Diploma thesis focuses on a motion control of a mobile station in the simulation environment OPNET Modeler (OM). The thesis is summarized and described the possibilities of controlling the motion of a wireless mobile station. A significant part concentrates on the motion controlling of the station via an external file containing the path of movement. In this type of governance is a continuous movement of the station. The movement is driven by a simulation engine OM. External files designed for a motion control of the station can be created manually or used to write a data directly from OM to store the random generated routes for later use. For the manual creation of the trajectory is chosen the MATLAB tool that handles pre-created maps in the form of the input image into a form that the output of the tool can be used for motion control station in OM. To automate the process of creation and the subsequent trajectory motion control station is used by her library MATLAB Engine. It allows to call MATLAB functions from the language C/C ++ on which is based OM. For automated process of the movement control of the station is used a direct manipulation with the station position which is an alternative of the motion control station according to a set of the trajectory. The direct manipulation station position provides discrete movement of the station. The user’s created process which uses the discrete trajectory values manage the control.

The aim of this work is to study and theoretically process existing movement options of station in OPNET Modeler environment. These options are described in first part. Furthermore to design and implement function to display path obtained from real-world conditions and done to station following this route during simulation. Coordinates used to move station are obtained by GPX format, which is used to store clearly the GPS coordinates. Second part is devoted to describing creation of this function, first using PHP script, then direct implementation into OPNET Modeler environment using C++.

<div> <div> <div> <p>As cislunar and outer space exploration regains worldwide popularity, the low-thrust spacecraft technology, whether in the form of solar sails, electric propulsion or nuclear propulsion, has seen a major increase in the last two decades, as new technologies arise that not only seek for a reduction of the size of the spacecraft —and/or the payloads— but also to minimize the cost of spaceflights, while trying to approach further destinations in our solar system. Mission designers are being challenged with the need to develop new strategies to generate rapid and informed initial guesses for low-thrust spacecraft trajectory design, that are easily converged into fully continuous solutions in position, velocity and mass states, in a high-fidelity dynamical model that incorporates the true ephemerides and perturbations of the gravitational attracting bodies acting on the spacecraft as it navigates through space. </p> <p>In an effort to explore further mission options for spacecraft traveling in the lunar vicinity, new interest arises into the problem of constructing a general framework for the initial guess generation of low-thrust trajectories in cislunar space, that is independent of the force models in which the orbits of interest are de ned. Given the efficiency of the low-thrust engines, most vehicles are equipped to perform further exploration of the cislunar space after completion of their primary science and technology demonstrations in orbits around the Moon. In this investigation, a generalized strategy for constructing initial guesses for low-thrust spacecraft traveling between lunar orbits that exist within the context of multiple dynamical models is presented. These trajectories are converged as mass-optimal solutions in lower fidelity model, that are easily transitioned and validated in the higher-fidelity ephemeris model, and, achieve large orbital plane changes while evolving entirely within the cislunar region. </p> <p>The robustness of the initial guess generation of the spacecraft’s path, depends highly on the fidelity of the dynamical model utilized to construct such trajectories, as well as on the numerical techniques employed to converge and propagate them into continuous solutions. Other researchers have extensively investigated novel techniques for the generation of initial guesses for the low-thrust spacecraft trajectory design problem including, but not limited to, patched conics strategies, methodologies for the transformation of impulsive burns into nite burns, the orbit chaining framework and, more recently, artificial intelligence schemes. This investigation develops an adaptive orbit chaining type approach that relies on the energy parametrization of periodic orbits that exist within the context of the circular restricted three-body problem, to construct informed initial guess for the low-thrust spacecraft trajectory.</p> <div> <div> <div> <p>A variety of multiple transfer applications for vehicles traveling between orbits in the cislunar region is explored for a wide range of low-thrust spacecraft with varying thrust acceleration magnitude. The examples presented in this investigation are consistent with the low-thrust parameters of previously own missions that utilized the same propulsion capabilities, such as, the DAWN mission and the Japanese Hayabusa missions 1 and 2. The trajectories presented in this work are optimized for either propellant consumption or time- of-flight in the lower-fidelity model, and later transitioned into a higher-fidelity ephemeris model that includes the gravitational attraction of the Sun, the Earth and the Moon. </p> <p>Two strategies are explored for the transition of trajectories from a lower-fidelity model to the higher-fidelity ephemeris model, both of which are successful in retaining the transfer geometry. The framework presented in this investigation is further applied to the upcoming NASA Lunar IceCube (LIC) mission to explore possible extended mission options once its primary science and technology demonstration objectives are achieved. It is demonstrated in this investigation that the strategies developed and presented in this work are not only applicable to the specific low-thrust vehicles explored, but it is applicable to any spacecraft with any type of propulsion technology. Furthermore, the energy-informed adaptive algorithm is easily transition to generate trajectories in a range of varying dynamical models. </p> </div> </div> </div> </div> </div> </div>

