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Search: L4X0:1650 8580 > (2020-2024)

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1.
  • Adolfsson, Daniel, 1992- (author)
  • Robust large-scale mapping and localization : Combining robust sensing and introspection
  • 2023
  • Doctoral thesis (other academic/artistic)abstract
    • The presence of autonomous systems is rapidly increasing in society and industry. To achieve successful, efficient, and safe deployment of autonomous systems, they must be navigated by means of highly robust localization systems. Additionally, these systems need to localize accurately and efficiently in realtime under adverse environmental conditions, and within considerably diverse and new previously unseen environments.This thesis focuses on investigating methods to achieve robust large-scale localization and mapping, incorporating robustness at multiple stages. Specifically, the research explores methods with sensory robustness, utilizing radar, which exhibits tolerance to harsh weather, dust, and variations in lighting conditions. Furthermore, the thesis presents methods with algorithmic robustness, which prevent failures by incorporating introspective awareness of localization quality. This thesis aims to answer the following research questions:How can radar data be efficiently filtered and represented for robust radar odometry? How can accurate and robust odometry be achieved with radar? How can localization quality be assessed and leveraged for robust detection of localization failures? How can self-awareness of localization quality be utilized to enhance the robustness of a localization system?While addressing these research questions, this thesis makes the following contributions to large-scale localization and mapping: A method for robust and efficient radar processing and state-of-the-art odometry estimation, and a method for self-assessment of localization quality and failure detection in lidar and radar localization. Self-assessment of localization quality is integrated into robust systems for large-scale Simultaneous Localization And Mapping, and rapid global localization in prior maps. These systems leverage self-assessment of localization quality to improve performance and prevent failures in loop closure and global localization, and consequently achieve safe robot localization.The methods presented in this thesis were evaluated through comparative assessments of public benchmarks and real-world data collected from various industrial scenarios. These evaluations serve to validate the effectiveness and reliability of the proposed approaches. As a result, this research represents a significant advancement toward achieving highly robust localization capabilities with broad applicability.
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2.
  • Akalin, Neziha, 1988- (author)
  • Perceived Safety in Social Human-Robot Interaction
  • 2022
  • Doctoral thesis (other academic/artistic)abstract
    • This compilation thesis contributes to a deeper understanding of perceived safety in human-robot interaction (HRI) with a particular focus on social robots. The current understanding of safety in HRI is mostly limited to physical safety, whereas perceived safety has often been neglected and underestimated. However, safe HRI requires a conceptualization of safety that goes beyond physical safety covering also perceived safety of the users. Within this context, this thesis provides a comprehensive analysis of perceived safety in HRI with social robots, considering a diverse set of human-related and robot-related factors.Two particular challenges for providing perceived safety in HRI are 1) understanding and evaluating human safety perception through direct and indirect measures, and 2) utilizing the measured level of perceived safety for adapting the robot behaviors. The primary contribution of this dissertation is in addressing the first challenge. The thesis investigates perceived safety in HRI by alternating between conducting user studies, literature review, and testing the findings from the literature within user studies.In this thesis, six main factors influencing perceived safety in HRI are lifted: the context of robot use, the user’s comfort, experience and familiarity with robots, trust, sense of control over the interaction, and transparent and predictable robot behaviors. These factors could provide a common understanding of perceived safety and bridge the theoretical gap in the literature. Moreover, this thesis proposes an experimental paradigm to observe and quantify perceived safety using objective and subjective measures. This contributes to bridging the methodological gap in the literature.The six factors are reviewed in HRI literature, and the robot features that affect these factors are organized in a taxonomy. Although this taxonomy focuses on social robots, the identified characteristics are relevant to other types of robots and autonomous systems. In addition to the taxonomy, the thesis provides a set of guidelines for providing perceived safety in social HRI. As a secondary contribution, the thesis presents an overview of reinforcement learning applications in social robotics as a suitable learning mechanism for adapting the robots’ behaviors to mitigate psychological harm.
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3.
