<p>Many traffic incidents are due to the driver’s lack of attention, resulting in dangerous lane departures, either sliding off theroad or into the oppose lane. These kinds of incidents often have serious outcomes, which has led to much effort being concentrated on preventing or lessening the damages when the incident is already a fact, for example by installing safety belts and air bags. These measures may be considered to be acts of so-called passive safety. </p><p>Active safety on the other hand, means that the safety systems intervene before the incidents have occurred. Lane Keeping Aid (LKA), which has been developed and implemented in this master thesis project, is a system designed to support the driver in the lateral axis in situations when unwanted lane departure is an evident risk. </p><p>To be able to determine when the system should intervene and support the driver, information regarding how the driver handles the vehicle, along with the vehicle’s position and direction in the lane, is essential. The car’s position may be obtained by installing a camera in the vehicle. The information needed regarding other things, e.g. the car’s position in relation to the lane, is obtained by using a Kalmanfilter, which is based on a physically developed model, and which estimates the mentioned distances. Based on measurements and estimated values, it is possible for the LKA system to calculate an assistance torque, aimed at decreasing the lateral deviation from the centre of the lane. An electric power steering, instead of a conventional hydraulic steering servo is then used to produce the torque. </p><p>The LKA system has been developed in a simulation environment using Simulink before being implemented, in order to monitor the function of the system before beginning actual testdrives. Furthermore, real measurement data given at driving with the test vehicle has been used to adjust and test the function. </p><p>The results from the project’s first phase, in the simulation environment, show that the estimated values from the Kalmanfilter correlates well with real test data. Simulations with real measurement data show that the system functions as intended. </p><p>Finally, it may also be mentioned, that the system has yet not been fully tested in a vehicle equipped with an electric power steering, which ought to be included in future development of the system.</p>

Die Fahrzeuglokalisierung ist im Bereich der Fahrerassistenzsysteme von entscheidender Bedeutung und Voraussetzung fur verschiedene Anwendungen der Robotik, wie z.B. Navigation oder Kollisionsvermeidung fur fahrerlose Transportsysteme (FTS). In dieser Arbeit wird ein Verfahren zur Lokalisierung mittels Landmarken vorgestellt, die eine Orientierung bezuglich einer Karte ermoglichen. Dabei werden der Erweiterte- Kalman-Filter und der Partikel-Filter fur diese Aufgabe untersucht und verglichen. Ein Schwerpunkt dieser Betrachtungen stellt dabei der Partikel-Filter dar. Die besondere Problematik der Initialisierung wird ausfuhrlich fur beide Filter dargestellt. Simulationen und Versuche zeigen, dass sich der Partikel-Filter fur eine robuste Lokalisierung der Fahrzeugposition verwenden lasst. Im Vergleich dazu kann der Erweiterte-Kalman-Filter nur im begrenzten Maße eingesetzt werden. / The localization of vehicles is of vital importance in the field of driver assistance systems and a requirement of different applications for robotics, i.e. navigation or collision avoidance for automatic guided vehicle systems. In this thesis an approach for localization by means of landmarks is introduced, which enables an orientation regarding a map. The extended Kalman filter and the particle filter are analyzed and compared. The main focus for this consideration is on the particle filter. The problematic for initialization is discussed in detail for both filters. Simulations and tests prove that the particle filter is suitable for robust localization of the vehicle position. Compared to this, the extended Kalman filter can only be used to a certain extend.

<p>This report compares the development tools Ascet and Matlab with regard to their suitability as tools for model based development of signal processing software for embedded systems. We derive appropriate metrics of quality and perform an implementation of a signal processing algorithm called RAI, Roll Angle Indicator, in both tools. RAI is an algorithm that with an Extended Kalman Filter estimates the roll angle, that is how much a motorcycle is leaning. From the models implemented in Ascet and Matlab we then generate C-code for a embedded system. This code is then run on an embedded target containing a Infineon C167 microprocessor. Information from the implementation and the performance of the generated C-code is then used to compare the two model based development tools.</p>

This report compares the development tools Ascet and Matlab with regard to their suitability as tools for model based development of signal processing software for embedded systems. We derive appropriate metrics of quality and perform an implementation of a signal processing algorithm called RAI, Roll Angle Indicator, in both tools. RAI is an algorithm that with an Extended Kalman Filter estimates the roll angle, that is how much a motorcycle is leaning. From the models implemented in Ascet and Matlab we then generate C-code for a embedded system. This code is then run on an embedded target containing a Infineon C167 microprocessor. Information from the implementation and the performance of the generated C-code is then used to compare the two model based development tools.

