the cycle. Nestlings exposed to noise had higher body conditions than the control nestlings at 11 days of age, but conditions equalized between treatments by day 14. We found no differences in nestling baseline corticosterone levels or nestling telomere lengths between the two treatment groups. Our results revealed that noise altered adult behavior, which corresponded with altered nestling body condition. However, the absence of indicators of longer-term effects of noise on offspring suggests adult behavior may have been a short-term response.

Autonomous aerial vehicle path following requires robust guidance commands for successful mission completion. Discrete waypoints are typically used, but only allow for straight line path following. Following smooth trajectories is desired so ground features or tight turns can be highly controlled which can not be achieved while using waypoint guidance. Vector field guidance providing heading commands representing the desired path can instead be used to provide smooth path following for fixed wing aircraft, but requires the use of carrot chasing for multi-rotor vehicles using position control where a point is placed in front of the vehicle along the commanded heading. The carrot chasing method can create lagged areas causing velocity oscillation and drift from the planned path on curved trajectories resulting in poor path fol-lowing performance without explicit velocity control. Components from vector field guidance field can be used to generate velocity control commands instead of position commands which rely on carrot chasing for multi-rotor aircraft. The present work investigates velocity control for vector field guidance enabling smooth path following for multi-rotor autonomous vehicles so carrot chasing with position control can be replaced. Comparisons between vector field velocity control and carrot chasing position control path following performance using cross track error are made to identify improvements and deteriorations. Results may be used to obtain smoother path following using vector field guidance and velocity control with multi-rotor autonomous systems resulting in shorter flight times and less energy consumption compared to carrot chasing methods.

Accurate localization estimation in a global or local frame is needed for a navigation solution which results in safe and efficient vehicle guidance. SLAM is a common approach to vehicle localization in GPS denied environments, but solutions often require odometry that may not be available to UAVs. ICP and Hector SLAM are common approaches for vehicle localization that rely on scan matching without the need for odometry, however both suffer when operating in long corridors with limited features for 2D single laser LiDAR scan matching. The present work injected localization solutions for the ICP method with known pose while the vehicle injection rate to velocity ratio was varied to explore their effects on average cross track error, accumulated position error, and to compare to Hector SLAM, another common method for scan matching based SLAM. The impact of the present work is that there has been an investigation on how vehicle velocity and known pose injection rate effect cumulative localization accuracy using scan matching ICP based SLAM techniques. Performance was measured using the cross track error and accumulative position error of ICP and Hector SLAM when compared to known vehicle position in simulation. Results may be used to identify system constraints such as maximum vehicle velocity for a successful mission completion given a known position injection rate and acceptable localization error.