Master of Sciences, Case Western Reserve University, 2021, EMC - Mechanical Engineering

Earthworm-like robots use peristaltic waves to locomote. While previous peristaltic turning used elliptical arcs, here Bezier curves enable optimization of paths while matching initial and final configurations. For the common case of a robot approaching a barrier with a passage that is offset from the original path, we show if the distance to the obstacle is less than 5 times the robot diameter, the forward reachable path offset is limited by the turning of the worm robot. We show the limit boundary and how to search backward motions if needed. Furthermore, we show how increasing number of segments affects turning; the difference between the path of the robot's anterior and posterior are increased. Finally, when verified on a physical worm-like robot, the total motion was 85% of simulation (due to slip), but the path shape is similar and ratio of offset to clearance distance was within 7% of predictions.

MS, University of Cincinnati, 2021, Engineering and Applied Science: Mechanical Engineering

In Cooperative Localization, agents share their sensor information with each other for collective state estimation. Observability of the system plays a key role in maintaining localization accuracy, irrespective of the estimation technique used. Since the observability of nonlinear systems is state-dependent, controllers can be developed to solve for states that improve observability of the system. In this thesis, we solve such a problem in relative framework. For missions like aerial refueling, landing, formation flying etc., knowledge of relative pose is more important for successful execution of the mission. When flying through GPS-denied hostile environments, the agents must estimate their relative pose with respect to each other using on-board sensors. Depending on the nature of the mission, agents can be constrained to move in certain trajectories that can make the system unobservable, because of which estimation errors accumulate over time. In such cases, additional vehicles can be introduced to provide more measurements, which improves localization accuracy. Furthermore, the path followed by these vehicles can improve the observability of the system. Therefore, we perform a detailed nonlinear observability analysis of the N-vehicle system and derive a cost function from the observability gramian (O'O). We solve for trajectories of these additional supporting vehicles that maximize this cost using Trajectory Optimization coupled with Model Predictive Control (MPC) approach. When multiple supporting vehicles are involved, we can solve for the trajectories of all the vehicles collectively or solve for each vehicle individually. We present simulation results using MATLAB/Simulink that demonstrates the effectiveness and consistency of the controller developed. We show that the distributed approach offers the same accuracy with low computation time compared to the centralized approach. We also study the effect of the number of additional supporting vehicles (open full item for complete abstract)

Master of Science in Electrical Engineering, University of Dayton, 2020, Electrical and Computer Engineering

The purpose of this thesis is to produce a closed-loop feedback mission planning tool that allows for the operator to control multiple Unmanned Aerial Vehicles (UAV) within a mission. Different styles of UAVs and mission planners that are available on the market were evaluated and selected for their cost, size, ability to customize, and fit for mission work. It was determined that commercially available mission planners do not provide the level of automation required, such as allowing for different algorithms for assigning UAV tasks and for planning UAV flight paths within a mission. Comparisons were made between different algorithms for path planning and tasking. From these comparisons, a bio-inspired machine-learning algorithm, Genetic Algorithm (GA), was chosen for assigning tasks to UAVs and Dubins path was chosen for modeling UAV flight paths within the mission simulation. Since market mission planners didn't allow for control of multiple UAVs, or wouldn't allow for the operator to add algorithms to increase usability and automation of the program, it was decided to create a Graphic User Interface (GUI) that would allow the operator to customize UAVs and the mission scenario. A test mission scenario was then designed, which included 9 Points of Interest (POI), 1 to 3 Targets of Interest (TOI), 3 to 5 UAVs, as well as simulation options that modeled failure of a task or a UAV crash. Operator feedback was incorporated into the simulation by allowing the operator to determine a course of action if a failure occurred, such as reprogramming the other UAVs to complete the tasks left by the crashed UAV or reassessing a failed task. Overall mission times decreased for reprogramming the UAVs versus running a separate mission to complete any tasks left by the crashed UAV. Additional code was added to the GA and Dubins path to increase speed without decreasing solution fitness.

