With over 53 million articles and 11 million images, Wikipedia is the greatest encyclopedia in history. The number of users is equally significant, with daily views surpassing 1 billion. Such an enormous system needs automation of tasks to make it possible for the volunteers to maintain. When it comes to textual data, there is a system based on machine learning called ORES providing automation to tasks such as article quality estimation and article topic routing. A visual counterpart system also needs to be developed to support tasks such as vandalism detection in images and for a better understanding of the visual data of Wikipedia. Researchers from the Wikimedia Foundation identified a hindrance to implementing the visual counterpart of ORES: the images of Wikipedia lack topical metadata. Thus, this work aims to develop a deep learning model that classifies images into a set of topics, which have been pre-determined in parallel work. State-of-the-art image classification models and other methods to mitigate the existing class imbalance are used. The conducted experiments show, among others, that: using the data that considers the hierarchy of labels performs better; resampling techniques are ineffective at mitigating imbalance due to the high label concurrence; sample-weighting improves metrics; and that initializing parameters as pre-trained on ImageNet rather than randomly yields better metrics. Moreover, we find interesting outlier labels that, despite having fewer samples, obtain better performance metrics, which is believed to be either due to bias from pre-training or simply more signal in the label. The distribution of the visual data predicted by the models displayed. Finally, some qualitative examples of the model predictions to some images are presented, proving the ability of the model to find correct labels that are missing in the ground truth

This thesis aims to create a system using colour markings for Volvo’s autonomous wheel loaders which determines their restricted area and operation using sensors available on the machine. The wheel loader shall be able to interpret and distinguish different colours of spray paint, and depending on the colour, act accordingly. Six different colours are evaluated across two different colour types to find the most suitable ones for the system. Multiple tests are presented throughout the thesis to find the approach with the most optimal performance that meets the system's requirements. The system is evaluated in various weather conditions to determine how the weather affects the performance of the system. The thesis also compares two different line-following approaches, where one is based on edge detection using Canny Edge and Hough transform, and the other uses histogram analysis and sliding window search, to distinguish and track the colour markings. While the wheel loader is in operation, it collects GPS coordinates to create a map of the path taken by the wheel loader and the location of various tasks. The evaluation shows that red, green and blue in fluorescent colour type are the most suitable colours for such a system. The line-following algorithm that utilises perspective warp, histogram and a sliding window search was the most prominent for accurate line detection and tracking. Furthermore, the evaluation showed that the performance of the system was affected depending on the weather condition.

Since the advent of machine learning and machine vision methods, progress has been made in tackling the long-standing research question of autonomous grasping of arbitrary objects using robotic end-effectors. Building on these efforts, we focus on a subset of the general grasping problem concerning the automation of a forwarder. This forestry vehicle collects and transports felled and cut tree logs in a forest environment to a nearby roadside landing. The forwarder must safely and energy-efficiently grip logs to minimize fuel consumption and reduce loading times. In this thesis project, we develop a data-driven model for predicting the expected outcome of grasping attempts made by the forwarder's crane. For a given pile of logs, such a model can estimate the optimal horizontal location and angle for applying the claw grapple, enabling effective grasp planning. We utilize physics-based simulations to create a ground truth dataset of 12 500 000 simulated grasps distributed across 5000 randomly generated log piles. Our semi-generative, supervised model is a fully convolutional network that inputs the orthographic depth image of a pile and returns images predicting the corresponding grasps' initial grapple angle and outcome metrics as a function of position. Over five folds of cross-validation, our model predicted the number of grasped logs and the initial grapple angle with a normalized root mean squared error of 15.77(2)% and 2.64(4)%, respectively. The grasps' energy efficiency and energy waste were similarly predicted with a relative error of 14.43(2)% and 21.06(3)%. / Sedan tillkomsten av maskininlärnings- och maskinseendebaserade metoder har betydande framsteg gjorts inom forskningsområdet för autonom greppning av godtyckliga objekt med en robotisk sluteffektor. Vi bygger vidare på dessa resultat och fokuserar på en del av det generella greppningsproblemet gällande automatisering av en skotare. Denna skogsmaskin samlar in och transporterar fällda och kapade trädstammar från avverkningsplats till upplag intill närliggande skogsbilväg. Skotaren måste greppa och lyfta stockarna på ett säkert och energieffektivt sätt för att minimera bränsleförbrukningen samt minska lastningstiderna. I detta examensarbete utvecklar vi en datadriven modell för att förutsäga det förväntade resultatet av gripförsök utförda av skotarens kran. För en given timmerstockshög kan en sådan modell uppskatta den optimala positionen och vinkeln för att applicera skotarens gripklo, vilket möjliggör effektiv planering av lastningen. Vi använder fysikbaserade simuleringar för att skapa ett dataset med 12 500 000 simulerade gripförsök fördelade över 5000 slumpmässigt genererade timmerhögar. Vår semi-generativa, övervakade modell är ett djupt faltningsnätverk utan helt sammankopplade neuronlager som tar in en ortografisk djupbild av en timmerhög och returnerar bilder som predikterar de motsvarande gripförsökens initiala gripvinkel och resultatmått som en funktion av position. Vid en femfaldig korsvalidering förutsåg vår modell antalet greppade stockar och den initiala gripvinkeln med ett normaliserat rotmedelkvadratfel på 15.77(2)% respektive 2.64(4)%. Gripförsökens energieffektivitet och energiförlust predikterades på liknande sätt med ett relativt fel på 14.43(2)% och 21.06(3)%.

