SOFA: A Multi-Model Framework for Interactive Physical Simulation

International audienceSOFA (Simulation Open Framework Architecture) is an open-source C++ library primarily targeted at interactive computational medical simulation. SOFA facilitates collaborations between specialists from various domains, by decomposing complex simulators into components designed independently and organized in a scenegraph data structure. Each component encapsulates one of the aspects of a simulation, such as the degrees of freedom, the forces and constraints, the differential equations, the main loop algorithms, the linear solvers, the collision detection algorithms or the interaction devices. The simulated objects can be represented using several models, each of them optimized for a different task such as the computation of internal forces, collision detection, haptics or visual display. These models are synchronized during the simulation using a mapping mechanism. CPU and GPU implementations can be transparently combined to exploit the computational power of modern hardware architectures. Thanks to this flexible yet efficient architecture, \sofa{} can be used as a test-bed to compare models and algorithms, or as a basis for the development of complex, high-performance simulators

Co-evolution of morphology and controller for a robot

Genetic algorithms are inspired by the process of natural selection that exists in nature. This process is what leads species to evolve and adapt to their surroundings, with the fittest species reproducing, leading to new generations that can take advantage of their surroundings better than before. This type of process can be used in evolutionary robotics to achieve controllers that are able to solve specific tasks to evolve morphologies for a specific purpose such as to walk, swim, grasp objects, among others. Robotic grippers are used in most factories nowadays, as well as in other workplaces such as hospitals and laboratories. They are used in tasks such as grabbing/moving objects, painting, surgeries, among many other uses. Grippers are therefore a case study with several possibilities that lend themselves to evolving morphologies through genetic algorithms. In this dissertation, we explore morphology generation through genetic algorithms. Using grippers as our case study, we were able to generate grippers capable of grabbing and lifting an object. To evolve these grippers, we created a simulated environment where grippers followed a script with instructions to grab the object and then move up. In total 120 different grippers were generated in these experiments. Out of those 120 generated grippers, 28% were able to grab and lift an object successfully. After the evaluation process was completed, we experimented with the grippers in five different scenarios to test their robustness. In these scenarios, the object’s starting conditions were different from those in the evaluation process.Os algoritmos genéticos são inspirados pelo processo de seleção natural que existe na natureza. Este processo leva espécies a evoluir e adaptar-se ao meio ambiente envolvente, com as espécies mais aptas reproduzindo, levando a que novas gerações possam tirar um melhor proveito do ambiente que as rodeia. Este tipo de processo pode ser utilizado na robótica evolucionária para evoluir controladores capazes de resolver tarefas de forma a evoluir morfologias para uma finalidade específica, tais como andar, nadar, agarrar objetos, entre outros. Garras robóticas são utilizadas na maioria das fábricas, assim como noutros locais de trabalho tais como hospitais e laboratórios. Podem ser utilizadas em tarefas como agarrar/mover objetos, pintura, cirurgias, entre outros usos. São, portanto, um caso de estudo com várias possibilidades que se prestam à evolução de morfologias através de algoritmos genéticos. Nesta dissertação, exploramos a geração de morfologia através de algoritmos genéticos. Utilizando garras como o nosso caso de estudo, conseguimos gerar garras capazes de agarrar e levantar um objeto. Para evoluir essas garras, criamos um ambiente simulado onde cada garra seguiu um script com instruções para agarrar o objeto e, em seguida, mover para cima. No total, 120 garras diferentes foram geradas nestas experiências. Dessas garras geradas 120, 28% foram capazes de capturar e levantar um objeto com êxito. Após a conclusão do processo de avaliação, experimentamos as garras em cinco cenários diferentes para testar a sua robustez. Nesses cenários, as condições iniciais em que os objetos começam eram diferentes das do processo de avaliação

Interactive 3D Digital Models for Anatomy and Medical Education

This chapter explores the creation and use of interactive, three-dimensional (3D), digital models for anatomy and medical education. Firstly, it looks back over the history and development of virtual 3D anatomy resources before outlining some of the current means of their creation; including photogrammetry, CT and surface scanning, and digital modelling, outlining advantages and disadvantages for each. Various means of distribution are explored, including; virtual learning environments, websites, interactive PDF’s, virtual and augmented reality, bespoke applications, and 3D printing, with a particular focus on the level of interactivity each method offers. Finally, and perhaps most importantly, the use of such models for education is discussed. Questions addressed include; How can such models best be used to enhance student learning? How can they be used in the classroom? How can they be used for selfdirected study? As well as exploring if they could one day replace human specimens, and how they complement the rise of online and e-learning

Deformable meshes for shape recovery: models and applications

With the advance of scanning and imaging technology, more and more 3D objects become available. Among them, deformable objects have gained increasing interests. They include medical instances such as organs, a sequence of objects in motion, and objects of similar shapes where a meaningful correspondence can be established between each other. Thus, it requires tools to store, compare, and retrieve them. Many of these operations depend on successful shape recovery. Shape recovery is the task to retrieve an object from the environment where its geometry is hidden or implicitly known. As a simple and versatile tool, mesh is widely used in computer graphics for modelling and visualization. In particular, deformable meshes are meshes which can take the deformation of deformable objects. They extend the modelling ability of meshes. This dissertation focuses on using deformable meshes to approach the 3D shape recovery problem. Several models are presented to solve the challenges for shape recovery under different circumstances. When the object is hidden in an image, a PDE deformable model is designed to extract its surface shape. The algorithm uses a mesh representation so that it can model any non-smooth surface with an arbitrary precision compared to a parametric model. It is more computational efficient than a level-set approach. When the explicit geometry of the object is known but is hidden in a bank of shapes, we simplify the deformation of the model to a graph matching procedure through a hierarchical surface abstraction approach. The framework is used for shape matching and retrieval. This idea is further extended to retain the explicit geometry during the abstraction. A novel motion abstraction framework for deformable meshes is devised based on clustering of local transformations and is successfully applied to 3D motion compression

Advances in Medical Applications of Additive Manufacturing

In the past few decades, additive manufacturing (AM) has been developed and applied as a cost-effective and versatile technique for the fabrication of geometrically complex objects in the medical industry. In this review, we discuss current advances of AM in medical applications for the generation of pharmaceuticals, medical implants, and medical devices. Oral and transdermal drugs can be fabricated by a variety of AM technologies. Different types of hard and soft clinical implants have also been realized by AM, with the goal of producing tissue-engineered constructs. In addition, medical devices used for diagnostics and treatment of various pathological conditions have been developed. The growing body of research on AM reveals its great potential in medical applications. The goal of this review is to highlight the usefulness and elucidate the current limitations of AM applications in the medical field