Doctor of Philosophy, The Ohio State University, 2020, Mechanical Engineering

Autonomous driving is gaining popularity in research interest and industry investment over the last decade, due to its potential to increase driving safety to avoid driver errors which account for over 90% of all motor vehicle crashes. It could also help to improve public mobility especially for the disabled, and to boost the productivity due to enlarged traffic capacity and accelerated traffic flows. The path planning and following control, as the two essential modules for autonomous driving, still face critical challenges in implementations in a dynamically changing driving environment. For the local path/trajectory planning, multifold requirements need to be satisfied including reactivity to avoid collision with other objects, smooth curvature variation for passenger comfort, feasibility in terms of vehicle control, and the computation efficiency for real-time implementations. The feedback control is required afterward to accurately follow the planned path or trajectory by deciding appropriate actuator inputs, and favors smooth control variations to avoid sudden jerks. The control may also subject to instability or performance deterioration due to continuously changing operating conditions along with the model uncertainties. The dissertation contributes by raising the framework of path planning and control to address these challenges. Local on-road path planning methods from two-dimensional (2D) geometric path to the model-based state trajectory is explored. The latter one is emphasized due to its advantages in considering the vehicle model, state and control constraints to ensure dynamic feasibility. The real-time simulation is made possible with the adoption of control parameterization and lookup tables to reduce computation cost, with scenarios showing its smooth planning and the reactivity in collision avoidance with other traffic agents. The dissertation also explores both robust gain-scheduling law and model predictive control (MPC) for path followi (open full item for complete abstract)

Doctor of Philosophy (PhD), Ohio University, 2020, Electrical Engineering & Computer Science (Engineering and Technology)

In this dissertation, the problem of trajectory design for autonomous wheeled ground vehicles are investigated. Several line-of-sight (LOS) based trajectory design approaches are developed to solve the problem in various practical scenarios. For path planning of on-road driving, a LOS pure pursuit guidance (PPG) path planner is designed. Stability analysis for LOS PPG along a general reference path is conducted based on Lyapunov stability theory. By using the theoretical analysis results, a design guideline for the selection of the guidance parameters is derived. The geometric interpretation of LOS PPG for general guidance parameters is provided. Then, for a given feasible, collision-free path, the problem of converting the geometric path to a time-parameterized trajectory is studied. A novel receding-horizon type sub-optimal path-to-trajectory conversion algorithm is developed which is able to take into account dynamic constraints and has high computational efficiency. For the problem of path planning for autonomous car-like ground vehicle parking, a novel four-phase path planning algorithm is developed. The algorithm is able to cope with various parking scenarios in a unified, scalable manner with low computational cost. The four-phase algorithm is extended to standard N-trailer parking and a novel cascade path planning algorithm is developed. Besides the advantages inherited from the four-phase algorithm, the cascade algorithm for standard N-trailer parking is able to prevent jackknife phenomenon.

Master of Science (MS), Wright State University, 2019, Human Factors and Industrial/Organizational Psychology MS

This research expands upon the research conducted by Clark and Wilkes-Gibbs (1986) on how individuals collaborate and reach common ground in the domain of objects into the domain of action. Pairs of participants (N = 22) were asked to complete a set of six maneuvers with a remote-control car. Dialogue was transcribed and analyzed for total word count, verb phrase count, number of turns taken, number of errors committed, and selected other linguistic characteristics. Total word count, verb phrase count, number of turns taken, and number of errors committed all significantly decreased over time, either linearly or logarithmically. This research shows support for a general distinction between path and manner verbs by showing different associated language patterns for the different verb types. A key finding in this study is that learning of path maneuvers is dependent on learning features in the environment, whereas this is not important in manner maneuvers.

PHD, Kent State University, 2015, College of Arts and Sciences / Department of Modern and Classical Language Studies

Abstract This dissertation presents a comprehensive translator development evaluation framework that can be used for the evaluation of translator development in translation organizations. The proposed framework consists of three important constructs: a Translator Development Model (TDM), holistic rubrics that present levels of the development of descriptors of translator competences identified in the TDM, and a Translator Career Path (TCP). In this qualitative study, the methodology to collect and analyze the data takes an inductive approach that draws upon the literature to propose a Translator Development Model (TDM). This model is based on descriptors of expert translator performance and best employees' practice documented in the literature. The proposed TDM consists of five categories of translator development: declarative knowledge, procedural knowledge, psycho-physiological abilities, communication abilities, and professional abilities. Each category comprises a number of criteria important for the development of the translator. After these criteria are identified and arranged in the model, they are graded in holistic scales. The Dreyfus Model of Skills Acquisition is the conceptual framework used to build those scales. Each skill in the TDM will be graded as: novice, advanced beginner, competent, proficient, and expert. Consequently, the Translator Career Path (TCP) is built based on the TDM and its holistic scales. Thus, five levels of translator performance are identified in the TCP as five ranks. The first rank is the intern translator, which is equivalent to the novice level. The second rank is the assistant translator, which is equivalent to the advanced beginner level. The third rank is the associate translator, which is equivalent to the competent level. The fourth rank is the translator, which is equivalent to the proficient level. Finally, the fifth rank is the expert translator, which is equivalent to the expert level in the TDM scales. The main (open full item for complete abstract)