[pt] A auto-organização possui conceitos amplamente utilizados em diversos domínios que vão desde a biologia até aplicações em sistemas distribuí- dos. Além disso, os seus princípios servem como base e inspiração para a modelagem e desenvolvimento de tecnologias contemporâneas, como os drones e os sistemas autônomos. Nos últimos anos, os sistemas autônomos de direção e suas aplicações têm gerado um grande interesse na sociedade, principalmente pelo potencial de melhorar a segurança e a eficiência dos fluxos rodoviários. Neste trabalho, investigamos os principais aspectos de auto-organização presentes na formação de comboios veiculares dinâmicos. Ademais, modelamos e desenvolvemos um protocolo de gerenciamento de comboios baseado em fundamentos de auto-organização. Finalmente, demonstramos que o protocolo preserva a segurança e estabilidade do comboio em um cenário onde há redução de faixas no ambiente rodoviário. / [en] Self-organization has concepts widely used in many fields ranging from biology to applications in distributed systems. Besides, its principles serve as the basis and inspiration for the modeling and the development of contemporary technologies, such as drones and autonomous systems. In recent years, autonomous driving systems and their applications have instigated a great interest in society, primarily due to the potential of improving the safety and efficiency of road traffic. In this work, we investigate the main aspects of self-organization present in dynamic vehicle platoons. Also, we model and develop a platoon management protocol based on self-organizing principles. Finally, we demonstrate that the protocol maintains the safety and stability of the platoon in a lane reduction scenario in the road environment.

Most of the navigation systems used in today’s aircraft rely on Global Navigation Satellite Systems (GNSS). However, GNSS is not fully reliable. For example, it can be jammed by attacks on the space or ground segments of the system or denied at inaccessible areas. Hence to ensure successful navigation it is of great importance to continuously be able to establish the aircraft’s location without having to rely on external reference systems. Localization is one of many sub-problems in navigation and will be the focus of this thesis. This brings us to the field of visual odometry (VO), which involves determining position and orientation with the help of images from one or more camera sensors. But to date, most VO systems have primarily been established on ground vehicles and low flying multi-rotor systems. This thesis seeks to extend VO to new applications by exploring it in a fairly new context; a fixed-wing piloted combat aircraft, for vision-only pose estimation in applications of extremely large scene depth. A major part of this research work is the data gathering, where the data is collected using the flight simulator X-Plane 11. Three different flight routes are flown; a straight line, a curve and a loop, for two types of visual conditions; in clear weather with daylight and during sunset. The method used in this work is ORB-SLAM3, an open-source library for visual simultaneous localization and mapping (SLAM). It has shown excellent results in previous works and has become a benchmark method often used in the field of visual pose estimation. ORB-SLAM3 tracks the straight line of 78 km very well at an altitude over 2700 m. The absolute trajectory error (ATE) is 0.072% of the total distance traveled in daylight and 0.11% during sunset. These results are of the same magnitude as ORB-SLAM3 on the EuRoC MAV dataset. For the curved trajectory of 79 km ATE is 2.0% and 1.2% of total distance traveled in daylight and sunset respectively.  The longest flight route of 258 km shows the challenges of visual pose estimation. Although it is managing to close loops in daylight, it has an ATE of 3.6% during daylight. During sunset the features do not possess enough invariant characteristics to close loops, resulting in an even larger ATE of 14% of total distance traveled. Hence to be able to use and properly rely on vision in localization, more sensor information is needed. But since all aircraft already possess an inertial measurement unit (IMU), the future work naturally includes IMU data in the system. Nevertheless, the results from this research show that vision is useful, even at the high altitudes and speeds used by a combat aircraft.

Modern autonomous systems should be able to interact with their surroundings in a flexible yet safe manner. To guarantee this behavior, such systems must learn how to approach unseen entities in their environment through the inference of relationships between actions and objects, called affordances. This research project introduces a neuro-symbolic AI system capable of inferring affordances using attribute detection and knowledge representation as its core principles. The attribute detection module employs a visuo-lingual image captioning model to extract the key object attributes of a scene, while the cognitive knowledge module infers the affordances of those attributes using conditional probability. The practical capabilities of the neuro-symbolic AI system are assessed by implementing a simulated robot system that interacts within the problem space of jars and bottles. The neuro-symbolic AI system is evaluated through its caption-inferring capabilities using image captioning and machine translation metrics. The scores registered in the evaluation show a successful attribute captioning rate of more than 71%. The robot simulation is evaluated within a Unity virtual environment by interacting with 50 jars and bottles, equally divided between lifting and twisting affordances. The robot system successfully interacts with all the objects in the scene due to the robustness of the architecture but fails in the inference process 24 out of the 50 iterations. Contrary to previous works approaching the problem as a classification task, this study shows that affordance inference can be successfully implemented using a cognitive visuo-lingual method. The study’s results justify further study into the use of neuro-symbolic AI approaches to affordance inference.

