The estimated position of an industrial robot’s end-effector is crucial for its performance. Contemporary methods for doing the estimation is limited in certain aspects, and alternative methods are in high demand. This work builds on a method previously introduced where an Inertial Measurement Unit (IMU) device is combined with the robot’s system via sensor fusion. The IMU must be calibrated before its signal is used in sensor fusion and this work implements and builds on current cutting-edge methods for calibration. Sensor fusion is a crucial part of the method and here a complementary filter is used. The finished estimation is then used with Iterative Learning Control (ILC) to investigate if the accuracy can be further improved and also test its viability. Results from ILC show that the IMU can indeed be used to estimate the end-effector's trajectory but that sensor fusion is mandatory. Further research could be done to allow the estimation to be done online instead of offline and ILC could be tested more extensively.

An IMU is a sensor with many differing use cases, it makes use of an accelerometer, gyroscope and sometimes a magnetometer. One of the biggest problems with IMU sensors is the effect vibrations can have on their data. The reason for this study is to find a solution to this problem by filtering the data. The tests for this study were conducted in cooperation with Husqvarna using two of their automowers. The tests were made by running the automowers across different surfaces and recording the IMU data. To find filters for the IMU data a comprehensive literature survey was conducted to find suitable methods to filter out vibrations. The two filters selected for further testing were the complementary filter and the LMS filter. When the tests had been run all the data was added to data sheets where it could be analyzed and have the filters added to the data. From the gathered data the data spikes were clearly visible and were more than enough to trigger the mower's emergency stop and need to be manually reset. The vibrations were too irregular to filter using the LMS filter since it requires a known signal to filter against. Hence only the complementary filter was implemented fully. With the complementary filter these vibrations can be minimized and brought well below the level required to trigger an emergency stop. With a high filter weight constant such as 0.98, the margin of error from vibrations can be brought down to +- 1 degrees as the lowest and +- 4,6 degrees as highest depending on the surface and automower under testing. The main advantage with using the complementary filter is that it only requires one weight constant to adjust the filter intensity making it easy to use. The one disadvantage is that the higher the weight constant is the more delay there is on the data.

Monte Carlo Localization (MCL) is an algorithm that allows a robot to determine its location when provided a map of its surroundings. Particles, consisting of a location and an orientation, represent possible positions where the robot could be on the map. The probability of the robot being at each particle is calculated based on sensor input. Traditionally, MCL only utilizes the position of objects for localization. This thesis explores using wall and floor surface textures to help the algorithm determine locations more accurately. Wall textures are captured by using a laser range finder to detect patterns in the surface. Floor textures are determined by using an inertial measurement unit (IMU) to capture acceleration vectors which represent the roughness of the floor. Captured texture data is classified by an artificial neural network and used in probability calculations. The best variations of Texture MCL improved accuracy by 19.1\% and 25.1\% when all particles and the top fifty particles respectively were used to calculate the robot's estimated position. All implementations achieved comparable performance speeds when run in real-time on-board a robot.

Journeying into the information era the need for new technologies used forsending vast amounts of data eciently has risen, as has the possibilities and needfor dierent types of AI-controlled robots. In this project a RC-car was modiedand equipped with a Raspberry PI and laser radar to let it automatically navigatearound a room using UWB transmitters and receivers. A framework for roboticapplications called ROS, Robot Operating System, was used with a large numberof open source packages to ll dierent functions. Custom scripts was created totie everything together, allowing all dierent components in the system to work inunison.

Las plataformas de georreferenciación de sensores, ya sean aerotransportadas, formando parte de los conocidos vehículos no tripulados (UAV), navales o terrestres, están teniendo mucho interés en los últimos años. La utilización de las técnicas de georreferenciación directa permite reducir los tiempos y optimizar la generación de cartografía y datos de distinta naturaleza espacial. El objetivo de esta Tesis es el desarrollo e implementación de un programa informático capaz de integrar tanto la calibración de los sensores ópticos del sistema fotogramétrico multicámara portátil GNSS/INS, como establecer las relaciones geométricas entre los distintos sensores (ópticos y no ópticos) que conforman el soporte multicámara mediante constreñimientos geométricos. Adicionalmente, se pretende estudiar los distintos comportamientos del sistema multicámara portátil GNSS/INS bajo las condiciones geométricas impuestas, variando el número de puntos de apoyo necesarios para alcanzar una orientación absoluta de máxima calidad y fiabilidad en la orientación espacial de los sensores. El método de ajuste de haces de rayos de un bloque de imágenes se utiliza en la calibración de los sensores que integran el sistema fotogramétrico multicámara portátil GNSS/INS. La georreferenciación mixta con puntos de apoyo dotados de coordenadas UTM está también implementada en el programa. El programa captura las coordenadas de la antena GNSS y la orientación de la IMU en el momento de toma de la imagen, generando lo que se conoce como georrefenciación directa de los sensores ópticos mediante un sistema no acoplado / Navarro Tarin, S. (2014). Orientación espacial de múltiples sensores integrados con constreñimientos geométricos [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37883 / Alfresco

