Using Low-cost IoT-based inclinometers for damage detection of a Bridge model

Nowadays, researchers are paying close attention to using inclinometers for Structural Health Monitoring (SHM) applications. Moreover, the applications based on using inclinometers can detect the magnitude and location of bridge pathologies. However, as these applications are based on expensive commercial inclinometers, their use is typically exclusive to the SHM of structures with a high monitoring budget. There is a gap in the literature with the development and validation of low-cost accurate angular-meters for decreasing the monitoring cost of inclinometer-based damage detection applications. This work aims to develop low-cost IoT-based inclinometers for detecting damage in bridge structures. The Low-cost Adaptable Reliable Angle-meter (LARA) is a novel inclinometer that accurately measures an induced inclination by combining the measurements of five gyroscopes and five accelerometers. The accuracy, resolution, Allan variance, and standard deviation of LARA are examined through laboratory experiments and are compared with those obtained by numerical slope calculations and a commercial inclinometer (HI-INC). For further experimental validation, a robotic vehicle model is designed and developed to simulate a moving load over a bridge model. The vehicle model integrates IoT technology and can be utilized in different damage detection experiments. The outcomes of a load test experiment using a simple beam model demonstrate the high accuracy (0.003 degrees) of LARA measurements. LARA may be used for structural damage identification and location in bridges utilizing inclinometers because of its low cost and high accuracy

Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV

Several and distinct Unmanned Aircraft Vehicle (UAV) applications are emerging, demanding steps to be taken in order to allow those platforms to operate in an un-segregated airspace. The key risk component, hindering the widespread integration of UAV in an un-segregated airspace, is the autonomous component: the need for a high level of autonomy in the UAV that guarantees a safe and secure integration in an un-segregated airspace. At this point, the UAV accurate state estimation plays a fundamental role for autonomous UAV, being one of the main responsibilities of the onboard autopilot. Given the 21st century global economic paradigm, academic projects based on inexpensive UAV platforms but on expensive commercial autopilots start to become a non-economic solution. Consequently, there is a pressing need to overcome this problem through, on one hand, the development of navigation systems using the high availability of low cost, low power consumption, and small size navigation sensors offered in the market, and, on the other hand, using Global Navigation Satellite Systems Software Receivers (GNSS SR). Since the performance that is required for several applications in order to allow UAV to fly in an un-segregated airspace is not yet defined, for most UAV academic applications, the navigation system accuracy required should be at least the same as the one provided by the available commercial autopilots. This research focuses on the investigation of the performance of an integrated navigation system composed by a low performance inertial measurement unit (IMU) and a GNSS SR. A strapdown mechanization algorithm, to transform raw inertial data into navigation solution, was developed, implemented and evaluated. To fuse the data provided by the strapdown algorithm with the one provided by the GNSS SR, an Extended Kalman Filter (EKF) was implemented in loose coupled closed-loop architecture, and then evaluated. Moreover, in order to improve the performance of the IMU raw data, the Allan variance and denoise techniques were considered for both studying the IMU error model and improving inertial sensors raw measurements. In order to carry out the study, a starting question was made and then, based on it, eight questions were derived. These eight secondary questions led to five hypotheses, which have been successfully tested along the thesis. This research provides a deliverable to the Project of Research and Technologies on Unmanned Air Vehicles (PITVANT) Group, consisting of a well-documented UAV Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV II navigation algorithm, an implemented and evaluated navigation algorithm in the MatLab environment, and Allan variance and denoising algorithms to improve inertial raw data, enabling its full implementation in the existent Portuguese Air Force Academy (PAFA) UAV. The derivable provided by this thesis is the answer to the main research question, in such a way that it implements a step by step procedure on how the Strapdown IMU (SIMU)/GNSS SR should be developed and implemented in order to replace the commercial autopilot. The developed integrated SIMU/GNSS SR solution evaluated, in post-processing mode, through van-test scenario, using real data signals, at the Galileo Test and Development Environment (GATE) test area in Berchtesgaden, Germany, when confronted with the solution provided by the commercial autopilot, proved to be of better quality. Although no centimetre-level of accuracy was obtained for the position and velocity, the results confirm that the integration strategy outperforms the Piccolo system performance, being this the ultimate goal of this research work

