A Novel Dynamic Measurement System for Evaluating the Braking Coordination of Articulated Vehicles

The braking coordination between tractor and semitrailer is vital to the safety of articulated vehicles. Traditional evaluation about braking coordination is based on the pressure measurement along air braking pipeline, which needs to change original braking structure to install gauges and cannot directly reflect the final braking coordination of different wheels. To overcome these limitations, this paper proposes a novel dynamic measurement system for evaluating the braking coordination of articulated vehicles. During the brake test, all wheel velocities of the whole articulated vehicle are synchronously obtained through a specially designed distributed acquisition platform. To effectively eliminate gross errors and noises in wheel angular velocity data, a 3-order autoregressive (AR) model and an improved-thresholding wavelet filtering algorithm are developed. Further, a novel direct evaluation method about braking coordination is proposed according to the differences in angular velocity dropping time of all wheels. Finally, the overall system is assessed through real field tests. The results validate the feasibility and effectiveness of the proposed system

Impact of Indoor Location Information Reliability on Users’ Trust of an Indoor Positioning System

Indoor positioning systems have been used as a supplement to provide positioning in settings where GPS does not function. However, the accuracy of calculated results varies among techniques and algorithms used; system performance also differs across testing environments. As a result, users’ responses to and opinions of these positioning results could be different. Furthermore, user trust, most closely associated with their confidence in the system, will also vary. A relatively little studied topic is the effect of positioning variance on a user’s opinion or trust of such systems (GPS as well, for that matter). Therefore, understanding how user interaction with such systems (through trust) changes is important for achieving more usable positioning system design. An experiment was designed to examine if the sequence of location accuracy will affect users’ trust in an individual episode positioning result as well as the system overall. The simulated positioning system running on an iPad used for this experiment provides 10 priming positioning results at a specific category of accuracy. The accuracy is controlled and is presented as either 1. ACCURATE (within 5 meters of actual location), 2. INACCURATE (greater 15 meters), 0r 3. WRONG BUILDING (outside current building’s footprint). After one set of these priming locations a series of 55 post-priming locations across the same categories in addition to 10 CONTINUOUS locations (with between 6 and 15 meters of error) were presented. At each experimental site participants located themselves using the simulated system and rated their trust for that location. Variables obtained from the experiment include: 1. Two types of trust at each location (positioning trust and system trust); 2. Spatial abilities, sense of direction, and ancillary survey data (user characteristics). Results show that users’ trust varies among different accuracy categories and changes over time according to the system performance in association with their own characteristics. Specifically, the accuracy of the priming locations has an impact on users’ trust of later results. Besides, users’ trust in individual positioning results is quite variable and the variability is closely related to accuracy, while user trust of the overall system is less variable

Caracterización de una unidad de medida inercial para la obtención de entornos de simulación de un sistema de navegación inercial de bajo coste mediante Allan Variance

Alex Garcia Quinchia, assessor del Departament de Microelectrònica i Sistemes Electrònics de la Universitat Autònoma de Barcelon

Modeling of Inertial Sensors

Tato diplomová práce se zabývá měřením a modelováním MEMS inerciálních snímačů. V této práci jsou uvedeny základní principy inerciálních snímačů spolu s jejich nejčastěji se vyskytujícími chybami. Dále je proveden průzkum trhu, pomocí kterého byly vybrány snímače pro měření. Následují dvě kapitoly uvádějící metody testování a modelování inerciálních snímačů. Nejrozsáhlejší částí práce je prezentace naměřených výsledků, kdy byly provedeny statické měření Allanovy odchylky, zemské rotace, teplotní závislosti nuly snímačů a dynamické testy citlivosti gyroskopu v závislosti na teplotě. V poslední části práce je prezentován návrh chybového modelu snímače pomocí autokorelačních funkcí a Allanovy odchylky se zhodnocením dosaţených výsledků.This master thesis deals with measurement and modeling of MEMS inertial sensors. This paper describes basic principles of inertial sensors along with their most often errors. The next part shows results from inertial sensor market analysis, which enabling a selection of sensors to be measured. The following two chapters present methods for inertial sensor modeling and testing. The biggest part of text is dedicated to presentation of measurement results showing us static measurement of Allan variance, Earth rotation, temperature dependent bias and dynamic measurement of gyroscope sensitivity testing over temperature. In the last part of the thesis is presented a design of sensor error model by autocorrelation function and Allan variance and also an evaluation of achieved results.