The purpose of this paper was to implement Kalman filtering in the form of pre-existing Simulink blocks into a CubeSat attitude determination and control system simulation and to evaluate their performance. In recent versions of Simulink, the block library has been expanded, providing a new level of abstraction for simulation engineers. The capabilities of such library filter blocks have previously not been explored for space applications and could offer a faster and more simplified filter integration process. Three types of filter implementations have been realized, being classic Kalman filter, extended Kalman filter and unscented Kalman filter. These have been applied to the outputs of the coarse Sun sensor and Earth horizon sensor, as well as to the simulation attitude estimate. State propagation functions have been defined in the form of constant and linear approximations in addition to state propagation following the same structure as the simulation reference truth. Filter efficiency was evaluated using control error, pointing knowledge, pointing accuracy and variance as performance measures. Furthermore, interventions were introduced in the form of sensor data loss and solar panel deployment. The Kalman filter blocks were successfully integrated into the simulation. Performance measures revealed that constant state transition functions provided the best performance in most cases, exceptions being the extended Kalman filter and unscented Kalman filter of the attitude estimate application. Here, the true state propagation instead outperformed the other filters. Signal data loss showed that the true state propagation was the only one that could accurately predict the attitude state in a scenario when sensors fail to provide data. Solar panel deployment could not be utilized to evaluate the filter performance as the filter implementation did not support prediction of a dynamic attitude state. Results suggest that the pre-existing Simulink filter blocks can provide an easier alternative to defining filters from scratch. However, great care needs to be taken when tuning block parameters and constructing state transition functions to assure proper behavior. / Syftet med arbetet har varit att implementera Kalmanfilter i formen av fördefinierade Simulink-block i en simulering av ett system för attitydbestämning och styrning för en CubeSat, och utvärdera prestandan. I nyare versioner av Simulink har blockbiblioteken utökats, vilket har introducerat nya nivåer av abstraktion för simuleringsingenjörer. Möjligheterna hos filterblock i sådana bibliotek har ännu inte utforskats för rymdtekniska tillämpningar, och skulle kunna leda till snabbare och enklare integrering av filter. Tre typer av filterimplementationer har genomförts: klassiska Kalmanfilter, utökat Kalmanfilter och oparfymerat Kalmanfilter. Dessa har applicerats till utdata från solsensor och jordhorisontsensor, samt till simuleringens uppskattade attityd. Funktioner för tillståndspropagering har definierats i formen av konstanta och linjära approximationer tillsammans med den verkliga tillståndspropageringen, som har samma struktur som simuleringens sanna referensvärde. Effektiviteten hos filtren har utvärderats genom kontrollfel, riktningskunskap, riktningsnoggrannhet och varians som prestandamått. Vidare har interventioner introducerats i form av förlust av sensordata och utfällning av solpaneler. Kalmanfilterblocken integrerades med framgång i simuleringen. Prestandamåtten visade att de konstanta funktionerna för tillståndspropagering gav bäst prestanda i de flesta fallen, förutom i fallet av utökat Kalmanfilter och oparfymerat Kalmanfilter i appliceringen på den uppskattade attityden. I det sistnämnda fallet var det den verkliga tillståndspropageringen som presterade bättre än de andra filtren. Förlust av signaldata visade att den verkliga tillståndspropageringen är den enda som med säkerhet kan förutsäga utvecklingen av attityden i ett läge där sensorerna inte längre levererar data. Utfällningen av solpanelerna kunde inte utnyttjas för att utvärdera prestandan hos filtren, då implementeringen av filtren inte kan förutsäga utvecklingen av ett dynamiskt attitydtillstånd. Resultaten antyder att fördefinierade Simulink-filter kan erbjuda ett enklare alternativ till att definiera filter helt från början. Dock så krävs noga omsorg vid inställning av blockparametrar och vid konstruktion av funktioner för tillståndspropagering för att säkerställa korrekt beteende

