<p dir="ltr">Introduces a new framework and state-of-the-art algorithm in closed-loop prediction for motion planning under differential constraints. More specifically, this work introduces the idea of sampling on specific "sampling regions" rather than the entire workspace to speed-up the motion planning process by orders of magnitude.</p>

A Flexible Manufacturing System (FMS) is an integrated system consisting of several automated work centers interconnected by an automated material handling system. An integrated scheduling methodology is required to schedule all FMS sub-systems. The overall objective of this research was to develop a scheduling methodology to integrate job sequencing and Automatic Guided Vehicle System dispatching within a FMS environment. To develop the new scheduling methodology, the currently used AGVS controller decision set was examined and expanded. The expanded decision set gives the AGVS controller more options to choose from when scheduling the AGVS. The developed integrated scheduling methodology contains four steps. The first step determines which job is to be processed next by each work center based on job sequencing heuristics. The second step determines which work center is to be serviced next by the AGVS based on the estimated time till the work center is forced to stop production. The third step determines which specific job is to be serviced next by the AGVS by combining the work centers' processing orders and the work center servicing priorities. The fourth step decides which AGV is to transport the job requiring immediate service. Based on the preliminary study of a fictitious FMS, the new scheduling methodology showed a statistically significant increase in total job throughput, and a significant decrease in average flow time. Work center utilization also increased. A slight increase in unloaded AGV travel time was found, but was outweighed by the other benefits. / Master of Science

The purpose of this research was to develop and validate a simulation framework for automated guided vehicle systems (AGVSs). The framework that was developed, AGVSF, uses the discrete, next-event simulation method and the C programming language. AGVSF consists of an organizational structure that provides for control of the execution of the simulation and a set of modular C functions used to model the AGVS. The structure of AGVSF allows the user to organize the Simulation logic in a consistent manner. The modularity and flexibility of the code result from clearly defining the interdependencies of the functions that make up the various events and operations of the simulation. This enables the user to substitute functions where needed to represent new operational methods which are not directly provided in the original framework code set. A set of functions is provided within AGVSF for modeling basic AGVSs and AGVS layouts. The framework concept has been validated by simulating an AGVS under different operating conditions and control algorithms. / Master of Science

Entre as etapas do desenvolvimento de automóveis pode-se apontar a definição das características de suas suspensões. A fase de definição da suspensão pode ser dividida dentro do seguinte cenário: a escolha de um determinado tipo de suspensão, os pontos (geometria) e quais os valores de rigidez/amortecimento para todo o sistema irá resultar em um comportamento dinâmico desejado para o veículo, bem como a viabilidade de produção. Além disso, o entendimento da interação entre os parâmetros de suspensão, é crucial para a otimização do desempenho. Este trabalho pretende propor um método para aperfeiçoar a fase de \"tuning\" da suspensão, com foco principal no conforto. O veículo considerado é um caminhão comercial, e entre os seus parâmetros considerados estão rigidezes de molas da cabine e suspensão, amortecimento da suspensão de cabine e curvas do amortecedor da suspensão primária. O modelo virtual do veículo foi desenvolvido em ambiente ADAMS, o qual, previamente à otimização, foi validado contra dados experimentais. Métricas foram especialmente desenvolvidas levando em consideração aspectos subjetivos de conforto veicular, para dessa forma eliminar a variabilidade entre as avaliações subjetivas e análises das simulações. Os resultados mostraram expressivas melhorias no conforto e através de dados experimentais essas melhorias foram confirmadas. / Among the development phases of an automotive vehicle one can point out the definition of the characteristics of its suspensions. Suspension definition phase can be understood as the following scenario: given a suspension type, which hard points (geometric) and what values of stiffness/damping for the whole system will result in a desired dynamic behavior for the vehicle as well as production feasibility. Moreover, understanding the iteration among the suspension parameters, even considering just the tuning ones, is crucial for performance optimization. This work intends to propose a method for vehicle tuning characteristics optimization, having as a target the ride comfort. The vehicle considered here is a commercial truck, and among its parameters one considers cabin and suspension springs, cabin dampers and suspension damper curves. A vehicle model was developed in ADAMS environment and prior to the optimization the vehicle was validated against experimental data. Metrics were specially developed to take into account subjective aspects of ride, and, in this way, eliminating the gap between subjective evaluations and simulations analysis. Results showed improvements in ride comfort. The resulting setup was measured and the improvements were confirmed with experimental data.