This thesis explores and evaluates solutions for the inspection of cutting tool containers at Sandvik Coromant, focusing on the transition from current vision systems utilizing infrared (IR) light to new methods compatible with recycled polypropylene (PP) plastic containers. The primary goal is to evaluate the effectiveness of stereo vision and artificial intelligence (AI) for depth estimation, ensuring that the containers are properly populated with cutting tools. Various methods and algorithms are tested to determine their accuracy and speed, to meet the time requirements of the production line at Sandvik Coromant. The results indicate that, while traditional IR-based systems excel in processing speed and robustness, monocular artificial intelligence methods offer adaptability that could be utilized with the new container material. Future work will involve further optimization and real-world testing to confirm these findings.

The fashion industry is a significant contributor to greenhouse gas emissions and textile waste, prompting the need for sustainable practices. This thesis explores the use of diffusion models for generating synthetic data to enhance datasets used in machine learning, specifically focusing on second-hand fashion. Diffusion models, known for their ability to create high-quality images, offer potential solutions to the imbalance and quality issues in existing datasets. The study investigates how image generation and editing through diffusion models can improve datasets, the effectiveness of different prompting strategies, and the performance of synthetic data in machine learning models compared to real data. The methodology involves using the Kandinsky 2.2 inpainting model to generate and edit images, followed by manual and automated classification to evaluate image quality. Experiments demonstrate that diffusion models can plausibly improve dataset quality by adding and removing damage in images, although fully automating this process remains challenging. The results indicate that augmenting the datasets with synthetic images can potentially enhance the performance of the model, although the variability of the results suggests the need for further research. This thesis contributes to the field of sustainable fashion by proposing innovative methods for dataset augmentation using state-of-the-art generative models, aiming to support the development of efficient and automated sorting processes in the textile industry.

Electro Hydrostatic Actuators (EHA) are emerging as a viable option for industrial machine builders as the design combines the best of both electro-mechanical and electro-hydraulic technologies. The EHA is a highly integrated, compact alternative to traditional hydraulic solutions. Automation engineers moving toward electro-mechanical actuation in pursuit of energy efficiency and environmental cleanliness, will find an EHA an attractive option for high force density actuators. This paper will address the factors to consider when assessing an industrial machine’s application suitability for this latest innovation in actuation. It describes principal base circuits, a concept for EHA building blocks and a realized pilot application as well as challenges on actuator and components level.

Målet med examensarbetet har varit att samla information åt ett tekniskt konsultföretag för att öka deras kunskap om autonoma system och fordonskommunikation. Statusen på arbetet kring dessa aktiva säkerhetssystem hos olika aktörer och hur systemen implementeras i dagens och framtidens fordon har undersökts genom omfattande litteraturstudier, intervjuer och marknadsanalyser. De autonoma systemen kan samla information från omgivningen genom sensorer och bidra till ett jämnare trafikflöde, ökad säkerhet, lättare bilar och bättre miljö. Genom fordonskommunikationen kan fordon kommunicera med varandra samt infrastrukturen och garantera en säker bilfärd. År 2030 utgörs innerstaden av autonom, elektrifierad kollektivtrafik för att transportera människor på begäran, samtidigt som personbilar till viss del förbjuds. Potentiella behov för människan i en fullt autonom bil har identifierats och diverse produktutvecklingsmetoder har tillämpats för att utforma två konceptuella lösningar för en framtida bilinteriör. Lösningarna visar interaktionen mellan människa och system eftersom underhållning och bekvämlighet blir viktigt i en fullt autonom bil. Respektive lösning är statsägd och rymmer fyra passagerare. I lösningarna är sittplatserna placerade på ett sätt som underlättar kommunikation mellan passagerarna. Passagerarna kan underhållas eller informeras individuellt eller gemensamt via text, ljud och bild. / The goal with this thesis project has been to collect information for a technical consulting company in order to increase their knowledge about autonomous systems and vehicular communication. The status of how various operators work with active safety systems and how the systems are implemented in current and future vehicles has been investigated through extensive literature studies, interviews and market research. The autonomous systems can collect information from the surrounding through sensors and contribute to better traffic efficiency, increased safety, lighter cars and a better environment. Through vehicle communication, the vehicle can communicate with each other in order to guarantee a safe ride. In 2030 the inner city constitutes of autonomous, electrified public transport to transport people on demand, meanwhile private cars are prohibited. Potential needs for the human in a fully, autonomous car has been identified and various product development methods has been applied in order to develop two conceptual solutions for a future car interior. The solutions show the interaction between human and system since entertainment and comfort becomes important in a fully, autonomous car. Each solution is state-owned and holds four passengers. In the solutions, the seats are placed in regard to facilitate communication between the passengers. The passengers can be entertained or informed individually or collectively by text, sound and images.