<p>The aim of the Master’s Thesis work is to study and develop algorithms for autonomous travel of a UGV (Unmanned Ground Vehicle). A vehicle for the mounting of sensors has been constructed in order to perform the work. Since the UGV is to be used outdoor in urban areas, GPS can be used. To improve precision and robustness, inertial navigation is used in addition to GPS, since GPS reception is likely to be diminished in such areas. The sensors used for navigation are consequently GPS, magnetometers, accelerometers, gyroscopes, tachometers and ultra sonic sensors measuring distance to be used in detection of obstacles. The system has been implemented in Matlab. Two alternative methods of navigation with sensor fusion have been developed; one is a decentralized method with Kalman filtering using an error model and the other is a centralized particle filter using an all-embracing model of the vehicle. The two methods have been evaluated and compared. Test results show that the two methods perform equivalently.</p><p>The autonomous travel is undertaken between predetermined waypoints. In order to steer the vehicle a PID-controller based on the error between heading and its reference value is used. The computation of the reference value is based on position and heading in comparison to the desired path. The system has been tested using different routes and the results show an evident improvement of the precision in navigation compared to using only GPS-data. This holds for both navigation methods. Simulation of collision avoidance using virtual force fields shows satisfying results as well as terrain navigation with coordinate map referencing.</p> / <p>Examensarbetet är en studie i utveckling av algoritmer för autonom förflyttning av en UGV (eng Unmanned Ground Vehicle). För ändamålet har en farkost konstruerats där budgetsensorer för navigering används. Farkosten är tänkt att färdas utomhus i tätbebyggt område och GPS används. För förbättring av noggrannhet och robusthet vid dålig GPS-mottagning används även sensorer för tröghetsnavigering vilket här innebär magnetometrar, accelerometrar, gyron och tachometrar. För hinderdetektering finns avståndsmätande ultraljudssonar. Systemet som tagits fram har implementerats i realtid i Matlab. Två olika navigeringsmetoder med sensorfusion har utprovats; en decentraliserad variant med kalmanfilter som är uppbyggd kring felmodeller och en centraliserad variant med ett partikelfilter som använder en helhetsmodell för farkosten. De båda navigeringsmetoderna har utvärderats och jämförts. Resultat visar att de båda metoderna presterar likvärdigt.</p><p>Den autonoma förflyttningen utförs mellan förutbestämda brytpunkter. För att styra farkosten har en PID-regulator baserad på felet mellan kurs och börvärde använts. Börvärdet på kurs baseras på nuvarande position och riktning relativt den önskade färdvägen. Olika körsituationer har testats och resultaten visar en markant förbättring av navigeringsprecisionen jämfört med endast GPS-mätningar för både kalman- och partikelfilter. Simuleringar på vektorfältsstyrning med virtuella kraftfält för att undvika hinder har utförts med goda resultat liksom simuleringar av kartreferenspositionering.</p>

The goal of this project was to make a shirt with three embedded IMU sensors (Inertial Measurement Unit) that can measure a person’s movements throughout an entire workday. This can provide information about a person’s daily routine movements and aid in finding activities which can lead to work-related injuries in order to prevent them. The objective was hence to construct a sensor fusion framework that could retrieve the measurements from these three sensors and to create an estimate of the human body orientation and to estimate the angular movements of the arms. This was done using an extended Kalman filter which uses the accelerometer and magnetometer values to retrieve the direction of gravity and north respectively, thus providing a coordinate system that can be trusted in the long term. Since this method is sensitive to quick movements and magnetic disturbance, gyroscope measurements were used to help pick up quick movements. The gyroscope measurements need to be integrated in order to get the angle, which means that we get accumulated errors. This problem is reduced by the fact that we retrieve a correct long-term reference without accumulated errors from the accelerometer and magnetometer measurements. The Kalman filter estimates three quaternions describing the orientation of the upper body and the two arms. These quaternions were then translated into Euler angles in order to get a meaningful description of the orientations. The measurements were stored on a memory card or broadcast on both the local net and the Internet. These data were either used offline in Matlab or shown in real-time in the program Unity 3D. In the latter case the user could see that a movement gives rise to a corresponding movement on a skeleton model on the screen.