Doctor of Philosophy / Genetics Interdepartmental Program / Jesse A. Poland / A central question in modern biology is to understand the genotype-to-phenotype (G2P) link, that is, how the genetics of an organism results in specific characteristics. However, prediction of phenotypes from genotypes is a difficult problem due to the complex nature of genomes, the environment, and their interactions. While the recent advancements in genome sequencing technologies have provided almost unlimited access to high-density genetic markers, large-scale rapid and accurate phenotyping of complex plant traits remains a major bottleneck. Here, we demonstrate field-based complex trait assessment approaches using a commercially available light-weight Unmanned Aerial Systems (UAS). By deploying novel data acquisition and processing pipelines, we quantified lodging, ground cover, and crop growth rate of 1745 advanced spring wheat lines at multiple time-points over the course of three field seasons at three field sites in South Asia. High correlations of digital measures to visual estimates and superior broad-sense heritability demonstrate these approaches are amenable for reproducible assessment of complex plant traits in large breeding nurseries. Using these validated high-throughput measurements, we applied genome-wide association and prediction models to assess the underlying genetic architecture and genetic control. Our results suggest a diffuse genetic architecture for lodging and ground cover in wheat, but heritable genetic variation for prediction and selection in breeding programs. The logistic regression-derived parameters of dynamic plant height exhibited strong physiological linkages with several developmental and agronomic traits, suggesting the potential targets of selection and the associated tradeoffs. Taken together, our highly reproducible approaches provide a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to understand the G2P and increase the rate of gain for complex traits in crop breeding.

<p>As modeling and simulation becomes a more important part of the modeling process, the demand on a known accuracy of the results of a simulation has grown more important. Sensitivity analysis (SA) is the study of how the variation in the output of a model can be apportioned to different sources of variation. By performing SA on a system, it can be determined which input/inputs influence a certain output the most. The sensitivity measures examined in this thesis are the Effective Influence Matrix, EIM, and the Main Sensitivity Index, MSI.</p><p>To examine the sensitivity measures, two tests have been made. One on a laboratory equipment including a hydraulic servo, and one on the conceptual landing gear model of the Gripen aircraft. The purpose of the landing gear experiment is to examine the influence of different frictions on the unfolding of the landing gear during emergency unfolding. It is also a way to test the sensitivity analysis method on an industrial example and to evaluate the EIM and MSI methods.</p><p>The EIM and MSI have the advantage that no test data is necessary, which means the robustness of a model can be examined early in the modeling process. They are also implementable in the different stages of the modeling and simulation process. With the SA methods in this thesis, documentation can be produced at all stages of the modeling process. To be able to draw correct conclusions, it is essential that the information that is entered into the analysis at the beginning is well chosen, so some knowledge is required of the model developer in order to be able to define reasonable values to use.</p><p>Wishes from the model developers/users include: the method and model quality measure should be easy to understand, easy to use and the results should be easy to understand. The time spent on executing the analysis has also to be well spent, both in the time preparing the analysis and in analyzing the results.</p><p>The sensitivity analysis examined in this thesis display a good compromise between usefulness and computational cost. It does not demand knowledge in programming, nor does it demand any deeper understanding of statistics, making it available to both the model creators, model users and simulation result users.</p>

As modeling and simulation becomes a more important part of the modeling process, the demand on a known accuracy of the results of a simulation has grown more important. Sensitivity analysis (SA) is the study of how the variation in the output of a model can be apportioned to different sources of variation. By performing SA on a system, it can be determined which input/inputs influence a certain output the most. The sensitivity measures examined in this thesis are the Effective Influence Matrix, EIM, and the Main Sensitivity Index, MSI. To examine the sensitivity measures, two tests have been made. One on a laboratory equipment including a hydraulic servo, and one on the conceptual landing gear model of the Gripen aircraft. The purpose of the landing gear experiment is to examine the influence of different frictions on the unfolding of the landing gear during emergency unfolding. It is also a way to test the sensitivity analysis method on an industrial example and to evaluate the EIM and MSI methods. The EIM and MSI have the advantage that no test data is necessary, which means the robustness of a model can be examined early in the modeling process. They are also implementable in the different stages of the modeling and simulation process. With the SA methods in this thesis, documentation can be produced at all stages of the modeling process. To be able to draw correct conclusions, it is essential that the information that is entered into the analysis at the beginning is well chosen, so some knowledge is required of the model developer in order to be able to define reasonable values to use. Wishes from the model developers/users include: the method and model quality measure should be easy to understand, easy to use and the results should be easy to understand. The time spent on executing the analysis has also to be well spent, both in the time preparing the analysis and in analyzing the results. The sensitivity analysis examined in this thesis display a good compromise between usefulness and computational cost. It does not demand knowledge in programming, nor does it demand any deeper understanding of statistics, making it available to both the model creators, model users and simulation result users.