  • Arain, Muhammad Asif, 1983- (author)
  • Efficient Remote Gas Inspection with an Autonomous Mobile Robot
  • 2020
  • Doctoral thesis (other academic/artistic)abstract
    • Human-caused greenhouse gas emissions are one of the major sources of global warming, which is threatening to reach a tipping point. Inspection systems that can provide direct information about critical factors causing global warming, such as systems for gas detection and location of gas sources, are urgently needed to analyze the fugitive emissions and take necessary actions.This thesis presents an autonomous robotic system capable of performing efficient exploration by selecting informative sampling positions for gas detection and gas distribution mapping – the Autonomous Remote Methane Explorer (ARMEx). In the design choice of ARMEx, a ground robot carries a spectroscopybased remote gas sensor, such as a Remote Methane Leak Detector (RMLD), that collects integral gas measurements along up to 30 m long optical-beams. The sensor is actuated to sample a large area inside an adjustable field of view, and with the mobility of the robot, adaptive sampling for high spatial resolution in the areas of interest is made possible to inspect large environments.In a typical gas sampling mission, the robot needs to localize itself and plan a traveling path to visit different locations in the area, which is a largely solved problem. However, the state-of-the-art prior to this thesis fell short of providing the capability to select informative sampling positions autonomously. This thesis introduces efficient measurement strategies to bring autonomy to mobile remote gas sensing. The strategies are based on sensor planning algorithms that minimize the number of measurements and distance traveled while optimizing the inspection criteria: full sensing coverage of the area for gas detection, and suitably overlapping sensing coverage of different viewpoints around areas of interest for gas distribution mapping.A prototype implementation of ARMEx was deployed in a large, real-world environment where inspection missions performed by the autonomous system were compared with runs teleoperated by human experts. In six experimental trials, the autonomous system created better gas maps, located more gas sources correctly, and provided better sensing coverage with fewer sensing positions than human experts.
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4.
  • Eriksson, Daniel, 1987- (author)
  • Getting to grips with cartons : Interactions of carbonboard packages with an artificial finger
  • 2021
  • Doctoral thesis (other academic/artistic)abstract
    • Packaging is an important part of most products in our modern world. It produces waste, but it also enables products to reach consumers safely and efficiently. Hence, the proper design of packaging is becoming increasingly important. Historically, cartonboard packages were designed for box compression strength. While this remains important, there are other types of loads that are important to consider. One such type of load arises from manual handling. As packages as moved and used, consumers need to exert forces on the package. These forces deform and can damage the package.Understanding these interactions can be challenging. By developing a method for quantifying the deformation due to manual handling, it becomes possible to measure and compare a redesigned package with the original to see if the performance has changed. This can aid packaging designers, but it can also be used for product control. The converting process is complex and deviations from specification can be introduced at many points along the production process.In this work, a method for quantifying interactions similar to those in manual handling is presented and evaluated. The method is then applied to study the effect of position and material properties on the mechanics of the interaction. The method is shown to have low variability and be robust to modifications in packaging and experimental design. It was seen that increasing the size of packages from 82 mm to 98 mm corresponded to decreasing the grammage by 10-20%. The method also showed the stiffening effect of corners and flaps, suggesting that the strategic placement of these design elements could help maintain the desired mechanical properties of the package at the point of interaction, provided the most likely point can be predicted.
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5.