Innovative Solutions for Navigation and Mission Management of Unmanned Aircraft Systems

The last decades have witnessed a significant increase in Unmanned Aircraft Systems (UAS) of all shapes and sizes. UAS are finding many new applications in supporting several human activities, offering solutions to many dirty, dull, and dangerous missions, carried out by military and civilian users. However, limited access to the airspace is the principal barrier to the realization of the full potential that can be derived from UAS capabilities. The aim of this thesis is to support the safe integration of UAS operations, taking into account both the user's requirements and flight regulations. The main technical and operational issues, considered among the principal inhibitors to the integration and wide-spread acceptance of UAS, are identified and two solutions for safe UAS operations are proposed: A. Improving navigation performance of UAS by exploiting low-cost sensors. To enhance the performance of the low-cost and light-weight integrated navigation system based on Global Navigation Satellite System (GNSS) and Micro Electro-Mechanical Systems (MEMS) inertial sensors, an efficient calibration method for MEMS inertial sensors is required. Two solutions are proposed: 1) The innovative Thermal Compensated Zero Velocity Update (TCZUPT) filter, which embeds the compensation of thermal effect on bias in the filter itself and uses Back-Propagation Neural Networks to build the calibration function. Experimental results show that the TCZUPT filter is faster than the traditional ZUPT filter in mapping significant bias variations and presents better performance in the overall testing period. Moreover, no calibration pre-processing stage is required to keep measurement drift under control, improving the accuracy, reliability, and maintainability of the processing software; 2) A redundant configuration of consumer grade inertial sensors to obtain a self-calibration of typical inertial sensors biases. The result is a significant reduction of uncertainty in attitude determination. In conclusion, both methods improve dead-reckoning performance for handling intermittent GNSS coverage. B. Proposing novel solutions for mission management to support the Unmanned Traffic Management (UTM) system in monitoring and coordinating the operations of a large number of UAS. Two solutions are proposed: 1) A trajectory prediction tool for small UAS, based on Learning Vector Quantization (LVQ) Neural Networks. By exploiting flight data collected when the UAS executes a pre-assigned flight path, the tool is able to predict the time taken to fly generic trajectory elements. Moreover, being self-adaptive in constructing a mathematical model, LVQ Neural Networks allow creating different models for the different UAS types in several environmental conditions; 2) A software tool aimed at supporting standardized procedures for decision-making process to identify UAS/payload configurations suitable for any type of mission that can be authorized standing flight regulations. The proposed methods improve the management and safe operation of large-scale UAS missions, speeding up the flight authorization process by the UTM system and supporting the increasing level of autonomy in UAS operations

Generic Multisensor Integration Strategy and Innovative Error Analysis for Integrated Navigation

A modern multisensor integrated navigation system applied in most of civilian applications typically consists of GNSS (Global Navigation Satellite System) receivers, IMUs (Inertial Measurement Unit), and/or other sensors, e.g., odometers and cameras. With the increasing availabilities of low-cost sensors, more research and development activities aim to build a cost-effective system without sacrificing navigational performance. Three principal contributions of this dissertation are as follows: i) A multisensor kinematic positioning and navigation system built on Linux Operating System (OS) with Real Time Application Interface (RTAI), York University Multisensor Integrated System (YUMIS), was designed and realized to integrate GNSS receivers, IMUs, and cameras. YUMIS sets a good example of a low-cost yet high-performance multisensor inertial navigation system and lays the ground work in a practical and economic way for the personnel training in following academic researches. ii) A generic multisensor integration strategy (GMIS) was proposed, which features a) the core system model is developed upon the kinematics of a rigid body; b) all sensor measurements are taken as raw measurement in Kalman filter without differentiation. The essential competitive advantages of GMIS over the conventional error-state based strategies are: 1) the influences of the IMU measurement noises on the final navigation solutions are effectively mitigated because of the increased measurement redundancy upon the angular rate and acceleration of a rigid body; 2) The state and measurement vectors in the estimator with GMIS can be easily expanded to fuse multiple inertial sensors and all other types of measurements, e.g., delta positions; 3) one can directly perform error analysis upon both raw sensor data (measurement noise analysis) and virtual zero-mean process noise measurements (process noise analysis) through the corresponding measurement residuals of the individual measurements and the process noise measurements. iii) The a posteriori variance component estimation (VCE) was innovatively accomplished as an advanced analytical tool in the extended Kalman Filter employed by the GMIS, which makes possible the error analysis of the raw IMU measurements for the very first time, together with the individual independent components in the process noise vector

