Today’s system to measure the volume in a tank consists of a vertically placed sensor. When a vehicle is tilted the level of the fluid might change and if the volume is calculated based only on the information from the sensor it could result in a incorrect value. This thesis exams a new method to calculate the volume in tanks by using sensor fusion to crosscheck the i formation from the level sensors when the vehicle is tilted. The information is then used to see if it is possible to identify the grade of tilt by the change of fluid level. If the grade of tilt can be measured then the volume can be calculated in a more accurate way. The accuracy of measuring the fluid level depends on the tolerance; tank volume, sensor measurement and montage for both tank and sensor. This limitation is also examined. To exam if the tilt can be measured a volume calculation program was created to calculate the volume in tank as a function of tilt and fluid level. Volume calculation was done for both nominal cases and cases with tolerances for the tank and sensor. When analyzing the change in fluid level from the data, the result was that the tilt can’t be determined in a sufficient reliable manner. The tilt combination causing the fluid level to change could be more than one. This means that there are too many non unique solutions to determine the tilt with 5 degree accuracy.

Den obemannade  helikoptern Skeldar utvecklas av Saab Aeronautics. Saab är intresserade av hur väl sensorfusion kan användas för navigering av en obemannad helikopter. Med sensorfusion kan tillgängliga sensordata fusioneras. Det är mätningar från accelerometer, gyro, GPS och magnetometer som används av olika Kalmanfilter. Olika sensorfusionsmetoder föreslås, däribland Extended Kalman Filter (EKF) och ett heuristiskt filter för att visa på hur man intuitivt kan göra för att fusionera mätningar jämfört med det modellbaserade sättet som Kalmanfiltret erbjuder. För att skaffa sig en uppfattning om filtrets prestanda kommer orienteringen, där attityden är avgörande för stabiliteten, att valideras mot en AHRS. Vid reglering av helikoptern används återkoppling därför är det inte helt nödvändigt att navigeringen blir helt exakt. Sensorerna som används vid navigeringen utvärderas, dels med avseende på hur väl de uppfyller databladets specifikationer och dels med avseende på om sensorerna måste kalibreras och tillvägagångssättet för att göra detta.

The purpose of this master's thesis is to evaluate whether it is feasible to use the panchromatic band of Landsat 7 in order to improve the spatial resolution of colour images. The images are to be used as texture in visual databases for flight simulators and for this reason it is important that the fusion preserves natural colours. A number of methods for fusing panchromatic and multispectral images are discussed. Four of them are implemented and evaluated. The result is that standard methods such as HSI substitution are not suitable for this purpose since they do not preserve natural colours. However, if only high frequencies of the panchromatic image are used the resolution can be improved without noticeable colour distortion.

<p>The purpose of this master's thesis is to evaluate whether it is feasible to use the panchromatic band of Landsat 7 in order to improve the spatial resolution of colour images. The images are to be used as texture in visual databases for flight simulators and for this reason it is important that the fusion preserves natural colours. </p><p>A number of methods for fusing panchromatic and multispectral images are discussed. Four of them are implemented and evaluated. The result is that standard methods such as HSI substitution are not suitable for this purpose since they do not preserve natural colours. However, if only high frequencies of the panchromatic image are used the resolution can be improved without noticeable colour distortion.</p>

