Implementation in Embedded Systems of State Observers Based on Multibody Dynamics

Programa Oficial de Doutoramento en Enxeñaría Naval e Industrial . 5015V01[Abstract] Simulation has become an important tool in the industry that minimizes either the cost and time of new products development and testing. In the automotive industry, the use of simulation is being extended to virtual sensing. Through an accurate model of the vehicle combined with a state estimator, variables that are difficult or costly to measure can be estimated. The virtual sensing approach is limited by the low computational power of invehicle hardware due to the strictest timing, reliability and safety requirements imposed by automotive standards. With the new generation hardware, the computational power of embedded platforms has increased. They are based on heterogeneous processors, where the main processor is combined with a co-processor, such as Field Programmable Gate Arrays (FPGAs). This thesis explores the implementation of a state estimator based on a multibody model of a vehicle in new generation embedded hardware. Different implementation strategies are tested in order to explore the advantages that an FPGA can provide. A new state-parameter-input observer is developed, providing accurate estimations. The proposed observer is combined with an efficient multibody model of a vehicle, achieving real-time execution.[Resumen] La simulación se ha convertido en una importante herramienta para la industria que permite minimizar tanto costes como tiempo de desarrollo y test de nuevos productos. En automoción, el uso de la simulación se extiende al desarrollo de sensores virtuales. Mediante un modelo preciso de un vehículo combinado con un observador de estados, variables que son caras o imposibles de medir pueden ser estimadas. La principal limitación para utilizar sensores virtuales en los vehículos es la baja potencia computacional de los procesadores instalados a bordo, debido a los estrictos requisitos impuestos por los standards de automoción. Con el hardware de nueva generación, el poder de cálculo de las plataformas empotradas se ha visto incrementado. Estos nuevos procesadores son del tipo heterogéneo, donde el procesador principal se complementa con un co-procesador, como una Field Programmable Gate Array (FPGA). Esta tesis explora la implementación de un observador de estados basado en un modelo multicuerpo de un vehículo en hardware empotrado de nueva generación. Se han probado diferentes implementaciones para evaluar las ventajas de disponer de una FPGA en el procesador. Se ha desarrollado un nuevo observador de estados, parámetros y entradas que permite obtener estimaciones de gran precisión. Combinando dicho observador con un eficiente modelo multicuerpo de un vehículo, se consigue rendimiento en tiempo real.[Resumo] A simulación estase a converter nunha importante ferramenta na industria que permite minimizar custes e tempo tanto de desenvolvemento coma de test de novos productos. En automoción, o uso da simulación esténdese á implementación de sensores virtuais. Mediante un modelo preciso dun vehículo combinado cun observador de estados, pódense estimar variables que son caras ou imposíbeis de medir. A principal limitación para utilizar sensores virtuais nos vehículos é a baixa potencia computacional dos procesadores instalados a bordo, debido aos estritos requisitos impostos polos estándares de automoción. Co hardware de nova xeración, o poder de cálculo das plataformas empotradas vese incrementado. Estos novos procesadores son de tipo heteroxéneo, onde o procesador principal compleméntase cun co-procesador, coma unha Field Programmable Gate Array (FPGA). Esta tese explora a implementación dun observador de estados basado nun modelo multicorpo dun vehículo en hardware empotrado de nova xeración. Diferentes implementacións foron probadas para avaliar as vantaxes de dispoñer dunha FPGA no procesador. Un novo observador de estados, parámetros e entradas deseñado nesta tese permite obter estimacións de gran precisión. Combinando dito observador cun eficiente modelo multicorpo dun vehículo, conséguese rendemento de tempo real

Ultra Wide-Band Localization and SLAM: A Comparative Study for Mobile Robot Navigation

In this work, a comparative study between an Ultra Wide-Band (UWB) localization system and a Simultaneous Localization and Mapping (SLAM) algorithm is presented. Due to its high bandwidth and short pulses length, UWB potentially allows great accuracy in range measurements based on Time of Arrival (TOA) estimation. SLAM algorithms recursively estimates the map of an environment and the pose (position and orientation) of a mobile robot within that environment. The comparative study presented here involves the performance analysis of implementing in parallel an UWB localization based system and a SLAM algorithm on a mobile robot navigating within an environment. Real time results as well as error analysis are also shown in this work

6-DoF Navigation Systems for Autonomous Underwater Vehicles

Austri

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

GNSS/INS/Star Tracker Integrated Navigation System for Earth-Moon Transfer Orbit

Over the last few years, new Global Navigation Satellite System (GNSS) applications have emerged that go far beyond the original objectives of GNSS which was providing position, velocity and timing (PVT) services for land, maritime, and air applications. Indeed, today, GNSS is used in Low Earth Orbit (LEO) for a wide range of applications such as real-time navigation, formation flying, precise time synchronization, orbit determination and atmospheric profiling. GNSS, in fact, can maximize the autonomy of a spacecraft and reduce the burden and costs of network operations. For this reason, there is a strong interest to also use GNSS for High Earth Orbit or Highly Elliptical Orbit (HEO) missions. However, the use of GNSS for HEO up to Moon altitudes is still new, and terrestrial GNSS receivers have not been designed to cope with the space environment which affects considerably the GNSS receiver performance and the GNSS solution (e.g. navigation solution). The goal of our research is therefore to develop a proof of concept of a spaceborne GNSS receiver for Earth-Moon transfer orbits, assisted by Inertial Navigation System (INS), a Star Tracker and an orbital forces model to increase the navigation accuracy and to achieve the required sensitivity

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.This research was partially funded by the Campus de Excelencia Internacional Andalucia Tech, University of Malaga, Malaga, Spain. Partial funding for open access charge: Universidad de Málag