Harness racing horses are exposed to high workload and consequently, they are at risk of joint injuries and lameness. In recent years, the interest in applications to improve animal welfare has increased and there is a demand for objective assessment methods that can enable early and robust diagnosis of injuries. In this thesis, experiments were conducted on video recordings collected by a helmet camera mounted on the driver of a sulky. The aim was to take the first steps toward equine gait analysis by investigating how semantic segmentation and 3D reconstruction of such data could be performed. Since these were the first experiments made on this data, no expectations of the results existed in advance. Manual pixel-wise annotations were created on a small set of extracted frames and a deep learning model for semantic segmentation was trained to localize the horse, as well as the sulky and reins. The results are promising and could probably be further improved by expanding the annotated dataset and using a larger image resolution. Structure-from-motion using COLMAP was performed to estimate the camera motion in part of a video recording. A method to filter out dynamic objects based on masks created from predicted segmentation maps was investigated and the results showed that the reconstruction was part-wise successful, but struggled when dynamic objects were not filtered out and when the equipage was moving at high speed along a straight stretch. Overall the results are promising, but further development needs to be conducted to ensure robustness and conclude whether data collected by the investigated helmet camera configuration is suitable for equine gait analysis.

Recent advancements in time-optimal trajectory planning, control, and state estimation for autonomous vehicles have paved the way for the emerging field of autonomous racing. In the last 5-10 years, this form of racing has become a popular and challenging testbed for autonomous driving algorithms, aiming to enhance the safety and performance of future intelligent vehicles. In autonomous racing, the main goal is to develop real-time algorithms capable of autonomously maneuvering a vehicle around a racetrack, even in the presence of moving opponents. However, as a vehicle approaches its handling limits, several challenges arise for online trajectory planning and control. The vehicle dynamics become nonlinear and hard to capture with low-complexity models, while fast re-planning and good generalization capabilities are crucial to execute optimal maneuvers in unforeseen scenarios. These challenges leave several open research questions, three of which will be addressed in this thesis. The first explores developing accurate yet computationally efficient vehicle models for online time-optimal trajectory planning. The second focuses on enhancing learning-based methods for trajectory planning, control, and state estimation, overcoming issues like poor generalization and the need for large amounts of training data. The third investigates the optimality of online-executed trajectories with simplified vehicle models, compared to offline solutions of minimum-lap-time optimal control problems using high-fidelity vehicle models. This thesis consists of four parts, each of which addresses one or more of the aforementioned research questions, in the fields of time-optimal vehicle trajectory planning, control and state estimation. The first part of the thesis presents a novel artificial race driver (ARD), which autonomously learns to drive a vehicle around an obstacle-free circuit, performing online time-optimal vehicle trajectory planning and control. The following research questions are addressed in this part: How optimal is the trajectory executed online by an artificial agent that drives a high-fidelity vehicle model, in comparison with a minimum-lap-time optimal control problem (MLT-OCP), based on the same vehicle model and solved offline? Can the artificial agent generalize to circuits and conditions not seen during training? ARD employs an original neural network with a physics-driven internal structure (PhS-NN) for steering control, and a novel kineto-dynamical vehicle model for time-optimal trajectory planning. A new learning scheme enables ARD to progressively learn the nonlinear dynamics of an unknown vehicle. When tested on a high-fidelity model of a high-performance car, ARD achieves very similar results as an MLT-OCP, based on the same vehicle model and solved offline. When tested on a 1:8 vehicle prototype, ARD achieves similar lap times as an offline optimization problem. Thanks to its physics-driven architecture, ARD generalizes well to unseen circuits and scenarios, and is robust to unmodeled changes in the vehicle’s mass. The second part of the thesis deals with online time-optimal trajectory planning for dynamic obstacle avoidance. The research questions addressed in this part are: Can time-optimal trajectory planning for dynamic obstacle avoidance be performed online and with low computational times? How optimal is the resulting trajectory? Can the planner generalize to unseen circuits and scenarios? At each planning step, the proposed approach builds a tree of time-optimal motion primitives, by performing a sampling-based exploration in a local mesh of waypoints. The novel planner is validated in challenging scenarios with multiple dynamic opponents, and is shown to be computationally efficient, to return near-time-optimal trajectories, and to generalize well to new circuits and scenarios. The third part of the thesis shows an application of time-optimal trajectory planning with optimal control and PhS-NNs in the context of autonomous parking. The research questions addressed in this part are: Can an autonomous parking framework perform fast online trajectory planning and tracking in real-life parking scenarios, such as parallel, reverse and angle parking spots, and unstructured environments? Can the framework generalize to unknown variations in the vehicle’s parameters and road adherence, and operate with measurement noise? The autonomous parking framework employs a novel penalty function for collision avoidance with optimal control, a new warm-start strategy and an original PhS-NN for steering control. The framework executes complex maneuvers in a wide range of parking scenarios, and is validated with a high-fidelity vehicle model. The framework is shown to be robust to variations in the vehicle’s mass and road adherence, and to operate with realistic measurement noise. The fourth and last part of the thesis develops novel kinematics-structured neural networks (KS-NNs) to estimate the vehicle’s lateral velocity, which is a key quantity for time-optimal trajectory planning and control. The KS-NNs are a special type of PhS-NNs: their internal structure is designed to incorporate the kinematic principles, which enhances the generalization capabilities and physical explainability. The research questions addressed in this part are: Can a neural network-based lateral velocity estimator generalize well when tested on a vehicle not used for training? Can the network’s parameters be physically explainable? The approach is validated using an open dataset with two race cars. In comparison with traditional and neural network estimators of the literature, the KS-NNs improve noise rejection, exhibit better generalization capacity, are more sample-efficient, and their structure is physically explainable.