The goal of this thesis is to create an artificial agent for an autonomous racing vehicle. This project is inspired by the F1/10 racing competition. The agent uses a planning algorithm to find a time-optimal trajectory. To achieve real-time performance, the agent analyzes the map of the track and it plans only for the next two corners immediately ahead of the vehicle. The agent re-plans several times per second as it drives along the circuit to account for imprecise trajectory following. We successfully tested the agent in the Gazebo simulator with good results. We also tested the algorithm on a custom car-like robot equipped with an on-board computer and sensors, but with limited success. 1

<div>In recent decades the revolutionary possibilities of low-thrust electric propulsion have been demonstrated by the success of missions such as Dawn and Hayabusa 1 and 2. The efficiency of low-thrust engines reduces the propellant mass required to achieve mission objectives and this benefit is frequently worth the additional time of flight incurred, particularly for robotic spacecraft. However, low-thrust trajectory design poses a challenging optimal control problem. At each instant in time, spacecraft control parameters that minimize an objective, typically propellant consumption or time of flight, must be determined. The characteristics of low-thrust optimal solutions are often unintuitive, making it difficult to develop an <i>a priori</i> estimate for the state and control history of a spacecraft that can be used to initialize an optimization algorithm. This investigation seeks to develop a low-thrust trajectory design framework to address this challenge by combining the existing techniques of orbit chaining and direct collocation. Together, these two methods offer a novel approach for low-thrust trajectory design that is intuitive, flexible, and robust.</div><div><br></div><div>This investigation presents a framework for the construction of orbit chains and the convergence of these initial guesses to optimal low-thrust solutions via direct collocation. The general procedure is first demonstrated with simple trajectory design problems which show how dynamical structures, such as periodic orbits and invariant manifolds, are employed to assemble orbits chains. Following this, two practical mission design problems demonstrate the applicability of this framework to real world scenarios. An orbit chain and direct collocation approach is utilized to develop low-thrust transfers for the planned Gateway spacecraft between a variety of lunar and libration point orbits (LPOs). Additionally, the proposed framework is applied to create a systematic method for the construction of transfers for the Lunar IceCube spacecraft from deployment to insertion upon its destination orbit near the Moon. Three and four-body dynamical models are leveraged for preliminary trajectory design in the first and second mission design applications, respectively, before transfers are transitioned to an ephemeris model for validation. Together, these realistic sample applications, along with the early examples, demonstrate that orbit chaining and direct collocation constitute an intuitive, flexible, and robust framework for low-thrust trajectory design. </div>

An unmanned aerial vehicle (UAV) swarm allows for a more time-efficient method of searching a specified area than a single UAV or piloted plane. There are a variety of factors that affect how well an area is surveyed. We specifically analyzed the effect both vehicle properties and communication had on the swarm search performance. We used non-dimensionalization so the results can be applied to any domain size with any type of vehicle. We found that endurance was the most important factor. Vehicles with good endurance sensed approximately 90% to 100% of the grid, even when other properties were lacking. If the vehicles lacked endurance, the amount of area the vehicles could sense at a given time step became more important and 10% more of the grid was sensed with the increase in sensed area. The maneuverability of the vehicles was measured as the vehicles' radii of turn compared to the search domain size. The maneuverability mattered the most in the middle-range endurance cases. In some cases 30% more of the grid was searched with improving vehicle maneuverability. In addition, we also examined four communication cases with different amounts of information regarding vehicle location. We found communication increased search performance by at least 6.3%. However, increasing the amount of information only changed the performance by 2.3%. We also studied the impact the range of vehicle communication had on search performance. We found that simulations benefited most from increasing the communication range when the amount of area sensed at a given time step was small and the vehicles had good maneuverability. We also extended the optimization to a multi-objective process with the inclusion of target tracking. We analyzed how the different weightings of the objectives affected the performance outcomes. We found that target tracking performance dramatically changes based on the given weighting of each objective and saw an increase of approximately 52%. However, the amount of the grid that was sensed only dropped by approximately 10%.

Indiana University-Purdue University Indianapolis (IUPUI) / Pain prevalence in nursing home (NH) residents is high. Studies report up to 85.0% of NH residents experience pain and up to 58.0% experience persistent pain. Pain in NH residents can lead to decreased happiness, quality of life, and life satisfaction. Traditionally, pain has been studied in relation to specific comorbid conditions or pain subtypes (nociceptive, neuropathic) with little consideration for the dynamic (temporal) nature of pain. Current pain subtypes are clinically linked to recommended pain treatments and provide insight into underlying mechanisms. However, current pain subtypes are limited by their focus on pain origin, do not include severity or duration of the pain experience, and do not illustrate how the course or trajectory of pain changes over time. Understanding the trajectory of pain experience can provide opportunities to alter the course of pain experience, improve residents’ quality of life and prevent adverse outcomes. This dissertation provides the first evidence of four distinct pain trajectories among NH residents including persistent pain which was associated with several resident characteristics and clinically relevant diagnosis. Using residents’ characteristics associated with persistent pain, such as a history of fracture or contracture, may improve care planning based on early identification or risk stratification and can improve mitigation of persistent pain. To identify and characterize pain trajectories in NH residents, the following activities were completed (1) systematic review of the literature related to prevalence of pain and associated factors in NH residents, (2) cross-sectional analysis of secondary data to examine prevalence of pain, persistent pain, and factors associated with pain in NH residents, and (3) a longitudinal retrospective analysis of secondary data using group-based trajectory modeling to identify, quantify, and characterize NH pain trajectories. The findings from this study highlight the prevalence and complexity of pain in NH residents.

