Inertial navigation is a relative navigation technique commonly used by autonomous vehicles to determine their linear velocity, position and orientation in three-dimensional space. The basic premise of inertial navigation is that measurements of acceleration and angular velocity from an inertial measurement unit (IMU) are integrated over time to produce estimates of linear velocity, position and orientation. However, this process is a particularly involved one. The raw inertial data must first be properly analyzed and modeled in order to ensure that any inertial navigation system (INS) that uses the inertial data will produce accurate results. This thesis describes the process of analyzing and modeling raw IMU data, as well as how to use the results of that analysis to design an INS. Two separate INS units are designed using two different micro-electro-mechanical system (MEMS) IMUs. To test the effectiveness of each INS, each IMU is rigidly mounted to an unmanned ground vehicle (UGV) and the vehicle is driven through a known test course. The linear velocity, position and orientation estimates produced by each INS are then compared to the true linear velocity, position and orientation of the UGV over time. Final results from these experiments include quantifications of how well each INS was able to estimate the true linear velocity, position and orientation of the UGV in several different navigation scenarios as well as a direct comparison of the performances of the two separate INS units.

Wearable sensors are at the heart of the digital health revolution. Integral to the use of these sensors for monitoring conditions impacting balance and mobility are accurate estimates of joint angles. To this end a simple and novel method of estimating hip joint angles from small wearable magnetic and inertial sensors is proposed and its performance is established relative to optical motion capture in a sample of human subjects. Improving upon previous work, this approach does not require precise sensor placement or specific calibration motions, thereby easing deployment outside of the research laboratory. Specific innovations include the determination of sensor to segment rotations based on functionally determined joint centers, and the development of a novel filtering algorithm for estimating the relative orientation of adjacent body segments. Hip joint angles and range of motion determined from the proposed approach and an existing method are compared to those from an optical motion capture system during walking at a variety of speeds and tasks designed to exercise the hip through its full range of motion. Results show that the proposed approach estimates flexion/extension angle more accurately (RMSE from 7.08 to 7.29 deg) than the existing method (RMSE from 11.64 deg to 14.33 deg), with similar performance for the other anatomical axes. Agreement of each method with optical motion capture is further characterized by considering correlation and regression analyses. Mean ranges of motion for the proposed method are not largely different from those reported by motion capture, and showed similar values to the existing method. Results indicate that this algorithm provides a promising approach for estimating hip joint angles using wearable inertial sensors, and would allow for use outside of constrained research laboratories.

It was demonstrated in recent studies that Complementary Limb Motion Estimation (CLME) is robust approach for controlling active knee prostheses. A wearable sensor system is then needed to provide inputs to the controller in a real-time platform. In the present work, a wearable sensor system based on magnetic and inertial measurement units (MIMU) together with a simple calibration procedure were proposed. This sensor system was intended to substitute and extend the capabilities of a previous device based on potentiometers and gyroscopes. The proposed sensor system and calibration were validated with an Optical Tracking System (OTS) in a standard gait lab and first results showed that the proposed solution had a performance comparable to similar studies in the literature.

<p>Saab Bofors Dynamics har som huvudsaklig verksamhet att utveckla missiler. Ofta krävs det att någon form av navigationsfunktion byggs in, och eftersom det inte är lätt att testa nya lösningar och algoritmer direkt på missilerna har man tagit fram två autonoma testplattformar, Freke och Gorm, i form av självgående bilar som kan ta sig fram inomhus i t.ex. en korridor.</p><p>För navigation utomhus lämpar sig GPS väl i många situationer, men i autonoma sammanhang är det inte tillräckligt. GPS är inte alltid tillgänglig på grund av att man inte alltid har fri sikt till minst fyra satelliter och i militära sammanhang är den också lätt att störa ut. Därför förlitar man sig hellre på noggranna tröghetsnavigeringssystem (TNS) som kan ge data i 100-tals Hz, och kompletterar med GPS för att korrigera för avvikelser. Oftast integrerar man sensorerna med någon form av Kalmanfilter för att kunna skatta felparametrar. Om GPS:en faller ifrån kan man med hjälp av felparametrarna navigera noggrannare med ett TNS än man skulle ha gjort annars</p><p>Noggranna TNS är dock både relativt stora och dyra. Referenssystem kan kosta uppåt en miljon kronor, och lämpar sig alltså varken för engångsbruk (i missiler) eller små billiga autonoma bilar. Nu finns det små och relativt billiga tröghetssensorer (IMU, Inertial Measurement Unit) som storleksmässigt lämpar sig väldigt väl, men som dock har ganska låg noggrannhet.</p><p>I detta examensarbete görs en studie av två olika navigationsmodeller där en billig IMU och en GPS integreras med hjälp av ett Extended Kalmanfilter (EKF).</p>

