Multi-agent Systems with Compasses

This paper investigates agreement protocols over cooperative and cooperative--antagonistic multi-agent networks with coupled continuous-time nonlinear dynamics. To guarantee convergence for such systems, it is common in the literature to assume that the vector field of each agent is pointing inside the convex hull formed by the states of the agent and its neighbors, given that the relative states between each agent and its neighbors are available. This convexity condition is relaxed in this paper, as we show that it is enough that the vector field belongs to a strict tangent cone based on a local supporting hyperrectangle. The new condition has the natural physical interpretation of requiring shared reference directions in addition to the available local relative states. Such shared reference directions can be further interpreted as if each agent holds a magnetic compass indicating the orientations of a global frame. It is proven that the cooperative multi-agent system achieves exponential state agreement if and only if the time-varying interaction graph is uniformly jointly quasi-strongly connected. Cooperative--antagonistic multi-agent systems are also considered. For these systems, the relation has a negative sign for arcs corresponding to antagonistic interactions. State agreement may not be achieved, but instead it is shown that all the agents' states asymptotically converge, and their limits agree componentwise in absolute values if and in general only if the time-varying interaction graph is uniformly jointly strongly connected.Comment: SIAM Journal on Control and Optimization, In pres

Angular positioning of a door or window - using a MEMS accelerometer and a magnetometer

The accurate and reliable detection of opening of doors and windows is vital for home security applications. This master thesis aims to present a way to achieve this using a low-cost and low-power ecompass, containing a MEMS accelerometer and a magnetometer. This has been achieved by attaching such a device to a door and collecting sensor data when opening and closing the door. Said data were then analysed in the Matlab environment to study the impact of different methods found in literature to correct for errors in measurements. These include Zero Velocity Compensation for the accelerometer values and hard- and soft-iron compensation for the magnetometer. Thereafter the angle of opening has been calculated, using corrected measurement values. The finished algorithm has also been adapted for implementation on a Cortex-M4 CPU as this, or a similar processor, is likely what is available to use with the e-compass in a real world application. This also motivates the adjustment of the algorithm to use less memory. Finally said implementation has been performed. The results show that it is possible to correct for most of the errors of the accelerometer, but the errors that are left will still propagate to the angular calculations, causing the angle to drift. This can be compensated for by using the angle calculated from magnetometer measurements. The correction of effects affecting the magnetometer is also mostly successful. Likewise the implementation of the algorithm on the processor shows promising results. However, to generalise the algorithm to work on all kinds of doors, as opposed to only the doors it has been developed on, further studies are required.Detection of breaking and entering using an eCompass To stop breaking and entering a new way of detecting the opening of doors or windows has been developed in collaboration with Verisure Innovation AB. The method is based on sensor data from a device called an eCompass, made up of an accelerometer and a magnetometer. In home security it is important to determine if a door or window is open or closed, as an opening when the alarm is activated might indicate an attempted burglary. Some sort of sensor to be placed on the door or window for this detection is required. Today a magnetic contact is often used. However, to provide options that could increase power efficiency, decrease cost or simplify the installation, an alternative component has been investigated. Since it is a consumer product, placed on doors and windows, it will need to be wireless; a component requiring chords everywhere would not be popular. This means that it needs to be battery-powered. To limit the amount of service, the device should still have a life-span of several years. Therefore it simply needs to be very low-power. Recent development of the accelerometers on the market provides just such a sensor; low power and sensitive enough to react to movement of the door or window. Not to forget, it is also affordable. An accelerometer is a component measuring acceleration acting on it. When a door is opened the outer edges of the door will move in a demi-circle. By placing the accelerometer close to the outer edge of the door, the acceleration when opening the door can be measured. From the acceleration it is possible to calculate an angle of the door in relation to the starting position. These calculations are subjected to certain problems: Firstly, errors will cause the calculated angles to drift. Secondly, the noise levels of the accelerometer will hide very low accelerations. During a burglary the burglar might try to escape notice by opening the door utterly slowly. Therefore some kind of verification of the angle is needed to counteract the drift and detect slow openings. One good option for this is to include a magnetometer. This is a component that measures magnetic fields. Combined with an accelerometer it is often referred to as an eCompass. As the name implies, it measures the compass heading, that is the angle relative to north. When attached to the door this angle will change as the door turns during opening. This can be related to the angle relative to the closed door by simply subtracting the compass heading of the door when closed. Then the resulting angle of the closed door is 0 degrees. This method has been tested and works in real life on doors, showing the angle of opening. By setting a limit of around 2 degrees for the "open" state of the door (to combat false negatives due to errors, while still having too small an angle for anyone to pass through) the "open" or "closed" state of the door can be determined. The inclusion of another component for the algorithm will cause the power consumption to increase, especially since the magnetometer has around 10 times higher power consumption than the accelerometer. To counteract this one could decrease the sampling rate of foremost the magnetometer. Since the accelerometer is very good at detecting motion, the main point of the magnetometer is to detect slow opening of doors. For that purpose it does not need to be sampled very often. It should therefore be possible to optimize the algorithm so that the magnetometer only detects openings too slow for the accelerometer, while the latter detects the fast openings. To sum up, it is possible to use the combination of an accelerometer and a magnetometer to determine the opening angle of a door or window. The power consumption can also be controlled by optimizing the algorithm