  • Fan, Han, 1989- (author)
  • Robot-aided Gas Sensing for Emergency Responses
  • 2022
  • Doctoral thesis (other academic/artistic)abstract
    • Emergency response personnel can be exposed to various extreme hazards during the response to natural and human-made disasters. In many of the scenarios, one of the risk factors is the presence of hazardous airborne chemicals. Addressing this risk factor requires typical tiring, taxing and toxic operations that are suitable to be aided by Mobile Robot Olfaction (MRO) techniques. MRO is the research domain combining intelligent mobile robots with an artificial sense of smell. It presents the prospect of practical applications for emergency response as it allows to convey useful information on-site and online without risking the safety of human responders. However, standard gas sampling procedures for laboratory use are not directly applicable to MRO due to the complexity of uncontrolled environments and the need for fast deployment and analysis. Besides, state-of-the-art gas sensing approaches have difficulties handling A Priori Unknown Gases (APUG). In APUG situations, the number or/and identities of the present chemicals are unknown, posing challenges in recognizing the underlying risks with conventional solutions such as supervised learning-based electronic noses or dedicated gas sensors targeting known analytes.This dissertation focuses on contributions toward real-world applications of robot-aided gas sensing with an APUG problem setup. The dissertation starts with a requirement analysis of Gas Sensing for Emergency Response (GSER) to identify the key tasks in ad hoc applications. Considering that not all analytes of interest in a field application may be known in advance, a pipeline incorporating non-supervised detection and discrimination of multiple chemicals and consequent distribution modelling is found to be important for GSER. The remainder of the thesis fills this pipeline with three steps: 1) An ensemble learning-based gas detection approach is proposed to recognize significant changes from sensor signals as well as model the baseline response pattern. 2) A clustering analysis-based gas discrimination approach is developed to perform online analysis that automatically learns the number of different chemical compounds from the acquired measurements and provides a probabilistic representation of their class labels. 3) The integration of the proposed non-supervised gas detection and gas discrimination approaches with gas distribution modelling allows prototyping of a GSER system, which can enhance emergency responders’ situational awareness in the target environment. This GSER system demonstrates the concept of discriminating and mapping multiple unknown chemical compounds in uncontrolled environments with validation and evaluation using real-world data sets.During the research on the GSER system, gas dispersal simulation is also investigated to facilitate MRO algorithm development and validation in general. In-field experiments of MRO algorithms are often time-consuming, expensive, cumber some, and lack repeatability, while most of the available simulation tools are limited to insitu gas sensors and simple environments. These issues were addressed by improving a simulation framework to replicate geometrical representations of actual real-world environments and support a variety of gas sensor models. The potential applicability of the resulting work is demonstrated by simulating a gas emission monitoring task and facilitating the development process of a state-of-the-art time-dependent gas distribution modelling algorithm.
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6.
  • Giaretta, Alberto, 1988- (author)
  • Securing the Internet of Things with Security-by-Contract
  • 2021
  • Doctoral thesis (other academic/artistic)abstract
    • Smart homes, industry, healthcare, robotics; virtually every market has seen the uprising of Internet of Things (IoT) devices with different degrees and nuances. IoT devices embody different desirable characteristics, such as mobility, ubiquity, variety, and affordability. All combined, these features made so that IoT devices reached 35 billion units in the world. However, the sudden uprising of market demand put enormous pressure on manufacturers. The necessity of delivering to customers as many devices as possible, in the shortest time possible, leads manufacturers to overlook features that are not perceived critical by the users, such as resiliency to cyberattacks. This led to severe security issues. The prime example is Mirai, a malware that infected hundreds of thousands of IoT devices in 2016 and used them to strike lethal Distributed Denial of Service (DDoS) attacks.In the first part of this thesis, we present the state of the art regarding IoT devices security resilience. In particular, we provide relevant examples of breaches, an analysis of the relationship between IoT and Cloud from a security point of view, and an example of an IoT device penetration test. Then, we focus on the usage of IoT devices in DDoS-enabled botnets and we provide an extensive study of DDoS-enabling malwares, discussing their evolution and their capabilities.In the second part, we contextualise the gathered knowledge and we show that the highlighted problems stem from two main causes: insecure configurations and insufficient secure configurability.We also show that, to address these two issues, it is necessary to equip IoT devices with precise and formal descriptions of their behaviour. Therefore, we propose SC4IoT, a security framework for IoT devices that combines Security-by-Contract (SC) paradigm and Fog Computing paradigm. First, we provide a thorough breakdown of our proposal. We start from high-level lifecycles that describe how devices participate to SC4IoT. Then, we discuss the pillars that compose the framework (e.g., security contracts and security policies), together with their formal descriptions. Last, we provide precise algorithms for achieving security-policy matching capabilities, as well as routines for allowing the framework to deal with dynamic changes while maintaining consistency.
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7.