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (leaves 317-339).

This research focuses primary on recognition of lane-change behaviors using support vector machines (SVMs). Previous research and statistical results show that the vast majority of motor vehicle accidents are caused by driver behavior and errors. Therefore, the interpretation and evaluation of driver behavior is important for road safety analysis and improvement. The main limit to understanding driver behavior is the data availability. In particular, a full-scale lane-change data set is difficult to collect in a real traffic environment because of the safety and cost issues. Considering the data demands of the recognition model development and the obstacles of field data collection, data were collected from two aspects: simulation data and the field data. To obtain field data, an in-vehicle data recorder (IVDR) that integrates a Global Positioning System (GPS) and Inertial Measurement Unit (IMU) are developed to collect data on speed, position, attitude, acceleration, etc. To obtain simulation data, a lane-change simulation with a speed controller and a trajectory tracking controller with preview ability were developed, and sufficient lane-change data were generated. Proportional-Integral-Derivative (PID) control is applied to the speed controller and trajectory tracking controller. Simulation data were divided into two classes: dual lane-change data and single lane-change data; field data were further divided as single lane-change and non-lane-change data. Two-class and three-class classification SVM model are trained by simulation data and field data, and the model parameters were optimized by Genetic Algorithm (GA). A radial basis function and polynomial kernel functions were found that suitable for this recognition task. The recognition results indicate that, the SVM model trained by simulation data and non-lane-change data can correctly classify up to 85 percent of single lane-change field data.

A Wearable Motion Analysis System (WMAS) was developed to evaluate gait, particularly parameters that are indicative of mild traumatic brain injury. The WMAS consisted on six Opal IMUs attached on the sternum, waist, left and right thigh and left and right shank. Algorithms were developed to calculate the knee flexion angle, stride length and cadence parameters during slow, normal and fast gait speeds. The WMAS was validated for repeatability using a robotic arm and accuracy using the Vicon motion capture system, the gold standard for gait analysis. The WMAS calculated the gait parameters to within a clinically acceptable range and is a powerful tool for gait analysis and potential concussion diagnosis outside of a laboratory setting.

Fossil fuels and their byproducts are a vital part of our economy, and society. Until renewable energy sources and energy storage technologies advance to the point where they are reliable and inexpensive, the US Economy will continue to depend upon fossil fuels. Current resources are being consumed, and the "easy to reach" reserves are becoming depleted. This leads to the requirement for more exploratory drilling, and the potential for more disasters like the recent Deepwater Horizon spill in the Gulf of Mexico. Drilling is the first of several steps in the creation of a productive oil or natural gas well. Completing a well involves casing the walls in concrete to prevent damage to the surrounding rock formations and to ensure that all of the oil or gas is captured without escaping to the surrounding environment. Ensuring the piping, which is used to case wells, is assembled correctly and to manufacturer's specifications is the focus of this study. Individual pipe sections are screwed together with a requirement for torque and number of turns. Each joint must be verified to ensure integrity, and minimize the possibility of a spill or leak. The torque measurement can be accomplished by a "torque sub", a sensor installed in-line with the drill string. The torque sub is a wireless sensor that transmits torque data to the control system for logging and display. This thesis defines the parameters required to integrate a "number of turns" measurement into an existing torque sub so that both parameters can be captured, recorded and reported using a single device. The Yost Engineering 3-Space Sensor was evaluated for use in this application. The configuration that gave the most accurate data was selected, along with the determination of some correction factors to account for site specific variation in the signals. A calibration algorithm is discussed, along with several unique methods for ensuring that the sensor output doesn't drift over the course of the joint make-up process. / text