I denna rapport redogörs för en implementering av fusion mellan kortdistanssensorer. Syftet med denna implementering är att erhålla Stop &amp; Go-funktionalitet till den adaptiva farthållaren som idag finns som tillval i Scanias lastbilar. Adaptiv farthållning, ACC, är en funktion som automatiskt anpassar fordonets hastighet ifall detta kommer ikapp ett annat fordon som färdas långsammare än den inställda hastigheten. Scanias system använder sig idag av en långdistanssensor som ser långt men har smalt synfält. Genom att komplettera denna med kortdistanssensorer, som ser kort men brett, kan önskvärt synfält i närområdet för att säkert kunna implementera Stop &amp; Go-funktionen uppnås. Sensorfusion bygger på principen att två eller fler sensorer som ser samma sak ger en mer korrekt bild av verkligheten än en enskild. Fusionen kan genomföras på många olika sätt. I rapporten beskrivs tre metoder övergripligt; Bayesiska nätverk, auktionsalgoritmen samt Gating. I implementeringen används gatingmetoden. Initialt implementeras en målspårningsalgoritm med kalmanfilter. Efter uppdatering av sensormjukvaran väljs dock denna bort eftersom sensorernas interna målspårning då anses som tillräcklig. En modell med sensorfusionen och målselekteringen byggs i Simulink och programmeras sedan in i en hårdvaruenhet. Syftet med detta är att kunna utvärdera funktionen i ett prototypfordon. Inledningsvis rapporteras enbart korrelerade mål från modellen. För mer kontinuerlig målföljning, främst i kurvor, implementeras därefter en algoritm som även tar hänsyn till enskilda sensorers observationer. Dessa accpeteras dock först efter en viss valideringstid eftersom denna information har lägre konfidens än korrelerade mål. Provkörningar visar att målföljningen fungerar väl. En stor svårighet har varit att sålla bort stillastående mål från rörliga, främst i låga farter. Detta eftersom sensorernas hastighetsangivelse har låg precision vilket medför att ett stillastående mål kan rapporteras som ett långsamt rörligt. / In this thesis report an implementation of fusion between short range radars is described. The purpose of this implementation is to obtain Stop &amp; Go functionality for the adaptive cruise control which is offered as an option in today’s Scania trucks. Adaptive cruise control, ACC, is a feature that automatically adapts the vehicle speed if it should catch up to another vehicle moving slower than the desired set speed. For this application Scania today use a long range sensor that has a long but narrow field of view. By complementing this sensor with short range sensors, which have short but wide fields of view, the desired field of view in the short range area can be obtained. This is necessary in order to be able to safely implement the Stop &amp; Go functionality. Sensor fusion is based on the principle that two or more sensors overlooking the same area give a more accurate impression of reality than a single one. The fusion can be conducted in several ways. In the report three different methods are briefly described; Bayesian Networks, the Auction Algorithm and Gating. In the implementation the gating method is applied. Initially a target tracking algorithm using Kalman filter is implemented. However, after software updates in the short range sensors this algorithm is no longer used. This is because the improved tracking made internally by the sensors is considered to be sufficient, hence making an external tracking algorithm redundant. The sensor fusion and the target selection are implemented in a Simulink model which is later programmed into a hardware unit. The purpose of the latter is to be able to evaluate the functionality in a prototype vehicle. Initially, only associated targets are reported from the model. In order to obtain a more continuous target tracking, mainly while driving in curves, observations made only by single sensors are also considered. However, these measurements have lower level of confidence than the associated targets. Therefore these measurements first have to be validated for a certain period of time before they are approved. Test runs indicate that the target tracking works as intended. One major difficulty has been to separate stationary targets from slow moving ones, especially in low speeds. This is due to the fact that the sensors’ speed measurements are fairly inaccurate. Therefore a stationary target could be reported as a slow moving one.