System Development of an Unmanned Ground Vehicle and Implementation of an Autonomous Navigation Module in a Mine Environment

There are numerous benefits to the insights gained from the exploration and exploitation of underground mines. There are also great risks and challenges involved, such as accidents that have claimed many lives. To avoid these accidents, inspections of the large mines were carried out by the miners, which is not always economically feasible and puts the safety of the inspectors at risk. Despite the progress in the development of robotic systems, autonomous navigation, localization and mapping algorithms, these environments remain particularly demanding for these systems. The successful implementation of the autonomous unmanned system will allow mine workers to autonomously determine the structural integrity of the roof and pillars through the generation of high-fidelity 3D maps. The generation of the maps will allow the miners to rapidly respond to any increasing hazards with proactive measures such as: sending workers to build/rebuild support structure to prevent accidents. The objective of this research is the development, implementation and testing of a robust unmanned ground vehicle (UGV) that will operate in mine environments for extended periods of time. To achieve this, a custom skid-steer four-wheeled UGV is designed to operate in these challenging underground mine environments. To autonomously navigate these environments, the UGV employs the use of a Light Detection and Ranging (LiDAR) and tactical grade inertial measurement unit (IMU) for the localization and mapping through a tightly-coupled LiDAR Inertial Odometry via Smoothing and Mapping framework (LIO-SAM). The autonomous navigation module was implemented based upon the Fast likelihood-based collision avoidance with an extension to human-guided navigation and a terrain traversability analysis framework. In order to successfully operate and generate high-fidelity 3D maps, the system was rigorously tested in different environments and terrain to verify its robustness. To assess the capabilities, several localization, mapping and autonomous navigation missions were carried out in a coal mine environment. These tests allowed for the verification and tuning of the system to be able to successfully autonomously navigate and generate high-fidelity maps