Saab Bofors Dynamics har som huvudsaklig verksamhet att utveckla missiler. Ofta krävs det att någon form av navigationsfunktion byggs in, och eftersom det inte är lätt att testa nya lösningar och algoritmer direkt på missilerna har man tagit fram två autonoma testplattformar, Freke och Gorm, i form av självgående bilar som kan ta sig fram inomhus i t.ex. en korridor. För navigation utomhus lämpar sig GPS väl i många situationer, men i autonoma sammanhang är det inte tillräckligt. GPS är inte alltid tillgänglig på grund av att man inte alltid har fri sikt till minst fyra satelliter och i militära sammanhang är den också lätt att störa ut. Därför förlitar man sig hellre på noggranna tröghetsnavigeringssystem (TNS) som kan ge data i 100-tals Hz, och kompletterar med GPS för att korrigera för avvikelser. Oftast integrerar man sensorerna med någon form av Kalmanfilter för att kunna skatta felparametrar. Om GPS:en faller ifrån kan man med hjälp av felparametrarna navigera noggrannare med ett TNS än man skulle ha gjort annars Noggranna TNS är dock både relativt stora och dyra. Referenssystem kan kosta uppåt en miljon kronor, och lämpar sig alltså varken för engångsbruk (i missiler) eller små billiga autonoma bilar. Nu finns det små och relativt billiga tröghetssensorer (IMU, Inertial Measurement Unit) som storleksmässigt lämpar sig väldigt väl, men som dock har ganska låg noggrannhet. I detta examensarbete görs en studie av två olika navigationsmodeller där en billig IMU och en GPS integreras med hjälp av ett Extended Kalmanfilter (EKF).

Land vehicle navigation is primarily dependent upon the Global Positioning System (GPS) which provides accurate navigation in open sky. However, in urban and rural canyons GPS accuracy degrades considerably. To help GPS in such scenarios, it is often integrated with inexpensive inertial sensors. Such sensors have complex stochastic errors which are difficult to mitigate. In the presence of speed measurements from land vehicle, a reduced number of inertial sensors can be used which improve performance and termed as the Reduced Inertial Sensor System (RISS). Existing low-cost RISS/GPS integrated algorithms have limited accuracy due to use of approximations in error models and employment of a Linearized Kalman Filter (LKF). This research developed an enhanced error model for RISS which was integrated with GPS using an Extended Kalman Filter (EKF) for improved navigation of land vehicles. The proposed system was tested on several road experiments and the results confirmed the sustainable performance of the system during long GPS outages. To further increase the accuracy, Differential GPS (DGPS) is employed where carrier phase measurements are typically used. This requires resolution of Integer Ambiguity (IA) that comes at computational and time expense. This research uses pseudorange measurements for DGPS which mitigate large biases due to atmospheric errors and obviate the resolution of IA. These measurements are integrated with the enhanced RISS to filter increased noise and help GPS during signal blockages. The performance of the proposed system was compared with two other algorithms employing undifferenced GPS measurements where atmospheric effects are mitigated using either the Klobuchar model or dual frequency receivers. The proposed system performed better than both the algorithms in positional accuracy, multipath and GPS outages. This research further explored the reduction of Time-to-Fix Ambiguities (TTFA) for land vehicle navigation. To reduce the TTFA through inertial aiding, previous research used high-end Inertial Measurement Units (IMUs). This research uses MEMS grade IMU by integrating the enhanced RISS with carrier phase measurements using EKF. This algorithm was also tested on three road trajectories and it was shown that this integration helps reduce the TTFA as compared to the GPS-only case when fewer satellites are visible. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2014-06-23 11:30:58.036