Magnetic Field Aided Indoor Navigation

This research effort examines inertial navigation system aiding using magnetic field intensity data and a Kalman filter in an indoor environment. Many current aiding methods do not work well in an indoor environment, like aiding using the Global Positioning System. The method presented in this research uses magnetic field intensity data from a three-axis magnetometer in order to estimate position using a maximum – likelihood approach. The position measurements are then combined with a motion model using a Kalman filter. The magnetic field navigation algorithm is tested using a combination of simulated and real measurements. These tests are conducted using a magnetic field intensity map of the entire test environment. The result of these tests show that the position aiding algorithm is capable of generating positon estimates from real data within less than 1 meter of the true trajectory, with most estimates .3 meters away from the true trajectory in a laboratory hallway environment. To further explore the capabilities of the position aiding algorithm, a leader-follower scenario is implemented. In this scenario, the follower uses magnetic field intensity data collected by the leader to estimate its current position and attempt to follow the leader’s trajectory. The results show that tracking is possible, and that the measurement span of the leader has a large impact on the result

Navegação de um robô móvel por processamento de imagem

Trabalho final de Mestrado para obtenção do grau de Mestre em Engenharia Eletrotécnica – Ramo de Automação e Eletrónica IndustrialA robótica móvel é um campo científico notável, que pretende automatizar a movimentação de um mecanismo com uma designada função, para que este tenha a habilidade de realizar as suas tarefas sem permanecer numa posição fixa. Para este efeito, utiliza uma variedade de recursos para identificar o seu ambiente durante o deslocamento. Um dos métodos mais significativos, para o auxílio do reconhecimento do espaço envolvente, é o processamento de imagem. Esta dissertação visa o desenvolvimento de um robô móvel, composto por uma câmera, numa posição alta, com o intuito de realizar a aquisição da imagem, e sensores para evitar obstáculos. O utilizador interage com o robô móvel através do número de dedos da mão. Assim, o algoritmo de processamento de imagem desenvolvido permite controlar a movimentação do robô. O robô móvel foi estruturado com uma perspetiva simples e de baixo custo, mas que também conseguisse desempenhar o seu papel eficientemente. Para realizar o controlo do mesmo é utilizado um Raspberry Pi model 3B+. O foco principal deste projeto está presente no algoritmo de processamento de imagem, desenvolvido em python, com auxílio da biblioteca OpenCV. Empregando métodos de extração de características e de obtenção de contornos, foi possível identificar a mão do utilizador e o número de dedos apresentados. Este programa permite assim, controlar o robô móvel como um tipo de comando, em que consoante o número de dedos exibidos irá executar um diferente tipo de movimento.Mobile robotics is a remarkable scientific field, that aims to automate the movement of a mechanism with a designated function, so that it has the ability to perform its tasks without remaining in a fixed position. For this purpose, it utilizes a variety of features to identify its environment while moving. One of the most significant methods for assisting in the recognition of the surrounding space, is image processing. This dissertation aims to develop a mobile robot, composed of a camera in an elevated position, in order to perform image acquisition, and sensors to avoid obstacles. The user interacts with the mobile robot through the number of fingers on the hand. Thus, the developed image processing algorithm allows control of the robot's movement. The mobile robot was structured with a low-cost perspective and to be relatively simple, but also able to perform its role efficiently. A Raspberry Pi model 3B+ is used to control it. The main focus of this project is present in the image processing algorithm, developed in python, with the aid of the OpenCV library. By employing methods of feature and contour extraction, it was possible to identify the user's hand and the number of fingers displayed. From this program, it is possible to control the mobile robot as a type of remote, where depending on the number of fingers displayed it will perform a different movement function.N/