The aim of the Master’s Thesis work is to study and develop algorithms for autonomous travel of a UGV (Unmanned Ground Vehicle). A vehicle for the mounting of sensors has been constructed in order to perform the work. Since the UGV is to be used outdoor in urban areas, GPS can be used. To improve precision and robustness, inertial navigation is used in addition to GPS, since GPS reception is likely to be diminished in such areas. The sensors used for navigation are consequently GPS, magnetometers, accelerometers, gyroscopes, tachometers and ultra sonic sensors measuring distance to be used in detection of obstacles. The system has been implemented in Matlab. Two alternative methods of navigation with sensor fusion have been developed; one is a decentralized method with Kalman filtering using an error model and the other is a centralized particle filter using an all-embracing model of the vehicle. The two methods have been evaluated and compared. Test results show that the two methods perform equivalently. The autonomous travel is undertaken between predetermined waypoints. In order to steer the vehicle a PID-controller based on the error between heading and its reference value is used. The computation of the reference value is based on position and heading in comparison to the desired path. The system has been tested using different routes and the results show an evident improvement of the precision in navigation compared to using only GPS-data. This holds for both navigation methods. Simulation of collision avoidance using virtual force fields shows satisfying results as well as terrain navigation with coordinate map referencing. / Examensarbetet är en studie i utveckling av algoritmer för autonom förflyttning av en UGV (eng Unmanned Ground Vehicle). För ändamålet har en farkost konstruerats där budgetsensorer för navigering används. Farkosten är tänkt att färdas utomhus i tätbebyggt område och GPS används. För förbättring av noggrannhet och robusthet vid dålig GPS-mottagning används även sensorer för tröghetsnavigering vilket här innebär magnetometrar, accelerometrar, gyron och tachometrar. För hinderdetektering finns avståndsmätande ultraljudssonar. Systemet som tagits fram har implementerats i realtid i Matlab. Två olika navigeringsmetoder med sensorfusion har utprovats; en decentraliserad variant med kalmanfilter som är uppbyggd kring felmodeller och en centraliserad variant med ett partikelfilter som använder en helhetsmodell för farkosten. De båda navigeringsmetoderna har utvärderats och jämförts. Resultat visar att de båda metoderna presterar likvärdigt. Den autonoma förflyttningen utförs mellan förutbestämda brytpunkter. För att styra farkosten har en PID-regulator baserad på felet mellan kurs och börvärde använts. Börvärdet på kurs baseras på nuvarande position och riktning relativt den önskade färdvägen. Olika körsituationer har testats och resultaten visar en markant förbättring av navigeringsprecisionen jämfört med endast GPS-mätningar för både kalman- och partikelfilter. Simuleringar på vektorfältsstyrning med virtuella kraftfält för att undvika hinder har utförts med goda resultat liksom simuleringar av kartreferenspositionering.

Zur detaillierten Beschreibung des Lastabtragverhaltens textiler Bewehrung im Beton ist es erforderlich, das Penetrationsverhalten der Betonmatrix in die stark heterogene Garnstruktur zu beschreiben. Zur Charakterisierung der Mikrostruktur im Querschnitt wurde eine Bildanalysemethode entwickelt, um die Verbundsituation der einzelnen Filamente quantitativ auswerten zu können. Um eine räumliche Beschreibung der Verbundsituation zu erreichen, wurde die Strategie verfolgt, aus aufeinander folgenden Schichtaufnahmen mittels Rasterelektronenmikroskopie eine räumliche Struktur abzuleiten. Hierzu wurden zum einen die experimentelle Vorgehensweise erarbeitet und zum anderen ein Ansatz für die Zuordnung der Filamente zwischen den einzelnen Querschnitten entwickelt.

As autonomous driving is rapidly becoming the next major challenge in the auto- motive industry, the problem of Simultaneous Localization And Mapping (SLAM) has never been more relevant than it is today. This thesis presents the idea of examining SLAM algorithms by implementing such an algorithm on a radio con- trolled car which has been fitted with sensors and microcontrollers. The software architecture of this small-scale vehicle is based on the Robot Operating System (ROS), an open-source framework designed to be used in robotic applications. This thesis covers Extended Kalman Filter (EKF)-based SLAM, FastSLAM, and GraphSLAM, examining these algorithms in both theoretical investigations, simulations, and real-world experiments. The method used in this thesis is model- based development, meaning that a model of the vehicle is first implemented in order to be able to perform simulations using each algorithm. A decision of which algorithm to be implemented on the physical vehicle is then made backed up by these simulation results, as well as a theoretical investigation of each algorithm. This thesis has resulted in a dynamic model of a small-scale vehicle which can be used for simulation of any ROS-compliant SLAM-algorithm, and this model has been simulated extensively in order to provide empirical evidence to define which SLAM algorithm is most suitable for this application. Out of the algo- rithms examined, FastSLAM was proven to the best candidate, and was in the final stage, through usage of the ROS package gMapping, successfully imple- mented on the small-scale vehicle.