<p>I denna rapport redogörs för en implementering av fusion mellan kortdistanssensorer. Syftet med denna implementering är att erhålla Stop & Go-funktionalitet till den adaptiva farthållaren som idag finns som tillval i Scanias lastbilar. Adaptiv farthållning, ACC, är en funktion som automatiskt anpassar fordonets hastighet ifall detta kommer ikapp ett annat fordon som färdas långsammare än den inställda hastigheten. Scanias system använder sig idag av en långdistanssensor som ser långt men har smalt synfält. Genom att komplettera denna med kortdistanssensorer, som ser kort men brett, kan önskvärt synfält i närområdet för att säkert kunna implementera Stop & Go-funktionen uppnås. Sensorfusion bygger på principen att två eller fler sensorer som ser samma sak ger en mer korrekt bild av verkligheten än en enskild. Fusionen kan genomföras på många olika sätt. I rapporten beskrivs tre metoder övergripligt; Bayesiska nätverk, auktionsalgoritmen samt Gating. I implementeringen används gatingmetoden.</p><p>Initialt implementeras en målspårningsalgoritm med kalmanfilter. Efter uppdatering av sensormjukvaran väljs dock denna bort eftersom sensorernas interna målspårning då anses som tillräcklig. En modell med sensorfusionen och målselekteringen byggs i Simulink och programmeras sedan in i en hårdvaruenhet. Syftet med detta är att kunna utvärdera funktionen i ett prototypfordon. Inledningsvis rapporteras enbart korrelerade mål från modellen. För mer kontinuerlig målföljning, främst i kurvor, implementeras därefter en algoritm som även tar hänsyn till enskilda sensorers observationer. Dessa accpeteras dock först efter en viss valideringstid eftersom denna information har lägre konfidens än korrelerade mål.</p><p>Provkörningar visar att målföljningen fungerar väl. En stor svårighet har varit att sålla bort stillastående mål från rörliga, främst i låga farter. Detta eftersom sensorernas hastighetsangivelse har låg precision vilket medför att ett stillastående mål kan rapporteras som ett långsamt rörligt.</p> / <p>In this thesis report an implementation of fusion between short range radars is described. The purpose of this implementation is to obtain Stop & Go functionality for the adaptive cruise control which is offered as an option in today’s Scania trucks. Adaptive cruise control, ACC, is a feature that automatically adapts the vehicle speed if it should catch up to another vehicle moving slower than the desired set speed. For this application Scania today use a long range sensor that has a long but narrow field of view. By complementing this sensor with short range sensors, which have short but wide fields of view, the desired field of view in the short range area can be obtained. This is necessary in order to be able to safely implement the Stop & Go functionality. Sensor fusion is based on the principle that two or more sensors overlooking the same area give a more accurate impression of reality than a single one. The fusion can be conducted in several ways. In the report three different methods are briefly described; Bayesian Networks, the Auction Algorithm and Gating. In the implementation the gating method is applied.</p><p>Initially a target tracking algorithm using Kalman filter is implemented. However, after software updates in the short range sensors this algorithm is no longer used. This is because the improved tracking made internally by the sensors is considered to be sufficient, hence making an external tracking algorithm redundant. The sensor fusion and the target selection are implemented in a Simulink model which is later programmed into a hardware unit. The purpose of the latter is to be able to evaluate the functionality in a prototype vehicle. Initially, only associated targets are reported from the model. In order to obtain a more continuous target tracking, mainly while driving in curves, observations made only by single sensors are also considered. However, these measurements have lower level of confidence than the associated targets. Therefore these measurements first have to be validated for a certain period of time before they are approved.</p><p>Test runs indicate that the target tracking works as intended. One major difficulty has been to separate stationary targets from slow moving ones, especially in low speeds. This is due to the fact that the sensors’ speed measurements are fairly inaccurate. Therefore a stationary target could be reported as a slow moving one.</p>

Denna rapport syftar till att beskriva arbetet kring att kunna detektera närvaro i ett rum medhjälp av så enkla sensorer som möjligt, kopplade till en Arduino. Samtidigt som detta skerså används också systemet till att med samma sensorer visa klimatet i rummet. Läsaren fåren inblick i problematiken med att detektera människor samt inom funktionen av de valdasensorerna. Utöver detta studeras energiförbrukningen i systemet. Rapportenmynnar ut i enslutsats där en procentuell chans för närvaro presenteras via en internetuppkoppling medhjälp av en omfattande testning av sensorernas beteende.

Diese Dissertation befasst sich mit der sensorgeführten Regelung von Industrierobotern zur Konturverfolgung. Beispiele dafür sind das robotergestützte Nähen, Entgraten oder das Auftragen von Dichtmasse entlang von Schweißnähten. Beim Nähen und Entgraten müssen während der Verfolgung der Kontur bestimmte Kontaktkräfte an möglicherweise nachgiebigen Werkstücken eingehalten werden. Dabei ist es in modernen Fertigungsprozessen wichtig, die Bewegung des Roboters mit wenig Einrichtaufwand vorzugeben. Dazu werden Sensorsysteme eingesetzt, die Bildinformationen und Kraftmessungen verarbeiten, um den Roboter mit gewünschter Kontaktkraft entlang sichtbarer Konturen eines Werkstückes zu führen. Der Fokus dieser Arbeit ist die Fusion der Sensordaten, um die Vorteile der einzelnen Sensoren in einer Aufgabe zu vereinen. Es werden Messwerte eines Kraft-Momenten Sensors, einer Kamera, eines Beschleunigungssensors und der kartesischen Position und Orientierung des Roboters verwendet. Zuerst wird die Berechnung der kartesischen Roboterposition untersucht. Es wird ein Beobachter vorgestellt, um unter Verwendung eines Beschleunigungssensors die Präzision des Positionssignales zu erhöhen. Anschließend wird das Kamerasystem untersucht und ein Verfahren vorgestellt, um Geschwindigkeit, Position und Orientierung des robotergeführten Werkzeuges entlang der Kontur vorzugeben. Danach wird auf die Ermittlung von Kontaktkräften eingegangen, wobei die Kompensation von Trägheitskräften mittels Beschleunigungssensoren untersucht wird. Der letzte Abschnitt befasst sich mit der Verbindung von bildgestützter Konturverfolgung und Kraftregelung an nachgiebigen Werkstücken. Durch die Nachgiebigkeit des Werkstückmaterials verformt sich die Kontur bei Kontakt. Durch bildgestützte Konturverfolgung wird eine Anpassung an diese Verformung vorgenommen -- somit besteht über die Verformbarkeit des Werkstückes eine Kopplung zwischen den beiden Regelkreisen. Diese Kopplung wird gelöst, indem auf Basis eines dynamischen Modells der Umgebung eine Kompensation der Werkstückverformung berechnet wird. Die Modellparameter zur Kompensation werden durch online Identifikation ermittelt. / This thesis focuses on the sensor-guided control of industrial robots for contour-following. Examples include the robot-guided sewing, grinding or the application of sealant along weld seams. Grinding and sewing require a certain contact force while following a countour of a workpiece, whereas the worpieces might be compliant. Modern production processes require a fast and simple way to set up the motion of the robot for the required task. Therefore sensor systems are used, which process visual and tactile information to guide the tool at a desired contact force along visible contours of a workpiece. The focus of this work is the fusion of sensor data, used to benefit from the advantages of each of the individual sensors in one control scheme. I combine the measurements of a force-torque sensor, a camera, an acceleration sensor and of the Cartesian position of the robot. First, I introduce details on the calculation of the Cartesian robot position. I present an observer-based structure that uses an acceleration sensor to improve the precision of the robot position signal. Then, I analyze the camera system and present a control structure that adapts the position, orientation and velocity of the robot-guided tool along the contour. Thereafter, I show details of force measurement, whereas I compensate for inertial forces using an acceleration sensor. The last chapter addresses the combination of visual contour-following and force control on compliant workpieces. Under a certain contact force, the workpiece deforms due to its compliance. The position and orientation then is adapted to this deformed contour by visual control -- hence, there is a coupling between force and visual control. This coupling is solved by compensating for workpiece deformation using a dynamic model of the environment. The environmental parameters for compensation are identified online.

