This project will create a navigation system based on dead reckoning using anaccelerometer and a magnetometer. There have previously been several studiesmade on navigation with accelerometers, magnetometers (electronic compass) andgyros. With these three components it is possible to do positioning and differentkinds of movement analyses. There are several methods for detection of movementand calculation of position. To achieve greater accuracy in these applications,gyros are often used. Compared to magnetometers and accelerometer gyrosconsumes a lot of power. In an embedded system with limited power suppliesfrom a battery this may be unacceptable. In this project a positioning system without a gyro have been developedand evaluated. Is this possible to do, and what accuracy is possible to achieve arequestions asked.Algorithms have been developed and tested in MATLAB. The project is based ona device called a BeeBadge, part of the project will be to transfer the developedalgorithms from MATLAB to C-code. Optimizations of the code will be performeddue to the constraints in the memory and speed of the microcontroller on theBeeBadge.

An indirect tire pressure monitoring system uses the wheel rolling radius as an indicator of low tire pressure. When extra load is put in the trunk of a car, the load distribution in the car will change. This will affect the rolling radius which in its turn will be confused with a change in the tire pressure. To avoid this phenomenon, the load distribution has to be estimated. In this thesis methods for estimating the pitch angle of a car and an offset in the pitch angle caused by changed load distribution are presented and when an estimate is derived, a load distribution can be derived. Alot of available signals are used but the most important are the longitudinal accelerometer signal and the acceleration at the wheels derived from the velocity of the car. A few ways to detect or compensate for a non-zero road grade are also presented. Based on the estimated offset, a difference between the front and rear axle heights in the vehicle can be estimated and compensating for the changed load distribution in an indirect tire pressure monitoring system will be possible.

This project examines the level of accuracy that can be achieved in precision positioning by using built-in sensors in an Android smartphone. The project is focused in estimating the position of the phone inside a building where the GPS signal is bad or unavailable. The approach is sensor-fusion: by using data from the device’s different sensors, such as accelerometer, gyroscope and wireless adapter, the position is determined. The results show that the technique is promising for future handheld indoor navigation systems that can be used in malls, museums, large office buildings, hospitals, etc.

In this master thesis, control algoritms using arm side sensors are investigated for an industrial robot. The sensors can be position encoders placed after the gearbox and accelerometers on the robot arms. Control strategies are discussed and evaluated for different models of the robot, after which chosen strategies are applied to a realistic model. Control algoritms using arm side sensors (LQ, dual-loop and PD-PID) are compared to a PID-controller that only uses measurements of motor position for feedback control. The comparison are done with respect to disturbance rejection, oscillation damping, robustness and tracking performance of a reference trajectory. Results from tests with the realistic robot modell shows that disturbance rejection was improved a factor 2 to 5 while tracking performance was improved a factor 4 to 5 according to maximum deviation from the reference path. At the same time good re-sults are achieved regarding oscillation damping and robustness.

This master thesis is a study on how a rategyro triad, an accelerometer triad, and a magnetometer triad can be integrated into a navigation system, estimating a vehicle’s attitude, i.e. its roll, tipp, and heading angles. When only a rategyro triad is used to estimate a vehicle’s attitude, a drift in the attitude occurs due to sensor errors. When an accelerometer triad and a magnetometer triad are used, an error in the vehicle’s heading, appearing as a sine curve, depending on the heading, occurs. By integrating these sensor triads, the sensor errors have been estimated with a filter to improve the estimated attitude’s accuracy. To investigate and evaluate the navigation system, a simulation model has been developed in Simulink/Matlab. The implementation has been made using a Kalman filter where the sensor fusion takes place. Simulations for different scenarios have been made and the results from these simulations show that the drift in the vehicle’s attitude is avoided.

PolyTech AB in Malmköping manufactures gyro stabilized camera systems or helicopter applications. In this Master´s Thesis a shaker test rig for vibration testing of these systems is designed, implemented and evaluated. The shaker is required to have an adjustable frequency and displacement and different shakers that meet these requirements are treated in a literature study. The shaker chosen in the test rig is based on a mechanical solution that is described in detail. Additionally all components used in the test rig are described and modelled. The test rig is identified and evaluated from different experiments carried out at PolyTech, where the major part of the identification is based on data collected from accelerometers. The test rig model is used to develop a controller that controls the frequency and the displacement of the shaker. A three-phase motor is used to control the frequency of the shaker and a linear actuator with a servo is used to control the displacement. The servo controller is designed using observer and state feedback techniques. Additionally, the mount in which the camera system is hanging is modelled and identified, where the identification method is based on nonlinear least squares (NLS) curve fitting technique.

