TITLE:  ”Fuck society” SUB-HEADING: Mr. Robot som samtida dystopi. AUTHOR: Petra Stråle. EXAMINER: Johan Nilsson. LEVEL: BA Thesis.SUBJECT: Media and communication science. INSTITUTE: Department of Humanities, Education and Social Sciences University of Örebro.PURPOSE: The thesis purpose is to examine how the television series Mr. Robot can be viewed as an dystopian work of fiction that depicts our present society. METHOD: Content analysis. RESULT: The results indicates that the television series Mr. Robot can not be categorized as a classical dystopia, but does, however, contain dystopian elements and is connected to different forms of dystopia, like the critical dystopia and the proto-dystopia.

Laser welding is used in modern industrial production due to its high welding speed and good welding performance comparing to more traditional arc welding. To improve the flex-ibility, robots can be used to mount the laser tool. However, laser welding has a high require-ment for the accuracy in positioning the laser tool. There are three main related variables which affect the laser welding accuracy: robot path accuracy, workpiece geometry and fixture repeatability. Thus, joint tracking is very important for laser welding to achieve high quality welds. There are many joint tracking systems which were proposed in recent years. After receiv-ing the joint information, a control system is necessary to control the robot motion in real-time. The open control system for the industrial robot is one trend for the future. A lot of methods and systems are proposed to control the robot motion. Some systems can achieve a high accuracy in the experiments. However, it is still hard to apply them in practical indus-trial production. Thus more commercial solutions appear to overcome the robot motion problem nowadays. They are very useful to realize practical applications. ABB EGM path correction module, a new function of Robotware, is one of the com-mercial solutions for robot motion control in real time. In the experiments presented in this work, a computer is used to simulate a sensor to create a path correction signal. To test its feasibility for the laser welding application, many experiments are conducted. One was to test the robot path repeatability when there is no correction message sent to the robot. Another was to test the level of accuracy EGM can achieve during the correction process. Different types of paths and three different speeds were separately carried out. The results showed that it is possible to use the EGM in the laser welding application. In the EGM feasibility test, there exists deviation in the z-direction. Since these deviations are less than 0.2mm, it will have a minor influence the laser welding performance, implying that the EGM path correction can be applied in practical production.

La terminología “Robótica” es utilizada por primera vez por el escritor Isaac Asimov en 1942, en su cuento titulado como “Runaround”, cuando definió esta palabra como “el estudio sistemático de robots, su construcción, mantenimiento y comportamiento”. Desde ese entonces se ha internacionalizado este concepto, siendo esta rama de la ciencia la que se ocupa de todo el desarrollo respectivo al tema de los robots. Después de la aparición del nombre por primera vez en 1942, sucedieron un sinnúmero de hitos que sirvieron para el desarrollo de la robótica. A continuación mencionamos algunos hechos en orden cronológico para tener una mejor perspectiva del desarrollo de la robótica. En 1948 se publica el libro llamado Cybernetics, por Norbert Wiener, en el cual, por primera vez se incluyen conceptos para la operación de robots. En 1954 se patenta, por primera vez en la historia, el diseño de un robot y fue por George C. Devol en Estados Unidos. Esto sirve como ejemplo y es seguido por otras personas en todo el mundo como es el caso de C.W. Kenward del Reino Unido. En 1961 vuelve a aparecer George C. Devol quien con la ayuda de J.F. ENGELBERG, construyen el primer robot industrial denominado “Unimate-2000”. Al año siguiente, este robot se instala en las fábricas de General Motors. Por esta razón a estos dos investigadores se les considera como padres de la robótica industrial.

Volvo is continually updating and replacing their equipment and want to investigate the possibility to shorten the time it takes to implement changes and shorten the time in commissioning projects. The use of an emulation model of a production cell can shorten the commissioning time since the equipment and sequence of the cell can be thoroughly tested before implementation. Volvo also wants to investigate the possibility to validate equipment using emulation. The main objectives are to find an emulation software that suits Volvo’s needs and build an emulation model of an actual production cell at Volvo called G750. A literature review was performed in which the authors gained knowledge about virtual commissioning, simulation and emulation and the usage of these. A market survey was conducted in order to find emulation software that could handle Volvo’s complex production equipment consisting of ABB robots and Siemens PLC. A method for building emulation models of existing production equipment was found during the literature review. The software used to build the emulation model was Simumatik3D. Other software used to make the model as realistic as possible includes RobotStudio, WinCC and PLCSIM. The emulation model handles approximately 350 inputs and outputs. When the emulation model was finished experiments were conducted in order to answer research questions and to reach the main objectives. The experiments validate that the emulation model is representative of the real production cell regarding programming, fail scenarios and movement.

