Finite Element Model Validation and Testing of an Off-Road Vehicle under Dynamic Loading Conditions

In the vehicle design life cycle, validation tests consume a significant portion of the available development time. With a short, one-year design cycle, the Embry-Riddle Baja Team leverages computer-aided engineering (CAE) tools to simulate critical test cases whose loading conditions can be accurately represented by a series of static loads and constraints. Using these conditions, a Finite Element Model (FEM) can be employed to accurately predict the effects of the loading conditions in the components. During the initial design of the front suspension, one major load case was determined to be the main failure load. However, after validation testing the suspension exhibited a failure indicative of a load path not predicted. To obtain a more complete understanding of the dynamic loading conditions on the affected component, instrumentation was implemented to measure strain in the critical member. A dynamic vehicle test case was performed to measure a high-frequency, high-load case representative of an event during the vehicle service life. The measurement was then utilized to validate a Finite Element Model, in turn used to re-design the member. This component withstands the loading condition for infinite fatigue life without increasing the overall weight of the design, although the failure was not reproduced during testing

Cost-Effective GNSS Hardware for High-Accuracy Surveys and Its Prospects for Post-Processed Kinematic (PPK) and Precise Point Positioning (PPP) Strategies

This dissertation determines for the first time the vertical accuracy achievable with low-cost mass-market multi-frequency, multi-GNSS (LM3GNSS) receivers, and antennas in the context of Ellipsoid Reference Survey (ERS), usually employed in bathymetric operations aboard survey platforms. LM3GNSS receivers are relatively new in the market, and their emergence is driven by the automobile industry and several mass-market applications requiring location-based solutions at high accuracies. It is foreseeable that emerging hydrographic survey platforms such as autonomous surface vehicles, small unmanned aircraft, crowd-sourced bathymetric platforms, and offshore GNSS buoy will find LM3GNSS receivers attractive since they are power- and cost-effective (often less than $1,000 per unit). Previous studies have shown that some mass-market GNSS receivers\u27 positioning accuracy is at the sub-meter level in some positioning strategies, but the authors rarely discussed the vertical accuracy. In rare cases where attention is given to the vertical component, the experiment design did not address the dynamic antenna scenario typical of hydrographic survey operations and the positioning performance that meets the hydrographic survey community\u27s aspirations. The LM3GNSS receivers and low-cost antennas considered in this dissertation achieved vertical accuracies within 0.15 m at a 95% confidence level in simulated precise point positioning (PPP) and post-processed kinematic positioning strategies. This dissertation characterizes the signal strength, multipath, carrier-phase residuals, and code residuals in the measurement quality assessment of four LM3GNSS receivers and four low-cost antennas. The dissertation investigates the performances of the LM3GNSS receivers and low-cost antennas in different antenna-receiver pairings, relative to a high-grade GNSS receiver and antenna in simulated-kinematic and precise point positioning (PPP) strategies. This dissertation also shows that solutions with an uncalibrated antenna improve with a cloned ANTEX file making the results comparable to those achieved with high-end GNSS antenna. This dissertation also describes a GNSS processing tool (with graphic user interface), developed from scratch by the author, that implements, among others, orbit interpolation and geodetic computations as steps towards multipath computation and analysis. The dissertation concludes as follows: (1) The LM3GNSS hardware considered in this dissertation provides effective alternative positioning and navigation performance for emerging survey platforms such as ASV and sUAS. (2) LM3GNSS hardware can meet vertical positioning accuracy on the order of 0.15 m at a 95% confidence level in PPP strategy on less dynamic platforms. (3) LM3GNSS receivers can provide PPK solutions at medium (30 – 40 km) baselines with a vertical positioning accuracy better than 0.15m at a 95% confidence level. (4) LM3GNSS receivers in PPP strategy should meet IHO S-44 order-1 and order-2 in shallow waters. (5) Zephyr3 antenna, being a high-end GNSS antenna, may not always offer the best performance with the LM3GNSS receiver, especially in a dynamic environment. (6) Given the current tracking capabilities, the measurement quality, and positioning performances of LM3GNSS receivers relative to the geodetic grade receiver, it is foreseeable that the distinction between high-end GNSS and LM3GNSS receivers will most likely fade away as GNSS hardware technology advances. (7) Maximizing an LM3GNSS receiver in PPK strategy requires a multi-constellation-enabled reference station and high (i.e., 1 Hz) data tracking rate; otherwise, the PPK solutions will likely drift up to 20 cm

Visual Servoing

The goal of this book is to introduce the visional application by excellent researchers in the world currently and offer the knowledge that can also be applied to another field widely. This book collects the main studies about machine vision currently in the world, and has a powerful persuasion in the applications employed in the machine vision. The contents, which demonstrate that the machine vision theory, are realized in different field. For the beginner, it is easy to understand the development in the vision servoing. For engineer, professor and researcher, they can study and learn the chapters, and then employ another application method

Traffic Danger Recognition With Surveillance Cameras Without Training Data

We propose a traffic danger recognition model that works with arbitrary traffic surveillance cameras to identify and predict car crashes. There are too many cameras to monitor manually. Therefore, we developed a model to predict and identify car crashes from surveillance cameras based on a 3D reconstruction of the road plane and prediction of trajectories. For normal traffic, it supports real-time proactive safety checks of speeds and distances between vehicles to provide insights about possible high-risk areas. We achieve good prediction and recognition of car crashes without using any labeled training data of crashes. Experiments on the BrnoCompSpeed dataset show that our model can accurately monitor the road, with mean errors of 1.80% for distance measurement, 2.77 km/h for speed measurement, 0.24 m for car position prediction, and 2.53 km/h for speed prediction.Comment: To be published in proceedings of Advanced Video and Signal-based Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 378-383, IEE