The development of high-energy-density batteries, advanced sensor technologies, and advanced control algorithms for multirotor electric vertical takeoff and landing (eVTOL) unmanned aerial vehicles (UAVs) has led to interest in using these vehicles for a variety of applications including surveillance, package delivery, and even human transportation. In each of these cases, the ideal vehicle is one that can maneuver in congested spaces, but is efficient for traveling long distances. The combination of wings and vectored thrust make winged eVTOLs the obvious choice. However, these aircraft experience a much wider range of flight conditions that makes them challenging to model and control. This thesis contributes an aerodynamic model and a planning and control method for small, 1-2 m wingspan, winged eVTOLs. We develop the aerodynamic model based on first-principles, lumped-element aerodynamics, extending the lift and drag models to consider high-angle-of-attack flight conditions using models proposed in the literature. We present two methods for generating spline trajectories, one that uses the singular value decomposition to find a minimum-derivative polynomial spline, and one that uses B-splines to produce trajectories in the convex hull of a set of waypoints. We compare the quality of trajectories produced by both methods. Current control methods for winged eVTOL UAVs consider the vehicle primarily as a fixed-wing aircraft with the addition of vertical thrust used only during takeoff and landing. These methods provide good long-range flight handling but fail to consider the full dynamics of the vehicle for tracking complex trajectories. We present a trajectory tracking controller for the full dynamics of a winged eVTOL UAV in hover, fixed-wing, and partially transitioned flight scenarios. We show that in low- to moderate-speed flight, trajectory tracking can be achieved using a variety of pitch angles. In these conditions, the pitch of the vehicle is a free variable that we use to minimize the necessary thrust, and therefore energy consumption, of the vehicle. We use a geometric attitude controller and an airspeed-dependent control allocation scheme to operate the vehicle at a wide range of airspeeds, flight path angles, and angles of attack. We provide theoretical guarantees for the stability of the proposed control scheme assuming a standard aerodynamic model, and we present simulation results showing an average tracking error of 20 cm, an average computation rate of 800 Hz, and an 85% reduction in tracking error versus using a multirotor controller for low-speed flight.

Teacher attrition is problematic in schools serving marginalized populations. Teacher attrition rates are 50% higher in Title I schools than in non-Title I schools (Carver-Thomas & Darling-Hammond, 2017). Furthermore, attrition among teachers whose main teaching assignments are in mathematics and science is 70% higher in Title I schools as compared to mathematics and science teachers in non-Title I schools. This study investigates mathematics and science teachers who enter the profession through Teach for America (TFA), which places thousands of teachers in high-needs schools for a two-year commitment. While existing research on TFA literature has investigated the final decisions made by TFA teachers regarding their post-commitment plans, the process by which the teachers develop their post-commitment plans throughout their first two years in teaching, and the extent to which their plans change throughout these years is unknown, as is the role of TFA teachers’ support networks in this decision-making process. Focusing on the Massachusetts cohort that began teaching in the 2018–2019 school year, this study explores how ten TFA teachers developed, maintained, and used support networks using three semi-structured interviews with the teachers during their second year as teachers. I also develop a model for the teachers’ decision-making processes regarding their intended plans and the role of the teachers’ support networks in these processes. Finally, implications of this deepened understanding of the teachers’ decision-making process on teacher education programs, TFA, the schools/districts where the teachers are placed, and on future research on teacher retention are explored.

In safety-critical environments, it is crucial to have a backup strategy the system can turn to when facing a potentially unsafe situation. Run-time assurance provides a reliable methodology as a backup strategy. This work introduces a new framework for Run-time assurance, by generating trajectories in real-time using an optimal trajectory generation algorithm, then tracking the trajectory using transverse dynamics to design a feedback control law tailored for each trajectory generated. The generated trajectories are treated as safety backup trajectories that are only executed and followed by the plant if deemed necessary by the Run-time assurance logic. By using the Run-time assurance mechanism the system’s safety is ensured regardless of the behavior of the primary controller for the system with some constraints on the system. The framework assumes full knowledge of the environment and the system dynamics, while treating the trajectory generation part as a black box.