Entre as etapas do desenvolvimento de automóveis pode-se apontar a definição das características de suas suspensões. A fase de definição da suspensão pode ser dividida dentro do seguinte cenário: a escolha de um determinado tipo de suspensão, os pontos (geometria) e quais os valores de rigidez/amortecimento para todo o sistema irá resultar em um comportamento dinâmico desejado para o veículo, bem como a viabilidade de produção. Além disso, o entendimento da interação entre os parâmetros de suspensão, é crucial para a otimização do desempenho. Este trabalho pretende propor um método para aperfeiçoar a fase de \"tuning\" da suspensão, com foco principal no conforto. O veículo considerado é um caminhão comercial, e entre os seus parâmetros considerados estão rigidezes de molas da cabine e suspensão, amortecimento da suspensão de cabine e curvas do amortecedor da suspensão primária. O modelo virtual do veículo foi desenvolvido em ambiente ADAMS, o qual, previamente à otimização, foi validado contra dados experimentais. Métricas foram especialmente desenvolvidas levando em consideração aspectos subjetivos de conforto veicular, para dessa forma eliminar a variabilidade entre as avaliações subjetivas e análises das simulações. Os resultados mostraram expressivas melhorias no conforto e através de dados experimentais essas melhorias foram confirmadas. / Among the development phases of an automotive vehicle one can point out the definition of the characteristics of its suspensions. Suspension definition phase can be understood as the following scenario: given a suspension type, which hard points (geometric) and what values of stiffness/damping for the whole system will result in a desired dynamic behavior for the vehicle as well as production feasibility. Moreover, understanding the iteration among the suspension parameters, even considering just the tuning ones, is crucial for performance optimization. This work intends to propose a method for vehicle tuning characteristics optimization, having as a target the ride comfort. The vehicle considered here is a commercial truck, and among its parameters one considers cabin and suspension springs, cabin dampers and suspension damper curves. A vehicle model was developed in ADAMS environment and prior to the optimization the vehicle was validated against experimental data. Metrics were specially developed to take into account subjective aspects of ride, and, in this way, eliminating the gap between subjective evaluations and simulations analysis. Results showed improvements in ride comfort. The resulting setup was measured and the improvements were confirmed with experimental data.

Using a new implementation of a NASA flight simulation of the Quiet Short-Haul Research Aircraft, autopilots were designed to be capable of flying both straight in (ILS) approaches, and circling (SNI) approaches. A standard glideslope coupler was sufficient for most conditions, but a standard Proportional-Integral-Derivative (PID) based localizer tracker was not sufficient for maintaining a lateral track on the SNI course. To track the SNI course, a feed-forward system, using GPS steering provided much better results. NASA and the FAA embrace the concept of a Simultaneous, Non-Interfering (SNI) approach as a way to increase airport throughput while reducing the noise footprints of aircraft on approach. The NASA concept for the SNI approach for Short Takeoff and Landing (STOL) aircraft involves a straight in segment flown above the flight path of a normal approach, followed by a spiraling descent to the runway. As this is a procedure that would be utilized by regional airliners, it is assumed that it would be conducted under Instrument Flight Rules (IFR). GPS or INS guidance would be required to fly this approach, and it is likely that it would be necessary to fly the approach with a coupled autopilot: a stabilized, curving, instrument approach to decision altitude would be exceedingly difficult to fly. The autopilots in many current commuter and general aviation aircraft, however, were designed before the event of GPS, and do not have provisions for tracking curved paths. This study identifies problem areas in implementing the SNI circling approach on aircraft and avionics as they stand today and also gives examples of what can be done for the SNI approach to be successful.

With unmanned aerial vehicles (UAVs), also known as drones, becoming readily available and affordable, applications for these devices have grown immensely. One type of application is the use of drones to fly over large areas and detect desired entities. For example, a swarm of drones could detect marine creatures near the surface of the ocean and provide users the location and type of animal found. However, even with the reduction in cost of drone technology, such applications result costly due to the use of custom hardware with built-in advanced capabilities. Therefore, the focus of this thesis is to compile an easily customizable, low-cost drone design with the necessary hardware for autonomous behavior, swarm coordination, and on-board object detection capabilities. Additionally, this thesis outlines the necessary network architecture to handle the interconnection and bandwidth requirements of the drone swarm. The drone on-board system uses a PixHawk 4 flight controller to handle flight mechanics, a Raspberry Pi 4 as a companion computer for general-purpose computing power, and a NVIDIA Jetson Nano Developer Kit to perform object detection in real-time. The implemented network follows the 802.11s standard for multi-hop communications with the HWMP routing protocol. This topology allows drones to forward packets through the network, significantly extending the flight range of the swarm. Our experiments show that the selected hardware and implemented network can provide direct point-to-point communications at a range of up to 1000 feet, with extended range possible through message forwarding. The network also provides sufficient bandwidth for bandwidth intensive data such as live video streams. With an expected flight time of about 17 minutes, the proposed design offers a low-cost drone swarm solution for mid-range aerial surveillance applications.