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Free surface water entry of various objects has been studied using high-speed images and image processing techniques for decades. This thesis studies the forces, velocities, and trajectories of slender axisymmetric projectiles using an embedded inertial measurement unit (IMU). Three nose shapes (cone, ogive, and flat) were used in the study. Additionally, the projectiles were tested at vertical and oblique impact angles with different surface conditions. One-half of each projectile was coated down the centerline with a hydrophobic spray, creating a half hydrophobic, half hydrophilic case. The trajectory of this half-and-half case impacting vertically was compared to the trajectory of symmetrically coated projectiles impacting the free surface at oblique angles. The oblique impact cases showed significantly more final lateral displacement than the half-and-half case over the same depth. The amount of lateral displacement was also affected by the nose shape, with the cone nose shape achieving the largest lateral displacement for the oblique entry case. Instantaneous lift and drag coefficients were calculated using data from the IMU for the vertical, half-and-half, and oblique entry cases. Impact forces were calculated for each nose shape and the flat nose shape experienced impulsive forces between 25 N and 37 N when impacting vertically. The impact force for the flat nose decreased for the oblique entry case. Acoustic spectrograms showed that the sound produced during the water entry event predominately arises from the pinch-off for the cone and ogive nose shapes, with additional sound production from impact for the flat nose shape.

Over the last five years I have been investigating the phenomenon of movement sonification and different types of interaction ongoing within the experience. I have expanded my knowledge and skills in the fields of music, musical performance, dance, music theory, music technology, acoustics, physics, as well as cognitive and computer sciences.  This paper introduces the reader to an extraordinarily broad subject of movement sonification, namely how to create, participate in and understand the phenomenon, with special emphasis on the human perception and proprioception system. Particular attention is also paid to technological issues, presented and described using the example of IMU technology.  The thesis presents also my point of view on direct and indirect interaction through examples of my previous compositions, as well as works created by other artists. The two last chapters are devoted to practical aspects – experiences and observations made from different perspectives – a composer’s, creator’s, performer’s and spectator’s. Based on my latest interactive work Hypercycle I discuss my way of creating an interactive situation, the process of using all possible elements to make the work engaging, yet not overwhelming or overstimulating. / <p>The documentation also includes the following recordings: Hypercycle (2021) – documentation – visual instructions.mp4; Hypercycle (2021) – documentation – binaural.mp4; Hypercycle (2021) - exhibition (frag.).mov</p>

Specific rehabilitation is a key period for a lower-limb amputee patient. While learning how to walk with a prosthesis, the patient needs to avoid any gait compensations that may lead to future comorbidities. To reach a gait pattern close to the one of a healthy person, objective data may be of great help to complement the experience of the clinician team. By using 6 IMUs located on the feet, shanks and thighs accompanied by 3 accelerometers on the pelvis, sternum and head, data could be recorded during walking exercises of 7 rehabilitation sessions of a patient. To compute the absolute symmetry index of the stance phase duration and the stride duration all over the instrumented sessions, the gait events defining the transitions between gait phases were determined thanks to several algorithms. By first comparing the error obtained in the calculation of the stance phase duration with all tested algorithms as compared to the data from pressure insoles considered as a reference system, the algorithm developed by Trojaniello and collaborators [1] was found to be the most adapted to this situation. Using this algorithm on the data from all sessions highlighted the possibility to detect changes in the symmetry of stance phase duration and stride duration, that are relative to the gait quality. This means that IMUs seem to be able to monitor the progress of a patient during his rehabilitation. Hence, IMUs have proven themselves to be a system of great interest in the analysis of the gait pattern of a lower-limb amputee patient in rehabilitation, by allowing for an embedded measurement of much more parameters than the pressure insoles, whose calibration constituted a real limitation.