Doctor of Philosophy (PhD), Wright State University, 2007, Computer Science and Engineering PhD

Wavelength division multiplexing (WDM) optical networks are widely viewed as the most appropriate choice for future Internet backbone with the potential to fulfill the ever-growing demands for bandwidth. WDM divides the enormous bandwidth of an optical fiber into many non-overlapping wavelength channels, each of which may operate at the rate of 10 Gigabit per second or higher. A failure in a network such as a cable cut may result in a tremendous loss of data. Therefore, survivability is a very important issue in WDM optical networks. The objective of this dissertation is to address the survivability provisioning problem in WDM optical networks under a scheduled traffic model and a sliding scheduled traffic model that we propose. In contrast to the conventional traffic models considered in communication networks such as static traffic model and dynamic random traffic model, the scheduled traffic model and the sliding scheduled traffic model are able to capture the traffic characteristics of applications that require capacity on a time-limited basis. They also give service providers more flexibility in provisioning the requested demands and a better opportunity to optimize the network resources. The survivability provisioning problem is to determine a pair of link-disjoint paths under the link failure model or a pair of SRLG-disjoint paths under the Shared Risk Link Group (SRLG) failure model, one working path and one protection path, for each demand in a given set of traffic demands with the objective of minimizing the total resources used by all traffic demands while 100% restorability is guaranteed against any single failure. To provision survivable service under the scheduled traffic model, we develop two sets of integer linear program (ILP) formulations for joint and non-joint optimizations using different protection schemes such as dedicated and shared path based protections. We also design a capacity provision matrix based Iterative Survivable Routing (ISR) alg (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2010, Business Administration

I study the shortest path - capacitated maximum covering problem (SP-CMCLP). Current, ReVelle and Cohon (1985) first studied the un-capacitated version of this problem. The two objectives of the problem are the minimization of the path length from a predetermined starting node to a predetermined terminal node and the maximization of the total demand covered by the facilities located at the nodes in the path. They solved a special case in which a demand can be covered only if it is located on the path. I solve the general model. I also introduce facility capacity constraints, new algorithms and new demand coverage structures to this problem. I decompose the problem into a k-shortest path problem (kSP) and a capacitated maximum covering problem (CMCLP). The k-shortest path problem is solved by a path deletion algorithm. The capacitated maximum covering problem is solved by various heuristics and meta-heuristics including lagrangian relaxation, two versions of Tabu search and a simulated annealing method. To the knowledge of the author, the Tabu search and simulated annealing methods introduced are the first meta-heuristics developed for the capacitated maximum covering problem. In these meta-heuristics, I use four neighborhood structures. These are 1) one-interchange which exchanges an selected facility with an unselected facility, 2) client shift which shifts a satisfied demand from one selected facility to another selected facility, 3) demand swap (or demand reallocation) which swaps one (or more) assigned demand node (nodes) with one (or more) unassigned demand node (nodes) within the coverage distance of a selected facility site, 4) demand addition which adds one or more unassigned demand to a selected facility. I design an embedded meta-heuristic procedure which has inner loops of single neighborhoods and an outer loop of multiple alternate inner loops. I design a heuristic method and a penalty method for the demand allocation sub-problem in the embedded Tabu s (open full item for complete abstract)

Master of Science (M.S.), University of Dayton, 2013, Mechanical Engineering

This thesis develops techniques that address the design of planar four-bar linkages for tasks common to pick-and-place devices, used in assembly and manufacturing operations. The analysis approaches relate to two common kinematic synthesis tasks, motion generation and path-point generation. Motion generation is a task that guides a rigid body through prescribed task positions which include position and orientation. Path-point generation is a task that requires guiding a reference point on a rigid body to move along a prescribed trajectory. Pick-and-place tasks often require the exact position and orientation of an object (motion generation) at the end points of the task. Within the range of movement, the motion restrictions are less rigorous with only the position of the object (path generation) being specified to either avoid obstacles or provide direction for a suitable path. Established synthesis theory has been developed for either motion generation or path-point generation tasks. This thesis presents four-bar linkage synthesis methods for tasks that include a combination of motion and path-point generation. This synthesis challenge is addressed via two approaches: Geometric Constraint Programming (GCP) and numerical solutions to synthesis equations. Using GCP, a step-by-step methodology has been established to find solutions to these synthesis challenges. This technique provides a synthesis process that is intuitive, visual, and avoids the need for the designer to engage in solving complex equations, The drawback to kinematic synthesis using GCP, however, is that only one linkage solution is obtained and sketched by the designer. Using numerical methods, techniques are presented to formulate the kinematic chain constraint equations and solve for the appropriate link lengths and pivot locations. Numerical solutions are generated by the Bertini software package, a program that supports the calculation of large polynomial equations set. Examples of various combina (open full item for complete abstract)