Visual navigation is a ubiquitous yet complex task which is performed by many species for the purpose of survival. Although visual navigation is actively being studied within the robotics community, the determination of elemental constituents of a robust visual navigation system remains a challenge. Motion estimation is mistakenly considered as the sole ingredient to make a robust autonomous visual navigation system and therefore efforts are made to improve the accuracy of motion estimations. On the contrary, there are other factors which are as important as motion and whose absence could result in inability to perform seamless visual navigation such as the one exhibited by humans. Therefore, it is needed that a general model for a visual navigation system be devised which would describe it in terms of a set of elemental units. In this regard, a set of visual navigation elements (i.e. spatial memory, motion memory, scene geometry, context and scene semantics) are suggested as building blocks of a visual navigation system in this thesis. A set of methods are proposed which investigate the existence and role of visual navigation elements in a visual navigation system. A quantitative research methodology in the form of a series of systematic experiments is conducted on these methods. The thesis formulates, implements and analyzes the proposed methods in the context of visual navigation elements which are arranged into three major groupings; a) Spatial memory b) Motion Memory c) Manhattan, context and scene semantics. The investigations are carried out on multiple image datasets obtained by robot mounted cameras (2D/3D) moving in different environments. Spatial memory is investigated by evaluation of proposed place recognition methods. The recognized places and inter-place associations are then used to represent a visited set of places in the form of a topological map. Such a representation of places and their spatial associations models the concept of spatial memory. It resembles the humans’ ability of place representation and mapping for large environments (e.g. cities). Motion memory in a visual navigation system is analyzed by a thorough investigation of various motion estimation methods. This leads to proposals of direct motion estimation methods which compute accurate motion estimates by basing the estimation process on dominant surfaces. In everyday world, planar surfaces, especially the ground planes, are ubiquitous. Therefore, motion models are built upon this constraint. Manhattan structure provides geometrical cues which are helpful in solving navigation problems. There are some unique geometric primitives (e.g. planes) which make up an indoor environment. Therefore, a plane detection method is proposed as a result of investigations performed on scene structure. The method uses supervised learning to successfully classify the segmented clusters in 3D point-cloud datasets. In addition to geometry, the context of a scene also plays an important role in robustness of a visual navigation system. The context in which navigation is being performed imposes a set of constraints on objects and sections of the scene. The enforcement of such constraints enables the observer to robustly segment the scene and to classify various objects in the scene. A contextually aware scene segmentation method is proposed which classifies the image of a scene into a set of geometric classes. The geometric classes are sufficient for most of the navigation tasks. However, in order to facilitate the cognitive visual decision making process, the scene ought to be semantically segmented. The semantic of indoor scenes as well as semantic of the outdoor scenes are dealt with separately and separate methods are proposed for visual mapping of environments belonging to each type. An indoor scene consists of a corridor structure which is modeled as a cubic space in order to build a map of the environment. A “flash-n-extend” strategy is proposed which is responsible for controlling the map update frequency. The semantics of the outdoor scenes is also investigated and a scene classification method is proposed. The method employs a Markov Random Field (MRF) based classification framework which generates a set of semantic maps.

Huge natural disaster events can be so devastating that they often overwhelm human rescuers and yet, they seem to occur more often. The TRADR (Long-Term Human-Robot Teaming for Robot Assisted Disaster Response) research project aims at developing methodology for heterogeneous teams composed of human rescuers as well as ground and aerial robots. While the robots swarm the disaster sites, equipped with advanced sensors, they collect a huge amount row-data that cannot be processed efficiently by humans. Therefore, in the frame of the here presented work, a semantic interpreter has been developed that crawls through the raw data, using state of the art object detection algorithms to identify victim targets and extracts all kinds of information that is relevant for rescuers to plan their missions. Subsequently, this information is restructured by a reasoning process and then stored into a high-level database that can be queried accordingly and ensures data constancy.