This thesis studies the possibility to replace the global navigation satellite system (GNSS) with a phased array radio system (PARS) for positioning and navigation of an unmanned aerial vehicle (UAV). With the increase of UAVs in both civilian and military applications, the need for a robust and accurate navigation solution has increased. The GNSS is the main solution of today for UAV navigation and positioning. However, the GNSS can be disturbed by malicious sources, the signal can either be blocked by jamming or modified to give the wrong position by spoofing. Studies have been conducted to replace or support the GNSS measurements with other drift free measurements, e.g. camera or radar systems. The position measurements from PARS alone is shown not to provide sufficient quality for the application in mind. The PARS measurements are affected by noise and outliers. Reflections from the ground makes the PARS elevation measurements unusable for this application. A root mean square error (RMSE) accuracy of 10 m for a shorter flight and 198 m for a longer flight are achieved in the horizontal plane. The decrease in accuracy for the longer flight is assumed to come from a range bias that increases with distance due to the flat earth approximation used as the navigation frame. Positioning based on PARS aided with a filter and other GNSS independent sensors is shown to reduce the noise and remove the outliers. Five filters are derived and evaluated: a constant velocity extended Kalman filter (EKF), an inertial measurement unit (IMU) aided EKF, an IMU and barometer aided EKF, a converted measurements Kalman filter (CMKF) and a stationary Kalman filter (KF). The IMU and barometer aided EKF performed the best results with a RMSE of 8 m for a shorter flight and 106 m for a longer flight. The noise is significantly reduced compared to the standalone PARS measurements. The conclusion is that PARS can be used as a redundancy system with the IMU and barometer aided EKF. If the EKF algorithm is too computational demanding, the simpler stationary KF can be motivated since the accuracy is similar to the EKF. The GNSS solution should still be used as the primary navigation solution as it is more accurate.

This thesis project considers the potential use of inertial navigation on a consumer grade tablet mounted in a vehicle in an underground mine. The goal is to identify which sensors and techniques are useful and to design a navigation algorithm based on those results. The navigation algorithm is intended to work alongside the current received signal strength indication (RSSI) positioning system. Testing of the gyroscope, accelerometer and magnetometer sensors suggest that, while dead reckoning is likely not precise enough, an orientation filter can be designed that can be used for navigation. A complementary orientation filter using the gyroscope and accelerometer is then designed that shows better results than the default sensor fusion solutions available in Android. The filter is expandable and can come to include magnetometer data in the future. Based on the outputs of this filter, a navigation algorithm based onangle matching with map information is proposed. Precise positioning in an underground mine can be crucial to employee safety, and may also bring production benefits.