MS, Kent State University, 2025, College of Arts and Sciences / Department of Computer Science

Given an undirected, weighted, and labeled data graph 𝐺, a source node 𝑠, a desti- nation node 𝑡, and an ordered sequence of query keywords, a Keyword-Constrained Shortest Path (KCSP) query retrieves the shortest path that passes some vertices with query keywords in order. Existing methods for processing such queries have limita- tions: (i) they fail to handle arbitrary keyword constraints efficiently; and (ii) they rely heavily on exhaustive search techniques that are computationally expensive. Inspired by these challenges, we propose a learning-based framework for efficient processing of KCSP queries. A pre-processing step offline builds indexes and pre-trains learning- based models to accelerate online KCSP query performance. Our proposed approach integrates path pruning and neural network-based prediction to reduce computational cost, eliminates the need for exhaustive traversal, and allows efficient query processing in large-scale graphs by combining machine learning with graph algorithm optimiza- tions. Extensive experiments have been conducted to evaluate our proposed KCSP approach over both Oldenburg and Synthetic graphs, in terms of the query efficiency and scalabili

Doctor of Philosophy (Ph.D.), University of Dayton, 2024, Electrical and Computer Engineering

Additive manufacturing (AM) technology is rapidly advancing across diverse fields. For instance, the use of robotic arms in various AM processes has led to significant gains in printing flexibility and manufacturing scalability. However, despite these advancements, there remains a notable research gap concerning the mechanical properties of parts 3D-printed with robotic arms. This study focuses on developing a robotic fused filament fabrication (FFF) 3D-printing process with a layer resolution of 50 μm to 300 μm. The impact of the robotic printing process on the mechanical properties of printed parts is investigated and benchmarked against a commercial FFF 3D printer. In addition, we propose a novel tool path that can vary contour layer thickness within an infill layer to improve mechanical strength by minimizing air gaps between contours. SEM images suggest that this new tool path strategy leads to a significant reduction in the fraction of the void area within the contours, confirmed by a nearly 6% increase in the ultimate tensile strength. Furthermore, a novel strategy for non-planar contours is proposed, specifically designed for thin-shell 3D models. This approach aligns tool paths parallel to the Z-axis, organized into triangular segments, and utilizes planar slicing techniques. The method involves segmenting the point cloud and systematically printing non-planar contours on top of the planar contours. Axial compression testing reveals that samples produced using this strategy exhibit mechanical properties comparable to those of conventional 3D printing. However, distinct fracture patterns are observed: in conventional 3D-printed samples, fractures occur on both inner and outer surfaces, while in non-planar printed samples, fractures are confined to the inner surfaces (planar contours) and do not propagate to the outer non-planar contours. This demonstrates the potential of non-planar printing for improved structural integrity.

Doctor of Philosophy, Case Western Reserve University, 2025, Management

This dissertation explores the persistent gender gap in partnerships at Big Four public accounting firms, employing a mixed-methods approach to examine both individual experiences and career trajectories. Despite women entering the profession at equal rates to men, only about 23% of partners are women. This study aims to understand why this disparity persists and how career paths influence partnership attainment. Study 1 utilized qualitative interviews with 11 female advisory partners to investigate their lived experiences in reaching partnership. Findings revealed a common internal mental model for evaluating the partnership career path, consisting of three iterative phases: partner inquiry, developing partner behaviors, and solidifying a partner identity. This process highlights the importance of personal reflection and identity development in pursuing partnership. Study 2 employed Optimal Matching Analysis to quantitatively examine the career trajectories of 312 partners (159 women, 153 men) across audit, tax, and consulting functions. Results showed no significant gender differences in time to partnership or career transitions. However, distinct patterns emerged among business units, with audit and tax partners typically achieving partnership faster than consulting partners. Notably, only about 40% of partners followed traditional linear career paths within their initial function. The integrated analysis of these findings revealed a complex interplay between individual mindsets and organizational structures in shaping the path to partnership. Key elements include early career goals, professional socialization, self-managed career progression moderated by sponsorship, and making a strong business case for partnership. This analysis highlighted that while women and men who make partner do so in similar timeframes, the journey to partnership involves navigating psychological transitions, organizational changes, and social dynamics that (open full item for complete abstract)