  • Hoang, Dinh-Cuong, 1991- (author)
  • Vision-based Perception For Autonomous Robotic Manipulation
  • 2021
  • Doctoral thesis (other academic/artistic)abstract
    • In order to safely and effectively operate in real-world unstructured environments where a priori knowledge of the surroundings is not available, robots must have adequate perceptual capabilities. This thesis is concerned with several important aspects of vision-based perception for autonomous robotic manipulation. With a focus on topics related to scene reconstruction, object pose estimation and grasp configuration generation, we aim at helping robots to better understand their surroundings, to avoid undesirable contacts with the environment and to accurately grasp selected objects.With the wide availability of affordable RGB-D cameras, research on visual SLAM (Simultaneous Localization and Mapping) or scene reconstruction has made giant strides in development. As a key element of an RGB-D reconstruction system, a large number of registration algorithms have been proposed in the context of RGB-D Tracking and Mapping (TAM). The state-of-the-art methods rely on color and depth information to track camera poses. Besides depth and color images, semantic information is now often available due to the advancement of image segmentation driven by deep learning. We are interested to explore to what extent the use of semantic cues can increase the robustness of camera pose tracking. This leads to the first contribution of this dissertation. A method for reliable camera tracking using an objective function that combines geometric, appearance, and semantic cues with adaptive weights.Beyond the purely geometric model of the environment produced by classical reconstruction systems, the inclusion of rich semantic information and 6D poses of object instances within a dense map is useful for robots to effectively operate and interact with objects. Therefore, the second contribution of this thesis is an approach for recognizing objects present in a scene and estimating their full pose by means of an accurate 3D semantic reconstruction. Our framework deploys simultaneously a 3D mapping algorithm to reconstruct a semantic model of the environment, and an incremental 6D object pose recovery algorithm that carries out predictions using the reconstructed model. We demonstrate that we can exploit multiple viewpoints around the same object to achieve robust and stable 6D pose estimation in the presence of heavy clutter and occlusion.The methods taking RGB-D images as input have achieved state-of-the-art performance on the object pose estimation task. However, in a number of cases, color information may not be available — for example, when the input is point cloud data from laser range finders or industrial high-resolution 3D sensors. Therefore, besides methods using RGB-D images, studies on recovering the 6D pose of rigid objects from 3D point clouds containing only geometric information are necessary. The third contribution of this dissertation is a novel deep learning architecture to address the problem of estimating the 6D pose of multiple rigid objects in a cluttered scene, using only a 3D point cloud of the scene as an input. The proposed architecture pools geometric features together using a self-attention mechanism and adopts a deep Hough voting scheme for pose proposal generation. We show that by exploiting the correlation between poses of object instances and object parts we can improve the performance of object pose estimation.By applying a 6D object pose estimation algorithm, robots can perform grasping known objects where the 3D model of objects is available and a grasp database is pre-defined. What if we want to grasp novel objects? The fourth contribution of this thesis is a method for robust manipulation of novel objects in cluttered environments. we develop an end-to-end deep learning approach for generating grasp configurations for a two-finger parallel jaw gripper, based on 3D point cloud observations of the scene. The proposed model generates candidates by casting votes to accumulate evidence for feasible grasp configurations. We exploit contextual information by encoding the dependency of objects in the scene into features to boost the performance of grasp generation.
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8.
  • Kondyli, Vasiliki, 1989- (author)
  • Behavioural Principles for the Design of Human-Centred Cognitive Technologies : The Case of Visuo-Locomotive Experience
  • 2023
  • Doctoral thesis (other academic/artistic)abstract
    • The successful application, usability, and social emancipation of AI technologies necessitates that the design and implementation of technical systems be founded on human-centred principles, be it cognitive or behavioural, social, ethical etc. Towards this objective, this thesis develops an interdisciplinary methodology for embedding cognitive behavioural principles in the design and development of next-generation human-centred AI technologies that aim to assist and empower humans in everyday life.The interdisciplinary methodology developed in this research categorically focusses on two key aspects pertaining to human-centred technology design and engineering: (1) human behavioural precedents; and (2) cognitively founded representational and computational modalities:Human behavioural precedents are established by systematically analysing human visuo-locomotive experience during everyday activities involving (embodied) multimodal interactions. We conduct naturalistic behavioural experiments focusing on aspects of visual perception (e.g., inattention blindness) and spatial cognition (e.g., orientation, navigation) in diverse settings of everyday mobility. As specific -in-the-wild- experimental contexts, we focus on behavioural aspects involved in everyday (human) navigation and driving.Representational and computational modalities are developed based on cognitively-driven articulation of behavioural precedents. Particularly, a cognitive model of visuospatial complexity for grounding embodied multimodal interactions is developed by incorporating behavioural precedents pertaining to representations of space, motion, and interaction. Furthermore, precedents concerning human preferences are used as a basis for semantically-driven computational synthesis (e.g. in the generation and manipulation of spatial morphologies), and in the articulation of human-centred evaluation and standardisation of AI systems.As case studies we demonstrate the developed methodology in the backdrop of two application domains: (a) design assistance technologies, and (b) autonomous driving. More broadly, this thesis emphasises the need for embedding ecologically valid behavioural knowledge within the development of "human-centred" technologies.  Furthermore, this research paves the way for the development of systems that understand, interpret and anticipate human behaviour under ecologically valid naturalistic circumstances.