This thesis work designs and implements a wearable system to recognize physical activities and detect fall in real time. Recognizing peoples physical activity has a broad range of applications. These include helping people maintaining their energy balance by developing health assessment and intervention tools, investigating the links between common diseases and levels of physical activity, and providing feedback to motivate individuals to exercise. In addition, fall detection has become a hot research topic due to the increasing population over 65 throughout the world, as well as the serious effects and problems caused by fall.<p> In this work, the Sun SPOT wireless sensor system is used as the hardware platform to recognize physical activity and detect fall. The sensors with tri-axis accelerometers are used to collect acceleration data, which are further processed and extracted with useful information. The evaluation results from various algorithms indicate that Naive Bayes algorithm works better than other popular algorithms both in accuracy and implementation in this particular application.<p> This wearable system works in two modes: indoor and outdoor, depending on users demand. Naive Bayes classifier is successfully implemented in the Sun SPOT sensor. The results of evaluating sampling rate denote that 20 Hz is an optimal sampling frequency in this application. If only one sensor is available to recognize physical activity, the best location is attaching it to the thigh. If two sensors are available, the combination at the left thigh and the right thigh is the best option, 90.52% overall accuracy in the experiment.<p> For fall detection, a master sensor is attached to the chest, and a slave sensor is attached to the thigh to collect acceleration data. The results show that all falls are successfully detected. Forward, backward, leftward and rightward falls have been distinguished from standing and walking using the fall detection algorithm. Normal physical activities are not misclassified as fall, and there is no false alarm in fall detection while the user is wearing the system in daily life.

Inertial Measurement Units, the main component of a navigation system, are used in several systems today. IMU&rsquo / s main components, gyroscopes and accelerometers, can be produced at a lower cost and higher quantity. Together with the decrease in the production cost of sensors it is observed that the performances of these sensors are getting worse. In order to improve the performance of an IMU, the error compensation algorithms came into question and several algorithms have been designed. Inertial sensors contain two main types of errors which are deterministic errors like scale factor, bias, misalignment and stochastic errors such as bias instability and scale factor instability. Deterministic errors are the main part of error compensation algorithms. This thesis study explains the methodology of how the deterministic errors are defined by 27 state static and 60 state dynamic rate table calibration test data and how those errors are used in the error compensation model. In addition, the stochastic error parameters, gyroscope and bias instability, are also modeled with Gauss Markov Model and instant sensor bias instability values are estimated by Kalman Filter algorithm. Therefore, accelerometer and gyroscope bias instability can be compensated in real time. In conclusion, this thesis study explores how the IMU performance is improved by compensating the deterministic end stochastic errors. The simulation results are supported by a real IMU test data.

Article No.27

<p>In order to detect movements and vibrations on different appliance, especially robots and self-going devices are used different sensors. One of the most used movement sensors are accelerometers. They are three different types of accelerometers one-axes, two-axes and three-axes. They can be analogue or digital. Husqvarna AB uses an accelerometer to detect movements of their self-going lawnmower Automower. Since it begins to turn up others sensors on the market and the sensor which today uses in Husqvarna’s lawnmower don’t fulfils all functions there is a need to find other solutions. What sensors can be found on a market and which new has come? How can they be used best and which difference is between them? How can they use to detect movements in x, y and z direction and how can they read different movements to detect collision, inclination and lift? Sensor analysis will be done to find out how they can be used in this matter and an big analysis will be done to find out how a sensor witch already is in a lawnmower can be use on the better ways?</p> / <p>För att kunna detektera rörelse och vibrationer på olika anordningar och framförallt robotar och självgående maskiner används olika sensorer. En av de mest använda rörelsesensorer är accelerometer. Det finns en uppsjö av olika utföringar som kan vara med en axel, två axlar eller tre axlar. Accelerometrar finns som digitala eller analoga. Husqvarna AB använder en sådan sensor för att detektera rörelse på sin självgående gräsklippare Automower. Eftersom det börjar dyka upp andra sensorer på marknaden och den sensor som används idag uppfyller inte alla funktioner finns det behov av att lägga tid på att se vad kan åstadkommas med en sådan sensor. Vilka sensorer kan hittas på marknaden och vilka nya har dykt upp? Hur kan de användas på bästa sätt och vad skiljer de åt? Hur kan de användas för att detektera rörelse i x, y, z-led och hur kan de läsa av olika rörelse för att detektera krock, lutning och lyft? Det skall göras analys för att se hur olika sensorer reagerar och hur kan de användas på bästa sätt. Det skall utforskas hur den sensor som redan sitter kan användas på ett bättre sätt.</p>