Master of Sciences, Case Western Reserve University, 2024, EECS - Computer and Information Sciences

The process of handcrafting robotic spray-painting instructions is arduous, time-consuming, and inefficient. Nonetheless, these painting instructions are required to paint non-uniform vector graphics as road markings on the roadway. Therefore, an automated path planning process is desirable and necessary to translate arbitrary vector graphics into executable spray-painting instructions which meet the specific requirements of road painting robotic systems. Unfortunately, robotic spray-painting and associated path planning processes are not trivial; having many complexities and constraints. This paper presents an introduction to the problem, related research, an automated path planning technique, and an associated web application for visualizing, editing, post-processing, and exporting the automatically generated painting instructions.

Doctor of Philosophy, Miami University, 2025, Psychology

Binge eating is related to significant impairment as well as societal and financial costs, and occurs in almost half of all young adults. Only a minority of individuals with binge eating seek treatment for the illness, potentially due to fears of experiencing weight stigma in healthcare contexts, as most individuals with binge eating are larger-bodied. Weight stigma itself is associated with higher rates of binge eating, and interoceptive dysfunction could be one mechanism that facilitates the weight stigma-binge eating association. Hence, the aim of the current study was to test whether interoceptive dysfunction mediates the relation between weight stigma and binge eating in an experimental context. We hypothesized that: a) participants randomly assigned to experience weight stigma will engage in higher levels of binge eating than participants who do not; b) the relation between weight stigma and caloric consumption will be mediated by interoceptive dysfunction; and c) objective gastric interoceptive dysfunction will be a stronger mediator of the relation between weight stigma and caloric consumption than subjectively reported interoceptive dysfunction. Binge eating was measured via self-report, and caloric consumption via a behavioral paradigm (a yogurt taste test). Participants (n = 135; 80.7% White; 72.6% women) were adults aged 18 to 25 years old recruited from Miami University who reported subclinical binge eating (at least one overeating episode per month, with or without loss of control). Sixty-six participants were randomly assigned to the to the experimental condition (i.e., the weight stigma manipulation) whereas the rest were assigned to the control condition (n = 69). To test our hypotheses, we tested a path model including our condition variable, three mediator variables, and outcome variables in MPlus with 10,000 bootstraps. Contrary to our hypotheses, exposure to weight stigma was not related to increased binge eating or disordered eating urges, and (open full item for complete abstract)

PhD, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

The path planning and power management of hybrid fuel UAVs under presence of noise-restrictions is studied here. This problem is motivated by two scenarios: i) widespread use of UAVs in congested, urban environment; and ii) Noise-sensitive surveillance missions. In either case, it is envisioned that noise-restrictions are in place in subsets of the environment, such that ground-level noise produced by the UAV at hand must be under a certain intensity. In the case of urban usage, we consider it likely that such restrictions are eventually put in place near residential and business areas. In the case of a hybrid-fuel UAV, where energy sources include a battery-pack and combustion engine, the noise produced by the engine is intense relative to the propeller noise. In this scenario, the path planning and power planning is a coupled problem: given a path, certain power plans are infeasible, and given an energy plan certain paths are infeasible. Thus, the path of the UAV must be found in tandem with the power plan. This results in a novel problem, which we study here. The single-agent problem is studied first within a discrete framework, as is standard for vehicle motion planning. An environment is discretized into a graph, such that nodes represent locations in the configuration space and edges between the nodes are flight legs the UAV travels along between nodes. Edges are parameterized by cost and energy values. The objective is to find a feasible sequence of nodes of lowest cost without violating the power and noise constraints. We develop a fast, exact algorithm to solve this planning problem quickly on graphs of tens of thousands of nodes. The problem is approached in an optimal control framework, with only an initial approach presented in this dissertation. Battery modeling in the context of this problem is also studied briefly. The final piece of work is returning to the discrete problem in the context of multi-agent path finding (open full item for complete abstract)

