483 research outputs found

    Design of decorative 3D models: from geodesic ornaments to tangible assemblies

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    L'obiettivo di questa tesi è sviluppare strumenti utili per creare opere d'arte decorative digitali in 3D. Uno dei processi decorativi più comunemente usati prevede la creazione di pattern decorativi, al fine di abbellire gli oggetti. Questi pattern possono essere dipinti sull'oggetto di base o realizzati con l'applicazione di piccoli elementi decorativi. Tuttavia, la loro realizzazione nei media digitali non è banale. Da un lato, gli utenti esperti possono eseguire manualmente la pittura delle texture o scolpire ogni decorazione, ma questo processo può richiedere ore per produrre un singolo pezzo e deve essere ripetuto da zero per ogni modello da decorare. D'altra parte, gli approcci automatici allo stato dell'arte si basano sull'approssimazione di questi processi con texturing basato su esempi o texturing procedurale, o con sistemi di riproiezione 3D. Tuttavia, questi approcci possono introdurre importanti limiti nei modelli utilizzabili e nella qualità dei risultati. Il nostro lavoro sfrutta invece i recenti progressi e miglioramenti delle prestazioni nel campo dell'elaborazione geometrica per creare modelli decorativi direttamente sulle superfici. Presentiamo una pipeline per i pattern 2D e una per quelli 3D, e dimostriamo come ognuna di esse possa ricreare una vasta gamma di risultati con minime modifiche dei parametri. Inoltre, studiamo la possibilità di creare modelli decorativi tangibili. I pattern 3D generati possono essere stampati in 3D e applicati a oggetti realmente esistenti precedentemente scansionati. Discutiamo anche la creazione di modelli con mattoncini da costruzione, e la possibilità di mescolare mattoncini standard e mattoncini custom stampati in 3D. Ciò consente una rappresentazione precisa indipendentemente da quanto la voxelizzazione sia approssimativa. I principali contributi di questa tesi sono l'implementazione di due diverse pipeline decorative, un approccio euristico alla costruzione con mattoncini e un dataset per testare quest'ultimo.The aim of this thesis is to develop effective tools to create digital decorative 3D artworks. Real-world art often involves the use of decorative patterns to enrich objects. These patterns can be painted on the base or might be realized with the application of small decorative elements. However, their creation in digital media is not trivial. On the one hand, users can manually perform texture paint or sculpt each decoration, in a process that can take hours to produce a single piece and needs to be repeated from the ground up for every model that needs to be decorated. On the other hand, automatic approaches in state of the art rely on approximating these processes with procedural or by-example texturing or with 3D reprojection. However, these approaches can introduce significant limitations in the models that can be used and in the quality of the results. Instead, our work exploits the recent advances and performance improvements in the geometry processing field to create decorative patterns directly on surfaces. We present a pipeline for 2D and one for 3D patterns and demonstrate how each of them can recreate a variety of results with minimal tweaking of the parameters. Furthermore, we investigate the possibility of creating decorative tangible models. The 3D patterns we generate can be 3D printed and applied to previously scanned real-world objects. We also discuss the creation of models with standard building bricks and the possibility of mixing standard and custom 3D-printed bricks. This allows for a precise representation regardless of the coarseness of the voxelization. The main contributions of this thesis are the implementation of two different decorative pipelines, a heuristic approach to brick construction, and a dataset to test the latter

    Airfoil Optimization using Design-by-Morphing

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    We present Design-by-Morphing (DbM), a novel design methodology applicable to creating a search space for topology optimization of 2D airfoils. Most design techniques impose geometric constraints and sometimes designers' bias on the design space itself, thus restricting the novelty of the designs created, and only allowing for small local changes. We show that DbM methodology does not impose any such restrictions on the design space and allows for extrapolation from the search space, thus granting truly radical and large search space with a few design parameters. In comparison to other shape design methodologies, we apply DbM to create a search space for 2D airfoils. We optimize this airfoil shape design space for maximizing the lift-over-drag ratio, CLDmaxCLD_{max}, and stall angle tolerance, Δα\Delta \alpha. Using a bi-objective genetic algorithm to optimize the DbM space, it is found that we create a Pareto-front of radical airfoils exhibiting remarkable properties for both objectives

    On the motion planning & control of nonlinear robotic systems

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    In the last decades, we saw a soaring interest in autonomous robots boosted not only by academia and industry, but also by the ever in- creasing demand from civil users. As a matter of fact, autonomous robots are fast spreading in all aspects of human life, we can see them clean houses, navigate through city traffic, or harvest fruits and vegetables. Almost all commercial drones already exhibit unprecedented and sophisticated skills which makes them suitable for these applications, such as obstacle avoidance, simultaneous localisation and mapping, path planning, visual-inertial odometry, and object tracking. The major limitations of such robotic platforms lie in the limited payload that can carry, in their costs, and in the limited autonomy due to finite battery capability. For this reason researchers start to develop new algorithms able to run even on resource constrained platforms both in terms of computation capabilities and limited types of endowed sensors, focusing especially on very cheap sensors and hardware. The possibility to use a limited number of sensors allowed to scale a lot the UAVs size, while the implementation of new efficient algorithms, performing the same task in lower time, allows for lower autonomy. However, the developed robots are not mature enough to completely operate autonomously without human supervision due to still too big dimensions (especially for aerial vehicles), which make these platforms unsafe for humans, and the high probability of numerical, and decision, errors that robots may make. In this perspective, this thesis aims to review and improve the current state-of-the-art solutions for autonomous navigation from a purely practical point of view. In particular, we deeply focused on the problems of robot control, trajectory planning, environments exploration, and obstacle avoidance

    Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

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    Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

    Culling Techniques for Deformable NURBS Surfaces

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    [Resumo]: As superficies NURBS son unha representación estándar de modelos que se usan normalmente en deseño asistido por ordenador (CAD), enxeñaría naval, enxeñaría de automóbiles ou imaxes procesadas para uso médico. Adicionalmente estas ofrecen moitas vantaxes, debido a que con pequenas modificacións nalgúns puntos de control resultan en modelos moi complexos. No entanto, non hai garantía de obter un nivel suficientemente alto de teselación para evitar modelos con redes toscas, polo que no seguinte proxecto describiranse o uso de diferentes técnicas de culling, que axudarán a reducir o cómputo innecesario de superficies non visibles e en consecuencia a obter un mellor rendemento con menos artefactos.[Abstract]: NURBS surfaces are a standard representation of models typically used in computer-assisted design (CAD), naval engineering, car engineering, or medical-processed images. Furthermore, NURBS surfaces have many advantages, a small modification on some control points result in highly complex models. However there is no guarantee of obtaining an high enough level of tessellation in order to avoid coarse models, which is why in the following project it will be described the use of different culling techniques that might help to reduce the unnecessary computation of no visible surfaces and in consequence the achievement of a better performance with fewer artefacts.Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/202

    Chemometric tools for automated method-development and data interpretation in liquid chromatography

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    The thesis explores the challenges and advancements in the field of liquid chromatography (LC), particularly focusing on complex sample analysis using high-resolution mass spectrometry (MS) and two-dimensional (2D) LC techniques. The research addresses the need for efficient optimization and data-handling strategies in modern LC practice. The thesis is divided into several chapters, each addressing specific aspects of LC and polymer analysis. Chapter 2 provides an overview of the need for chemometric tools in LC practice, discussing methods for processing and analyzing data from 1D and 2D-LC systems and how chemometrics can be utilized for method development and optimization. Chapter 3 introduces a novel approach for interpreting the molecular-weight distribution and intrinsic viscosity of polymers, allowing quantitative analysis of polymer properties without prior knowledge of their interactions. This method correlates the curvature parameter of the Mark-Houwink plot with the polymer's structural and chemical properties. Chapters 4 and 5 focus on the analysis of cellulose ethers (CEs), essential in various industrial applications. A new method is presented for mapping the substitution degree and composition of CE samples, providing detailed compositional distributions. Another method involves a comprehensive 2D LC-MS/MS approach for analyzing hydroxypropyl methyl cellulose (HPMC) monomers, revealing subtle differences in composition between industrial HPMC samples. Chapter 6 introduces AutoLC, an algorithm for automated and interpretive development of 1D-LC separations. It uses retention modeling and Bayesian optimization to achieve optimal separation within a few iterations, significantly improving the efficiency of gradient LC separations. Chapter 7 focuses on the development of an open-source algorithm for automated method development in 2D-LC-MS systems. This algorithm improves separation performance by refining gradient profiles and accurately predicting peak widths, enhancing the reliability of complex gradient LC separations. Chapter 8 addresses the challenge of gradient deformation in LC instruments. An algorithm based on the stable function corrects instrument-specific gradient deformations, enabling accurate determination of analyte retention parameters and improving data comparability between different sources. Chapter 9 introduces a novel approach using capacitively-coupled-contactless-conductivity detection (C4D) to measure gradient profiles without adding tracer components. This method enhances inter-system transferability of retention models for polymers, overcoming the limitations of UV-absorbance detectable tracer components. Chapter 10 discusses practical choices and challenges faced in the thesis chapters, highlighting the need for well-defined standard samples in industrial polymer analysis and emphasizing the importance of generalized problem-solving approaches. The thesis identifies future research directions, emphasizing the importance of computational-assisted methods for polymer analysis, the utilization of online reaction modulation techniques, and exploring continuous distributions obtained through size-exclusion chromatography (SEC) in conjunction with triple detection. Chemometric tools are recognized as essential for gaining deeper insights into polymer chemistry and improving data interpretation in the field of LC

    Learning-based methods for planning and control of humanoid robots

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    Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans. No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience. This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity. First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks. Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness. The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3

    High-Order Numerical Integration on Domains Bounded by Intersecting Level Sets

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    We present a high-order method that provides numerical integration on volumes, surfaces, and lines defined implicitly by two smooth intersecting level sets. To approximate the integrals, the method maps quadrature rules defined on hypercubes to the curved domains of the integrals. This enables the numerical integration of a wide range of integrands since integration on hypercubes is a well known problem. The mappings are constructed by treating the isocontours of the level sets as graphs of height functions. Numerical experiments with smooth integrands indicate a high-order of convergence for transformed Gauss quadrature rules on domains defined by polynomial, rational, and trigonometric level sets. We show that the approach we have used can be combined readily with adaptive quadrature methods. Moreover, we apply the approach to numerically integrate on difficult geometries without requiring a low-order fallback method
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