Development of low-cost sensors for structural health monitoring applications

(English) There is increasing interest in developing low-cost sensors for economical structural health monitoring of civil engineering infrastructures. In addition to their price, they have the additional benefit of being easily connected to low-cost microcontrollers such as Arduino. A reliable data acquisition system based on Arduino technology can further lower the cost of data collection and monitoring, enabling long-term monitoring at an affordable cost. This thesis proposes the following four high-precision low-cost monitoring systems.Firstly, to correctly measure structural responses, a Cost Hyper-Efficient Arduino Product (CHEAP) has been developed. CHEAP is a system made up of five synchronized accelerometers connected to an Arduino microcontroller that works as a data collecting device. CHEAP is a uniaxial MEMS accelerometer with a sampling frequency of 85 Hz. To validate its performance, laboratory experiments were carried out and the results were compared with those of two high-precision accelerometers (PCB393A03 and PCB 356B18).Secondly, a unique low-cost inclinometer is presented, the Low-cost Adaptable Reliable Angle-meter (LARA), which measures inclination through the fusion of different sensors: five gyroscopes and five accelerometers. LARA combines a microcontroller based on Internet of Things technology (NODEMCU), allows wireless data transmission, and free commercial software for data collection (SerialPlot). To confirm the precision and resolution of this device, its measurements under laboratory conditions were compared with the theoretical ones and with those of a commercial inclinometer (HI-INC). Laboratory results of a load test on a beam demonstrate LARA's remarkable accuracy. It is concluded that the accuracy of LARA is sufficient for its application in detecting bridge damage.Thirdly, the effect of combining similar range sensors to investigate the increase of the accuracy and mitigation of the ambiental noises, is also elucidated. To investigate the sensor combination theory, a measuring equipment composed of 75 contactless ranging sensors controlled by only two microcontrollers (Arduinos), was built. The 75 sensors are 25 HC-SR04 (analog), 25 VL53L0X (digital), and 25 VL53L1X. (digital). In addition, the impact of various environmental conditions on the standard deviation, distribution functions, and error level of these sensors (HC-SR04, VL53L0X, and VL53L1X) is determined.Finally, a novel remote versatile data acquisition system is presented that allows the recording of time with microsecond resolution for the subsequent synchronization of the acquired data of the wireless sensors located at various points of a structure. This functionality is what would allow its application to static or quasi-static load tests or to the modal analysis of structures. The system developed has a noise density of 51 g/Hz and a sampling frequency of 333 Hz. This device was used to identify the eigenfrequencies and modal analysis of several structures (polvorín footbridges in Barcelona and Andoain Bridge, Donostia-San Sebastian). The comparison of the modal analysis of the Andoain Bridge using the acquired data of the developed accelerometer and data acquisition equipment with those of commercial accelerometers (PCB 607A61) were satisfactory.The low-cost accelerometer, inclinometer and data acquisition system developed and validated in this thesis can make SHM and infrastructure damage detection a reality at low cost, long term and remotely.(Español) Cada vez hay más interés en desarrollar sensores baratos para conocer de manera económica el estado de las infraestructuras civiles. Además de su precio, estos sensores tienen la ventaja añadida de poder conectarse fácilmente a microcontroladores de bajo coste como Arduino. Un sistema fiable de adquisición de datos basado en la tecnología Arduino puede disminuir aún más el coste de la recogida de datos y la monitorización, lo que permitiría una monitorización a largo plazo a un coste asequible. Esta tesis propone los cuatro siguientes sistemas de monitorización de alta precisión y bajo coste.En primer lugar, para medir correctamente las respuestas estructurales, se ha desarrollado el Cost Hyper-Efficient Arduino Product (CHEAP). CHEAP es un sistema compuesto por cinco acelerómetros sincronizados de bajo coste conectados a un microcontrolador Arduino que hace el papel de dispositivo de recogida de datos. CHEAP es un acelerómetro MEMS uniaxial con una frecuencia de muestreo de 85 Hz. Para validar su rendimiento, se efectuaron unos experimentos de laboratorio y sus resultados se compararon con los de dos acelerómetros de alta precisión (PCB393A03 y PCB 356B18). En segundo lugar, se presenta un inclinómetro de bajo coste, un Low-cost Adaptable Reliable Angle-meter (LARA), que mide la inclinación mediante la fusión de distintos sensores: cinco giroscopios y cinco acelerómetros. LARA combina un microcontrolador basado en la tecnología del Internet de las Cosas (NODEMCU), que permite la transmisión inalámbrica de datos, y un software comercial gratuito para la recogida de datos (SerialPlot). Para confirmar la precisión y resolución de este dispositivo, se compararon sus mediciones en condiciones de laboratorio con las teóricas y con las de un inclinómetro comercial (HI-INC). Los resultados de laboratorio de una prueba de carga en una viga demuestran la notable precisión de LARA. Se concluye que la precisión de LARA es suficiente para su aplicación en la detección de daños en puentes.En tercer lugar, también se dilucida el efecto de la combinación de sensores de rango similar para investigar el aumento de la precisión y la mitigación de los ruidos ambientales. Para investigar la teoría de la combinación de sensores, se construyó un equipo de medición compuesto por 75 sensores para la medición de distancias acoplados a dos microcontroladores de Arduino. Los 75 sensores son 25 HC-SR04 (analógicos), 25 VL53L0X (digitales) y 25 VL53L1X (digitales). Además, se determina el impacto de diversas condiciones ambientales en la desviación estándar, las funciones de distribución y el nivel de error de estos sensores.Por último, se presenta un novedoso y versátil sistema de adquisición de datos a distancia que permite el registro del tiempo con una resolución de microsegundos para la sincronización posterior de las lecturas de los sensores inalámbricos situados en diversos puntos de una estructura. Esta funcionalidad es lo que permitiría su aplicación a pruebas de carga estáticas o quasi-estaticas o al análisis modal de las estructuras. El sistema desarrollado tiene una densidad de ruido de 51 g/Hz y una frecuencia de muestreo de 333 Hz. Este dispositivo se utilizó para identificar las frecuencias propias y los modos de vibración de varias estructuras (pasarelas polvorín en Barcelona y Puente de Andoain, Donostia-San Sebastian). Los modos calculados en una de ellas, el Puente de Andoain, a partir de los datos obtenidos con el acelerómetro y sistema de adquisición de datos desarrollado se comparan satisfactoriamente con los de sensores comerciales (PCB 607A61). El acelerómetro, el inclinómetro y el sistema de adquisición de datos de bajo coste desarrollados y validados en esta tesis pueden hacer realidad la SHM y la detección de daños en infraestructuras a bajo coste, a largo plazo y de forma remota.Postprint (published version