Design and implementation of a control scheme for a MEMS rate integrating gyroscope

PhD ThesisMEMS gyroscopes are found across a large range of applications, from low precision low cost applications through to high budget projects that require almost perfect accuracy. MEMS gyroscopes fall into two categories – ‘rate’ and ‘rate integrating’, with the latter offering superior performance. The key advantage that the rate integrating type possesses is that it directly measures angle, eliminating the need for any integration step. This reduces the potential for errors, particularly at high rates. However, the manufacturing precision required is far tighter than that of the rate gyroscope, and this has thus far limited the development of rate integrating gyroscopes. This thesis proposes a method for reducing the effect of structural imperfections on the performance of a rate integrating gyroscope. By taking a conventional rate gyroscope and adjusting its control scheme to operate in rate integrating mode, the thesis shows that it is possible to artificially eliminate the effect of some structural imperfections on the accuracy of angular measurement through the combined use of electrostatic tuning and capacitive forcing. Further, it demonstrates that it is viable to base the designs for rate integrating gyroscopes on existing rate gyroscope architectures, albeit with some modifications. Initially, the control scheme is derived through the method of multiple scales and its potential efficacy demonstrated through computational modelling using Simulink. The control scheme is then implemented onto an existing rate gyroscope architecture, with a series of tests conducted that benchmark the gyroscope performance in comparison to standard performance measures. Experimental work demonstrates the angle measurement capability of the rate integrating control scheme, with the gyroscope shown to be able to measure angle, although not to the precision necessary for commercial implementation. However, the scheme is shown to be viable with some modifications to the gyroscope architecture, and initial tests on an alternative architecture based on these results are presented.United Technologies and System

UNDERSTANDING OF LOW REYNOLDS NUMBER AERODYNAMICS AND DESIGN OF MICRO ROTARY-WING AIR VEHICLES

The goal of the present research is to understand aerodynamics at low Reynolds numbers and synthesize rules towards the development of hovering micro rotary-wing air vehicles (MRAVs). This entailed the rigorous study of airfoil characteristics at low Reynolds numbers through available experimental results as well as the use of an unsteady Reynolds-Averaged Navier-Stokes solver. A systematic, experimental, variation of parameters approach with physical rotors was carried out to design and develop a micro air vehicle-scale rotor which maximizes the hover Figure of Merit. The insights gained in low Reynolds number aerodynamics have been utilized in the systematic design of a high endurance micro-quadrotor. Based on available characteristics, the physical relations governing electric propulsion system and structural weights have been derived towards a sizing methodology for small-scale rotary-wing vehicles

Navigation Sensor Stochastic Error Modeling and Nonlinear Estimation for Low-Cost Land Vehicle Navigation

The increasing use of low-cost inertial sensors in various mass-market applications necessitates their accurate stochastic modeling. Such task faces challenges due to outliers in the sensor measurements caused by internal and/or external factors. To optimize the navigation performance, robust estimation techniques are required to reduce the influence of outliers to the stochastic modeling process. The Generalized Method of Wavelet Moments (GMWM) and its Multi-signal extensions (MS-GMWM) represent the latest trend in the field of inertial sensor error stochastic analysis, they are capable of efficiently modeling the highly complex random errors displayed by low-cost and consumer-grade inertial sensors and provide very advantageous guarantees for the statistical properties of their estimation products. On the other hand, even though a robust version exists (RGMWM) for the single-signal method in order to protect the estimation process from the influence of outliers, their detection remains a challenging task, while such attribute has not yet been bestowed in the multi-signal approach. Moreover, the current implementation of the GMWM algorithm can be computationally intensive and does not provide the simplest (composite) model. In this work, a simplified implementation of the GMWM-based algorithm is presented along with techniques to reduce the complexity of the derived stochastic model under certain conditions. Also, it is shown via simulations that using the RGMWM every time, without the need for contamination existence confirmation, is a worthwhile trade-off between reducing the outlier effects and decreasing the estimator efficiency. Generally, stochastic modeling techniques, including the GMWM, make use of individual static signals for inference. However, it has been observed that when multiple static signal replicates are collected under the same conditions, they maintain the same model structure but exhibit variations in parameter values, a fact that called for the MS-GMWM. Here, a robust multi-signal method is introduced, based on the established GMWM framework and the Average Wavelet Variance (AWV) estimator, which encompasses two robustness levels: one for protection against outliers in each considered replicate and one to safeguard the estimation against the collection of signal replicates with significantly different behaviour than the majority. From that, two estimators are formulated, the Singly Robust AWV (SR-AWV) and the Doubly Robust (DR-AWV) and their model parameter estimation efficiency is confirmed under different data contamination scenarios in simulation and case studies. Furthermore, a hybrid case study is conducted that establishes a connection between model parameter estimation quality and implied navigation performance in those data contamination settings. Finally, the performance of the new technique is compared to the conventional Allan Variance in a land vehicle navigation experiment, where the inertial information is fused with an auxiliary source and vehicle movement constraints using the Extended and Unscented Kalman Filters (EKF/UKF). Notably, the results indicate that under linear-static conditions, the UKF with the new method provides a 16.8-17.3% improvement in 3D orientation compared to the conventional setting (AV with EKF), while the EKF gives a 7.5-9.7% improvement. Also, in dynamic conditions (i.e., turns), the UKF demonstrates an 14.7-17.8% improvement in horizontal positioning and an 11.9-12.5% in terms of 3D orientation, while the EKF has an 8.3-12.8% and an 11.4-11.7% improvement respectively. Overall, the UKF appears to perform better but has a significantly higher computational load compared to the EKF. Hence, the EKF appears to be a more realistic option for real-time applications such as autonomous vehicle navigation