Diseño e implementación de rutinas de navegación y comunicación para un robot móvil utilizado en sistemas multirobot de enjambre

Proyecto de Graduación (Licenciatura en Ingeniería Mecatrónica) Instituto Tecnológico de Costa Rica. Área Académica de Ingeniería Mecatrónica, 2019Se presenta el diseño e implementación de rutinas de navegación y comunicación para un robot móvil utilizado en sistemas multirobot de tipo enjambre para la digitalización de escenarios estáticos. Este trabajo forma parte del proyecto de investigación PROE E1F2, ejecutado por la escuela de matemática y el área académica de ingeniería mecatrónica del Instituto Tecnológico de Costa Rica. Para solucionar el problema, se propuso un esquema de control que combina la cinemática directa e inversa del robot móvil, junto a controladores PID y de retroalimentación proporcional. Además, se implementó una unidad de medición inercial con un algoritmo de fusión de sensores para disminuir errores de odometría del tipo deslizamiento. Para el sistema de comunicación se utilizó la técnica acceso múltiple por división de tiempo, para la cual fue necesario un método de sincronización de relojes llamado sincronización de tiempo por medición de retardo. El control automático presentó un error máximo de 2.16% en estado estacionario y con la integración de la unidad de medición inercial se redujo el error en la orientación del robot hasta en 10.70°. El sistema de comunicación logró manejar el flujo de información de cuatro nodos, recibiendo el 99.935% de los mensajes enviados a la base central.It’s shown the design and implementation of navigation and communication routines for a mobile robot used in a multirobot swarm system to digitize static scenarios. This work is part of the research project PROE E1F2, conducted by the mathematics school and the mechatronics engineer area of the Instituto Tecnológico de Costa Rica. To solve the problem, a control scheme using direct and inverse kinematics in combination with a PID controller and proportional feedback controller was proposed. Also, an inertial measurement unit was implemented with a sensor fusion algorithm to diminish odometry errors of the slip type. For the communication system, the time division multiple access technique was used, for which was necessary a method to synchronize clocks called delay measurement time synchronization. The automatic control presented a maximum error of 2.16% in the steady state and with the integration of the inertial measurement unit the robot orientation error was reduced up to 10.70°. The communication system was able to manage the information flow of four nodes, receiving 99.935% of the messages send to the central base

Heading drift mitigation for low-cost inertial pedestrian navigation

The concept of autonomous pedestrian navigation is often adopted for indoor pedestrian navigation. For outdoors, a Global Positioning System (GPS) is often used for navigation by utilizing GPS signals for position computation but indoors, its signals are often unavailable. Therefore, autonomous pedestrian navigation for indoors can be realized with the use of independent sensors, such as low-cost inertial sensors, and these sensors are often known as Inertial Measurement Unit (IMU) where they do not rely on the reception of external information such as GPS signals. Using these sensors, a relative positioning concept from initialized position and attitude is used for navigation. The sensors sense the change in velocity and after integration, it is added to the previous position to obtain the current position. Such low-cost systems, however, are prone to errors that can result in a large position drift. This problem can be minimized by mounting the sensors on the pedestrian’s foot. During walking, the foot is briefly stationary while it is on the ground, sometimes called the zero-velocity period. If a non-zero velocity is then measured by the inertial sensors during this period, it is considered as an error and thus can be corrected. These repeated corrections to the inertial sensor’s velocity measurements can, therefore, be used to control the error growth and minimize the position drift. Nonetheless, it is still inadequate, mainly due to the remaining errors on the inertial sensor’s heading when the velocity corrections are used alone. Apart from the initialization issue, therefore, the heading drift problem still remains in such low-cost systems. In this research, two novel methods are developed and investigated to mitigate the heading drift problem when used with the velocity updates. The first method is termed Cardinal Heading Aided Inertial Navigation (CHAIN), where an algorithm is developed to use building ‘heading’ to aid the heading measurement in the Kalman Filter. The second method is termed the Rotated IMU (RIMU), where the foot-mounted inertial sensor is rotated about a single axis to increase the observability of the sensor’s heading. For the CHAIN, the method proposed has been investigated with real field trials using the low-cost Microstrain 3DM-GX3-25 inertial sensor. It shows a clear improvement in mitigating the heading drift error. It offers significant improvement in navigation accuracy for a long period, allowing autonomous pedestrian navigation for as long as 40 minutes with below 5 meters position error between start and end position. It does not require any extra heading sensors, such as a magnetometer or visual sensors such as a camera nor an extensive position or map database, and thus offers a cost-effective solution. Furthermore, its simplicity makes it feasible for it to be implemented in real-time, as very little computing capability is needed. For the RIMU, the method was tested with Nottingham Geospatial Institute (NGI) inertial data simulation software. Field trials were also undertaken using the same low-cost inertial sensor, mounted on a rotated platform prototype. This method improves the observability of the inertial sensor’s errors, resulting also in a decrease in the heading drift error at the expense of requiring extra components