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

This thesis investigates the development of a sophisticated strategy for capturing a ground target moving unpredictably through a road network in a secure area. The approach assumes that the target, which does not perform evasive maneuvers, can be tracked using strategically deployed unattended ground sensors (UGS) that log and timestamp its movements. A high-altitude Unmanned Aerial Vehicle (UAV) acts as the pursuer in this critical search and capture mission, relying solely on intelligence from the UGS it comes into contact with. We introduce a state-of-the-art deep reinforcement learning method aimed at enabling the UAV to effectively intercept the target by utilizing the fragmented information from the sensors. This novel strategy trains the UAV to autonomously decide its course of action, driven by a reward system rooted in a pursuit-evasion game, despite having only partial information. Our results demonstrate a perfect success rate in capturing targets that move at a constant speed across the secure area under perfect sensor conditions, and a notable 97% success rate even when facing a 10% chance of sensor data inaccuracies.

Master of Science, The Ohio State University, 2024, Electrical and Computer Engineering

As manufacturing techniques such as topology optimization and additive manufacturing develop, components with increasing geometric complexity are becoming more common. Thus, it is necessary to develop automated non-destructive evaluation techniques that are adaptable to various surface geometries. This project seeks to leverage robotic simulation software to virtually plan and optimize eddy current inspections of various airplane components to detect flaws while eliminating false positives. The final deliverable will be a Robot Operating System (ROS) software package that generates an optimal tool path plan based on probe output for various scan resolutions.

Master of Science (M.S.), University of Dayton, 2023, Mechanical Engineering

This thesis presents the research that has been done in advancing topics in multi-robot coordinated path planning and singularity avoidance of mechanisms. For coordinated robots, an offline path planning solution has been developed that incorporates manufacturing constraints while taking into account the manipulator's kinematics and collision constraints. A loading dock optimization problem is first tackled due to it being a simpler system with one degree of freedom (DOF) while keeping the collaborative nature intact. Then the focus is shifted to spatial robots having 3 prismatic and/or revolute joints. This includes a discussion on the kinematics of the robots, the task allocation using a Tabu-Search Heuristic, and collision avoidance routines. The 3P robots have a one-to-one inverse kinematic solution with a unique configuration for any point within the workspace. This allows for a less computationally expensive optimization model. Finally, the path planning solution is applied to N overlapping 5R robots that have increased computational complexity due to one-to-many inverse kinematic solutions. As the number of links of the robot increases, the effort for combinatory collision checking routine explodes. Several simulations are presented to validate the proposed methodology. The research on singularity avoidance focuses on finding an actuating chain that can be attached to a mechanism to drive it in a singularity-free manner. For a single degree of freedom spatial mechanism, a reference frame attached to any of its links produces a continuous motion of this frame. Given the progression of this frame from the start through the end of the mechanism's motion, this research seeks to identify specific points relative to this moving reference frame. The points of interest are those that can be coupled with a second point determined in the fixed frame to act as the end joint locations for a spherical-prismatic-spherical (SPS) driving chain. If the selection of the poi (open full item for complete abstract)

Master of Science (MS), Ohio University, 2023, Mechanical Engineering (Engineering and Technology)

Autonomous uncrewed aircraft will require collision avoidance systems designed with autonomy in mind as they integrate into the increasingly crowded national airspace system. Current uncrewed aircraft collision avoidance systems typically require a remote pilot to execute avoidance or provide poorly defined guidance that does not benefit autonomous systems. Path Recovery Automated Collision Avoidance System re-plans flight paths to adjust to collisions autonomously using path planners and keep out zones but does not currently detect or mitigate overtaking collisions. This work investigates the effect of geometric keep out zones on the overtaking scenario for autonomous uncrewed aircraft. Keep out zone shapes were developed by relating relative velocities and turn rates of the aircraft in the overtaking scenario and tested using the Path Recovery Automated Collision Avoidance System. Operational ranges for approach heading, relative velocity, and look-ahead time were then determined. The developed set of keep out zones prevented intruder aircraft from entering the minimum separation distance of one wingspan of the mission aircraft in the overtaking scenario for scenarios with look-ahead times between five and twelve seconds, relative velocities of two to twenty, and approach angles between 110◦ and -110◦ measured from the heading of the main UAS. Minimum separation was maintained for low speed encounters with relative velocities between 1.1 and 2.0 for look-ahead times between two and eight seconds for all approach angles. With a look-ahead time range of five to eight seconds, overtaking collisions of all tested approach angles and relative speeds are handled with more than twice the separation required for success, showing that the developed keep out zones are feasible for implementation on possible autonomous collision avoidance systems.