Memoria para optar al título de Ingeniero Civil Eléctrico / Memoria para optar al título de Ingeniero Civil en Computación / El objetivo de este trabajo de título es el diseño e implementación de un sistema de memoria episódica de largo plazo para robots de servicio domésticos. Además, el sistema debe considerar componentes emocionales y ser integrado en el software del robot Bender, perteneciente al Laboratorio de Robótica del Departamento de Ingeniería Eléctrica de la Universidad de Chile. Un sistema de estas características es esencial para mejorar el desempeño de un robot doméstico, especialmente en el ámbito de la interacción humano-robot. Sin embargo, tras la revisión del estado del arte, se encuentran pocos trabajos relacionados y que aún no existe un consenso en el tema. El sistema es diseñado para cumplir con un conjunto de requerimientos episódicos mínimos para este tipo de memorias. Además, el diseño es agnóstico del robot objetivo, permitiendo la definición de estructuras de datos genéricas para la representación episódica, las que son manejadas por un sistema de plugins. De esta forma, el sistema puede ser integrado en otras plataformas robóticas basadas en Robot Operating System (ROS). El sistema implementado es capaz de recolectar episodios automáticamente desde las má- quinas de estado que definen el comportamiento del robot. Utiliza la base de datos MongoDB para el almacenamiento de episodios y está programado en C ++ y Python, solo utilizando paquetes estándar en ROS. Además, el sistema provee una API ROS capaz de responder consultas sobre estos episodios, las que permiten realizar búsquedas mediante combinaciones de condiciones lógicas. La implementación es evaluada de manera cuantitativa, mediante experimentos de escala- bilidad y eficiencia. Los resultados indican que el sistema se adapta al caso de uso esperado para el robot Bender. Sin embargo, la formulación de consultas al sistema tiene un alto im- pacto en su desempeño. Por esto, es importante seleccionar adecuadamente las operaciones, para que el uso de recursos no comprometa la interacción humano-robot. Desde otro enfoque, la integración del sistema LTM en Bender es validada mediante sesiones de demostración, donde el robot genera memorias sobre los humanos con quien interactúa. Se concluye que el sistema cumple con la mayoría de los requerimientos establecidos, teniendo que acotar el proyecto para dejar algunos aspectos como trabajo futuro. Particular- mente, queda propuesta la implementación de un sistema emocional para el robot.

Simultaneous localization and mapping (SLAM) is a challenging and vital problem in robotics. It is important in tasks such as disaster response, deep-sea and cave exploration, in which robots must construct a map of an unknown terrain, and at the same time localize themselves within the map. The issue with single- robot SLAM is the relatively high rate of failure in a realistic application, as well as the time and energy cost. In this work, we propose a new approach to decentralized multi-robot SLAM which uses a robot swarm to map the environment. This system is capable of mapping an environment without human assistance and without the need for any additional infrastructure. We assume that 1) no robot possesses sufficient memory to store the entire map of the environment, 2) the communication range of the robots is limited, and 3)there is no infrastructure present in the environment to assist the robot in communicating with others. To cope with these limitations, the swarm system is designed to work as an independent entity. The swarm can deploy new robots towards the region that is yet to be explored, coordinate the communication between the robots by using itself as the communication network and replace any malfunctioning robots. The proposed method proves to be a reliable and robust exploration algorithm. It is shown to be a self-growing mapping network that is able to coordinate among numerous robots and replace any broken robots hence reducing the chance of system failure.

Simultaneous localization and mapping (SLAM) is a challenging and vital problem in robotics. It is important in tasks such as disaster response, deep-sea and cave exploration, in which robots must construct a map of an unknown terrain, and at the same time localize themselves within the map. The issue with single- robot SLAM is the relatively high rate of failure in a realistic application, as well as the time and energy cost. In this work, we propose a new approach to decentralized multi-robot SLAM which uses a robot swarm to map the environment. This system is capable of mapping an environment without human assistance and without the need for any additional infrastructure. We assume that 1) no robot possesses sufficient memory to store the entire map of the environment, 2) the communication range of the robots is limited, and 3)there is no infrastructure present in the environment to assist the robot in communicating with others. To cope with these limitations, the swarm system is designed to work as an independent entity. The swarm can deploy new robots towards the region that is yet to be explored, coordinate the communication between the robots by using itself as the communication network and replace any malfunctioning robots. The proposed method proves to be a reliable and robust exploration algorithm. It is shown to be a self-growing mapping network that is able to coordinate among numerous robots and replace any broken robots hence reducing the chance of system failure.

Communication between humans and interactive robots will benefit if people have a clear mental model of the robots' intent and awareness. The aim with this thesis was to investigate how human-robot interaction is affected by manipulation of social cues on the robot. The research questions were: How do social cues affect mental models of the Pepper robot, and how can a participatory design method be used for investigating how the Pepper robot could communicate intent and awareness? The hypothesis for the second question was that nonverbal cues would be preferred over verbal cues. An existing standard platform was used, Softbank's Pepper, as well as state-of-the-art tasks from the RoboCup@Home challenge. The rule book and observations from the 2018 competition were thematically coded and the themes created eight scenarios. A participatory design method called PICTIVE was used in a design study, where five student participants went through three phases, label, sketch and interview, to create a design for how the robot should communicate intent and awareness. The use of PICTIVE was a suitable way to extract a lot of design ideas. However, not all scenarios were optimal for the task. The design study confirmed the use of mediating physical attributes to alter the mental model of a humanoid robot to reach common ground. Further, it did not confirm the hypothesis that nonverbal cues would be preferred over verbal cues, though it did show that verbal cues would not be enough. This, however, needs to be further tested in live interactions.