Heading drift mitigation for low-cost inertial pedestrian navigation

The concept of autonomous pedestrian navigation is often adopted for indoor pedestrian navigation. For outdoors, a Global Positioning System (GPS) is often used for navigation by utilizing GPS signals for position computation but indoors, its signals are often unavailable. Therefore, autonomous pedestrian navigation for indoors can be realized with the use of independent sensors, such as low-cost inertial sensors, and these sensors are often known as Inertial Measurement Unit (IMU) where they do not rely on the reception of external information such as GPS signals. Using these sensors, a relative positioning concept from initialized position and attitude is used for navigation. The sensors sense the change in velocity and after integration, it is added to the previous position to obtain the current position. Such low-cost systems, however, are prone to errors that can result in a large position drift. This problem can be minimized by mounting the sensors on the pedestrian’s foot. During walking, the foot is briefly stationary while it is on the ground, sometimes called the zero-velocity period. If a non-zero velocity is then measured by the inertial sensors during this period, it is considered as an error and thus can be corrected. These repeated corrections to the inertial sensor’s velocity measurements can, therefore, be used to control the error growth and minimize the position drift. Nonetheless, it is still inadequate, mainly due to the remaining errors on the inertial sensor’s heading when the velocity corrections are used alone. Apart from the initialization issue, therefore, the heading drift problem still remains in such low-cost systems. In this research, two novel methods are developed and investigated to mitigate the heading drift problem when used with the velocity updates. The first method is termed Cardinal Heading Aided Inertial Navigation (CHAIN), where an algorithm is developed to use building ‘heading’ to aid the heading measurement in the Kalman Filter. The second method is termed the Rotated IMU (RIMU), where the foot-mounted inertial sensor is rotated about a single axis to increase the observability of the sensor’s heading. For the CHAIN, the method proposed has been investigated with real field trials using the low-cost Microstrain 3DM-GX3-25 inertial sensor. It shows a clear improvement in mitigating the heading drift error. It offers significant improvement in navigation accuracy for a long period, allowing autonomous pedestrian navigation for as long as 40 minutes with below 5 meters position error between start and end position. It does not require any extra heading sensors, such as a magnetometer or visual sensors such as a camera nor an extensive position or map database, and thus offers a cost-effective solution. Furthermore, its simplicity makes it feasible for it to be implemented in real-time, as very little computing capability is needed. For the RIMU, the method was tested with Nottingham Geospatial Institute (NGI) inertial data simulation software. Field trials were also undertaken using the same low-cost inertial sensor, mounted on a rotated platform prototype. This method improves the observability of the inertial sensor’s errors, resulting also in a decrease in the heading drift error at the expense of requiring extra components

Method and Technology for Model-based Test Automation of Context-sensitive Mobile Applications