Deterministic and stochastic error modeling of inertial sensors and magnetometers

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical refences.This thesis focuses on the deterministic and stochastic modeling and model parameter estimation of two commonly employed inertial measurement units. Each unit comprises a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer. In the first part of the thesis, deterministic modeling and calibration of the units are performed, based on real test data acquired from a flight motion simulator. The deterministic modeling and identification of accelerometers is performed based on a traditional model. A novel technique is proposed for the deterministic modeling of the gyroscopes, relaxing the test bed requirement and enabling their in-use calibration. This is followed by the presentation of a new sensor measurement model for magnetometers that improves the calibration error by modeling the orientation-dependent magnetic disturbances in a gimbaled angular position control machine. Model-based Levenberg-Marquardt and modelfree evolutionary optimization algorithms are adopted to estimate the calibration parameters of sensors. In the second part of the thesis, stochastic error modeling of the two inertial sensor units is addressed. Maximum likelihood estimation is employed for estimating the parameters of the different noise components of the sensors, after the dominant noise components are identified. Evolutionary and gradient-based optimization algorithms are implemented to maximize the likelihood function, namely particle swarm optimization and gradient-ascent optimization. The performance of the proposed algorithm is verified through experiments and the results are compared to the classical Allan variance technique. The results obtained with the proposed approach have higher accuracy and require a smaller sample data size, resulting in calibration experiments of shorter duration. Finally, the two sensor units are compared in terms of repeatability, present measurement noise, and unaided navigation performance.Seçer, GörkemM.S

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

Improvement of AOCS for nanosatellite based on Helmholtz coils software design using a GUI and the implementation of a new system for measure angular velocities

This work contains the analysis and improvements regarding attitude nanosatellite testing procedures. An utterly new system for measuring angular velocities has been designed from scratch in order to be applied to Cubesat testing campaigns. This system will help to track the rotations of the nanosatellite in the different tests. Knowing the angular velocity of the nanosatellite while performing the tests is so important. It allows to calibrate the gyroscopes as well as set a perfect and knows conditions to the satellite. Also, the measurements can be compared with the gyroscope angular velocityes obtained in order to recalibrate the system if necesary. Furthermore, a new design for Helmholtz coils software has been proposed using a user Interface in C++. This new software allows the user to initialize the coils with the desired magnetic fields required for testing. Two main operating ways has been considered: Static and Dynamic orbit magnetic field generation. Both systems can work altogether in order to check the attitude determination and control of the nanosatellite during testing campaigns. Nanosatellite needs to perform testing before its launch. Thus, the whole project avoids expensive risks which could doom the mission. The results obtained in this work show that the angular tracking works as desired for the testing purposes and the Helmholtz coils GUI simplifies a lot the testing process from the user point of view.Aquest treball conté l’anàlisi i les millores pel que fa als procediments de prova de nano satèl·lits d’actitud. Un sistema completament nou per mesurar velocitats angulars ha sigut dissenyat des de zero per aplicar-lo a les campanyes de proves de Cubesat. Aquest sis- tema ajudarà a seguir les rotacions del nano satel·lit en les diferents proves. Con `eixer la velocitat angular del nano satel·lit mentre es realitzen les proves és molt important, ja que permet calibrar els giroscopis així com establir unes condicions perfectes i conegudes al satèl·lit