Tato práce se se zabývá návrhem kinematiky zavěšení kol obou náprav. Na základě analýz jízdních dat, multi-body simulací v softwaru Adams Car, simulací v Matlabu a analytických kalkulací v Mathcadu, je navržena řada změn s cílem zlepšit jízdní vlastnosti vozu Formule student, tyto změny jsou následně implementovány do CAD modelu vozu. Jednotlivé změny kinematiky náprav jsou provedeny na základě analýzy konkrétního problému, který se snaží řešit. Jednou z problematik je zástavbová náročnost systému odpružení a zavěšení zadních kol, zde je cílem snížit hmotnost, výšku těžiště a moment setrvačnosti. Další problematikou je geometrie předního kola, kde je cílem zlepšit využití pneumatik a snížit síly v řízení. Dále se práce zabývá simulacemi elastokinematiky zadní nápravy, součástí je také návrh měřícího zařízení. V poslední části je zkoumán vliv provedených změn i elastokinematiky na jízdní dynamiku vozu v ustálených stavech za pomocí MM metody simulované s modelem celého vozu v Adams Car a zpracované v Matlabu.

Indiana University-Purdue University Indianapolis (IUPUI) / Pipe bombs are a common form of improvised explosive device, due in part to their ease of construction. Despite their simplistic nature, the lethality of pipe bombs should not be dismissed. Due to the risk of harm and their commonality, research into the pipe bomb deflagration process and subsequent chemical analysis is necessary. The laboratory examination of pipe bomb fragments begins with a visual examination. While this is presumptive in nature, hypotheses formed here can lead to subsequent confirmatory exams. The purpose of this study was to measure the mass and velocity of pipe bomb fragments using high speed video. These values were used to discern any trends in container type (PVC or black/galvanized steel), energetic filler (Pyrodex or double base smokeless powder), and ambient temperature (13°C and -8°C). The results show patterns based on container type, energetic filler, and temperature. The second stage of a laboratory exam is chemical analysis to identify any explosive that may be present. Legality calls for identification only, not quantitation. The purpose of this study is to quantitate the amount of explosive residue on post-blast pipe bomb fragments. By doing so, the instrumental sensitivities required for this type of analysis will be known. Additionally, a distribution of the residue will be mapped to provide insight into the deflagration process of a device. This project used a novel sampling technique called total vaporization solid phase microextraction. The method was optimized for nitroglycerin, the main energetic in double base smokeless powder. Detection limits are in the part per billion range. Results show that the concentration of residue is not uniform, and the highest concentration is located on the endcaps regardless of container type. Total vaporization solid phase microextraction was also applied to automotive racing samples of interest to the National Hot Rod Association. The purpose of this project is two-fold; safety of the race teams in the form of dragstrip adhesive consistency and monitoring in the form of fuel testing for illegal adulteration. A suite of analyses, including gas chromatography mass spectrometry, infrared spectroscopy, and evaporation rate, were developed for the testing of dragstrip adhesives. Gas chromatography mass spectrometry methods were developed for both nitromethane based fuel as well as racing gasolines. Analyses of fuel from post-race cars were able to detect evidence of adulteration. Not only was a novel technique developed and optimized, but it was successfully implemented in the analysis of two different analytes, explosive residue and racing gasoline. TV-SPME shows tremendous promise for the future in its ability to analyze a broad spectrum of analytes.

The role risk taking has played in American history has helped shape current legislation concerning gambling. This thesis attempts to explain the discrepancies in legislation regarding distinct forms of gambling. While casinos are heavily regulated by state and federal laws, most statutes dealing with lotteries strive to regulate the activities of other parties instead of those of the lottery institutions. Incidentally, lotteries are the only form of gambling completely managed by the government. It can be inferred that the United States government is more concerned with people exploiting gambling than with the actual practice of wagering. In an effort to more fully understand the gambling debate, whether it should be allowed or banned, I examined different types of sources. Historical sources demonstrate how ingrained in American culture risk taking, the core of gambling, has been since the formation of this nation. Sources dealing with the economic implications of gambling were also studied. Additionally, sources dealings with the political and legal aspects of gambling were essential for this thesis. Legislature has tried to reconcile distinct problems associated with gambling, including corruption. For this reason sports gambling scandals and Mafia connections to gambling have also been examined. The American government has created much needed legislature to address different concerns relating to gambling. It is apparent that statutes will continue to be passed to help regulate the gambling industry. A possible consideration is the legalization of sports wagering to better regulate that sector of the industry.