Smartphone und Tablet Computer haben sich zu universalen Kommunikations- und Unterhaltungsplattformen entwickelt, die durch ständige Verfügbarkeit mobilen Internets die Verwendung mobiler, digitaler Dienste und Anwendungen immer mehr zur Normalität werden lassen und in alle Bereiche des Alltags vordringen. Die digitalen Marktplätze zum Vertrieb von Apps, sogenannten App Stores, sind Blockbuster-Märkte, in denen wenige erfolgreiche Produkte in kurzen Zeitintervallen den Großteil des Gesamtgewinns des Marktes erzielen. Durch dynamische, summative Bewertungssysteme in App Stores wird die Qualität einer App zu einem unmittelbaren Wert- und Aufwandstreiber. Die Qualität einer App steht in direktem Zusammenhang mit der Anzahl Downloads und somit mit dem wirtschaftlichen Erfolg. Mobile Geräte zeichnen sich gegenüber Desktop-Computern vorrangig dadurch aus, dass sie durch Sensoren in der Lage sind, Parameter ihrer Umgebung zu messen und diese Daten für Anwendungsinhalte aufzubereiten. Anwendungsfälle für solche Technologien sind beispielsweise ortsbasierte digitale Dienste, die Verwendung von Standortinformationen für Fahrzeug- oder Fußgängernavigation oder die Verwendung von Sensoren zur Interaktion mit einer Anwendung oder zur grafischen Aufbereitung in Augmented Reality-Anwendungen. Anwendungen, die Parameter ihrer Umgebung messen, aufbereiten und die Steuerung des Kontrollflusses einfließen lassen, werden als kontextsensitive Anwendungen bezeichnet. Kontextsensitivität hat prägenden Einfluss auf die fachliche und technische Gestaltung mobiler Anwendungen. Die fachliche Interpretation von Kontextparametern ist ein nicht-triviales Problem und erfordert eine sorgfältige Implementierung und gründliches Testen. Herausforderungen des Testens kontextsensitiver, mobiler Anwendungen sind Erstellung und Durchführung von Tests, die zum einen die zu testende Anwendung adäquat abdecken und zum anderen Testdaten bereitstellen und reproduzierbar in die zu testende Anwendung einspeisen. In dieser Dissertation wird eine Methode und eine Technologie vorgestellt, die wesentliche Aspekte und Tätigkeiten des Testens durch modellbasierte Automatisierung von menschlicher Arbeitskraft entkoppelt. Es wird eine Methode vorgestellt, die Tests für kontextsensitive Anwendungen aus UML-Aktivitätsdiagrammen generiert, die durch Verwendung eines UML-Profils zur Kontext- und Testmodellierung um Testdaten angereichert werden. Ein Automatisierungswerkzeug unterstützt die Testdurchführung durch reproduzierbare Simulation von Kontextparametern. Durch eine prototypische Implementierung der Generierung von funktionalen Akzeptanztests, der Testautomatisierung und Kontextsimulation wurde Machbarkeit des vorgestellten Ansatzes am Beispiel der mobilen Plattform Android praktisch nachgewiesen.Smartphones and tablet computers have evolved into universal communication and entertainment platforms. With the ubiquitous availability of mobile internet access, digital services and applications have become a commodity that permeates into all aspects of everyday life. The digital marketplaces for mobile app distribution, commonly referred to as App Stores, are blockbuster markets, where few extraordinarily successful apps generate the major share of the market's overall revenue in a short period of time. Through the implementation of dynamic, summative rating mechanisms in App Stores, app quality becomes a key value-driver of app monetarization, as app quality is directly associated with the number of app downloads, and hence with economic success. In contrast to desktop computers, mobile devices are uniquely characterized by a variety of sensors that measure environmental parameters and make them available as input to software. Potential uses of these technologies range from location-based digital services that use the user's location for vehicle or pedestrian navigation to augmented reality applications that use sensor information for user experience enhancement. Apps instrumenting physical and non-physical environmental parameters to control workflows or user interfaces are called context-aware applications. Context-awareness has a formative impact on the functional and technical design of mobile applications. The algorithmic interpretation of context data is a non-trivial problem that makes thorough implementation and careful testing mandatory to ensure adequate application quality. Major challenges of context-aware mobile application testing are test case creation and test execution. The impact of context-awareness on test case creation is the attainability of adequate test coverage, that in contrast to non-context-aware application extends beyond traditional input data. It requires the identification and characterization of context data sources and the provisioning of suitable, reproducible test data. This thesis addresses a method and technology to decouple test case creation and test execution from manual labor through the extensive use of model-driven automation technology. A method is presented that generates test cases for context-aware mobile applications from UML Activity Models by means of model transformation technology. A test execution framework facilitates the reproducible simulation of context data derived from an enriched system model. The approach is validated using a prototypical implementation of the test case generation algorithm. The simulation of context data during test execution ist validated using a modified implementation of the Android operation system