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9.
  • Larsson, Joakim, 1989- (author)
  • Have you heard about wire? Monitoring of the wire drawing process
  • 2024
  • Doctoral thesis (other academic/artistic)abstract
    • Wire made from metal is a fundamental component found in almost every complex product, ranging from a simple pen to a spacecraft. It is used to manufacture nails, screws, springs, rivets, cables, welding electrodes, and numerous other items that surround us daily.In an era characterized by increased environmental concerns and the pressing need for the industry to become more sustainable, process monitoring has emerged as a key instrument for strengthening the sustainability improvements of diverse industries and operations. Many industries have transitioned into the realm of Industry 4.0, entering an era of digital transformation and datadriven decision-making. However, the production of steel wire has fallen behind. The wire drawing process has been performed in a similar manner for the last century and the production machines generally lack advanced monitoring systems. To catch up, there is a great need to digitize the wire drawing process and that is the focus of this thesis, Monitoring of the wire drawing process.In this thesis several different methods to monitor the wire drawing process are developed and evaluated, resulting in a process monitoring system for the wire drawing process.
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10.
  • Morillo-Mendez, Lucas, 1991- (author)
  • SOCIAL ROBOTS / SOCIAL COGNITION : Robots' Gaze Effects in Older and Younger Adults
  • 2023
  • Doctoral thesis (other academic/artistic)abstract
    • This dissertation presents advances in social human-robot interaction (HRI) and human social cognition through a series of experiments in which humans face a robot. A predominant approach to studying the human factor in HRI consists of placing the human in the role of a user to explore potential factors affecting the acceptance or usability of a robot. This work takes a broader perspective and investigates if social robots are perceived as social agents, irrespective of their final role or usefulness in a particular interaction. To do so, it adopts methodologies and theories from cognitive and experimental psychology, such as the use of behavioral paradigms involving gaze following and a framework of more than twenty years of research employing gaze to explore social cognition. The communicative role of gaze in robots is used to explore their essential effectiveness and as a tool to learn how humans perceive them. Studying how certain social robots are perceived through the lens of research in social cognition is the central contribution of this dissertation.This thesis presents empirical research and the multidisciplinary literature on (robotic) gaze following, aging, and their relation with social cognition. Papers I and II investigate the decline in gaze following associated with aging, linked with a broader decline in social cognition, in scenarios with robots as gazing agents. In addition to the participants' self-reported perception of the robots, their reaction times were also measured to reflect their internal cognitive processes. Overall, this decline seems to persist when the gazing Overall, this decline seems to persist when the gazing agent is a robot, highlighting our depiction of robots as social agents. Paper IV explores the theories behind this decline using a robot, emphasizing how these theories extend to non-human agents. This work also investigates motion as a competing cue to gaze in social robots (Paper III), and mentalizing in robotic gaze following (Paper V).Through experiments with participants and within the scope of HRI and social cognition studies, this thesis presents a joint framework highlighting that robots are depicted as social agents. This finding emphasizes the importance of fundamental insights from social cognition when designing robot behaviors. Additionally, it promotes and supports the use of robots as valuable tools to explore the robustness of current theories in cognitive psychology to expand the field in parallel.
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