Automated generation of geometrically-precise and semantically-informed virtual geographic environnements populated with spatially-reasoning agents

La Géo-Simulation Multi-Agent (GSMA) est un paradigme de modélisation et de simulation de phénomènes dynamiques dans une variété de domaines d'applications tels que le domaine du transport, le domaine des télécommunications, le domaine environnemental, etc. La GSMA est utilisée pour étudier et analyser des phénomènes qui mettent en jeu un grand nombre d'acteurs simulés (implémentés par des agents) qui évoluent et interagissent avec une représentation explicite de l'espace qu'on appelle Environnement Géographique Virtuel (EGV). Afin de pouvoir interagir avec son environnement géographique qui peut être dynamique, complexe et étendu (à grande échelle), un agent doit d'abord disposer d'une représentation détaillée de ce dernier. Les EGV classiques se limitent généralement à une représentation géométrique du monde réel laissant de côté les informations topologiques et sémantiques qui le caractérisent. Ceci a pour conséquence d'une part de produire des simulations multi-agents non plausibles, et, d'autre part, de réduire les capacités de raisonnement spatial des agents situés. La planification de chemin est un exemple typique de raisonnement spatial dont un agent pourrait avoir besoin dans une GSMA. Les approches classiques de planification de chemin se limitent à calculer un chemin qui lie deux positions situées dans l'espace et qui soit sans obstacle. Ces approches ne prennent pas en compte les caractéristiques de l'environnement (topologiques et sémantiques), ni celles des agents (types et capacités). Les agents situés ne possèdent donc pas de moyens leur permettant d'acquérir les connaissances nécessaires sur l'environnement virtuel pour pouvoir prendre une décision spatiale informée. Pour répondre à ces limites, nous proposons une nouvelle approche pour générer automatiquement des Environnements Géographiques Virtuels Informés (EGVI) en utilisant les données fournies par les Systèmes d'Information Géographique (SIG) enrichies par des informations sémantiques pour produire des GSMA précises et plus réalistes. De plus, nous présentons un algorithme de planification hiérarchique de chemin qui tire avantage de la description enrichie et optimisée de l'EGVI pour fournir aux agents un chemin qui tient compte à la fois des caractéristiques de leur environnement virtuel et de leurs types et capacités. Finalement, nous proposons une approche pour la gestion des connaissances sur l'environnement virtuel qui vise à supporter la prise de décision informée et le raisonnement spatial des agents situés

Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local observations are matched to a general tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 2100\,m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12\,cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and speed limit is realistic during forest operations

Simplex Control Methods for Robust Convergence of Small Unmanned Aircraft Flight Trajectories in the Constrained Urban Environment

Constrained optimal control problems for Small Unmanned Aircraft Systems (SUAS) have long suffered from excessive computation times caused by a combination of constraint modeling techniques, the quality of the initial path solution provided to the optimal control solver, and improperly defining the bounds on system state variables, ultimately preventing implementation into real-time, on-board systems. In this research, a new hybrid approach is examined for real-time path planning of SUAS. During autonomous flight, a SUAS is tasked to traverse from one target region to a second target region while avoiding hard constraints consisting of building structures of an urban environment. Feasible path solutions are determined through highly constrained spaces, investigating narrow corridors, visiting multiple waypoints, and minimizing incursions to keep-out regions. These issues are addressed herein with a new approach by triangulating the search space in two-dimensions, or using a tetrahedron discretization in three-dimensions to define a polygonal search corridor free of constraints while alleviating the dependency of problem specific parameters by translating the problem to barycentric coordinates. Within this connected simplex construct, trajectories are solved using direct orthogonal collocation methods while leveraging navigation mesh techniques developed for fast geometric path planning solutions. To illustrate two-dimensional flight trajectories, sample results are applied to flight through downtown Chicago at an altitude of 600 feet above ground level. The three-dimensional problem is examined for feasibility by applying the methodology to a small scale problem. Computation and objective times are reported to illustrate the design implications for real-time optimal control systems, with results showing 86% reduction in computation time over traditional methods

Efficient Solutions in Path Planning for Autonomous Mobile Robots

Fecha de lectura de Tesis: 25 julio 2018.Los robots, máquinas que desempeñan un abanico de tareas de lo más variopinto. Desde realizar tareas muy específicas en cadenas de montaje hasta desempeñar la mayoría de labores cotidianas que los seres humanos tenemos que afrontar cada día. Como se puede intuir, para esto se necesitan no solo máquinas, sino máquinas dotadas de cierta inteligencia, que surge de la necesidad de que las máquinas abandonen su estatismo y monotonía para comenzar a enfrentarse a un mundo dinámico y ambiguo: nuestro mundo. El principal desencadenante que ha llevado al ser humano a dotar de inteligencia y movilidad a las máquinas es su afán de dominar y, al mismo tiempo, liberarse de un entorno cada vez más estresante. Hay dos aspectos irrefutables que marcan la versatilidad de una máquina: su inteligencia y su movilidad. Hablando de robótica y movilidad surge el problema de cómo y por dónde debe moverse un robot para alcanzar un determinado objetivo sin comprometer su integridad física. Como el significado de moverse puede ser muy amplio, aquí hablaremos de desplazamiento, en el sentido literal de viajar. Y cuando viajamos a algún lugar siempre nos preguntamos lo siguiente: ¿Por dónde vamos? y ¿Cuál es el la mejor alternativa?. Esta problemática, en robótica, se conoce como el problema del Path Planning. En esta tesis doctoral se aborda, de manera innovadora y altamente paralela, el problema del Path Planing sobre mapas reales extensos en un contexto de tiempo real. Este grado de paralelismo se consigue gracias al uso intensivo de las populares GPU (Unidad Gráfica de Procesamiento) y de los bien conocidos chips multi-core. Pero aquí no solo se aborda el problema del Path Planning desde un punto de vista altamente paralelo sino que, de manera transversal, también se aborda desde un punto de vista inteligente aplicando metaheurísticas

Microscope Embedded Neurosurgical Training and Intraoperative System

In the recent years, neurosurgery has been strongly influenced by new technologies. Computer Aided Surgery (CAS) offers several benefits for patients\u27 safety but fine techniques targeted to obtain minimally invasive and traumatic treatments are required, since intra-operative false movements can be devastating, resulting in patients deaths. The precision of the surgical gesture is related both to accuracy of the available technological instruments and surgeon\u27s experience. In this frame, medical training is particularly important. From a technological point of view, the use of Virtual Reality (VR) for surgeon training and Augmented Reality (AR) for intra-operative treatments offer the best results. In addition, traditional techniques for training in surgery include the use of animals, phantoms and cadavers. The main limitation of these approaches is that live tissue has different properties from dead tissue and that animal anatomy is significantly different from the human. From the medical point of view, Low-Grade Gliomas (LGGs) are intrinsic brain tumours that typically occur in younger adults. The objective of related treatment is to remove as much of the tumour as possible while minimizing damage to the healthy brain. Pathological tissue may closely resemble normal brain parenchyma when looked at through the neurosurgical microscope. The tactile appreciation of the different consistency of the tumour compared to normal brain requires considerable experience on the part of the neurosurgeon and it is a vital point. The first part of this PhD thesis presents a system for realistic simulation (visual and haptic) of the spatula palpation of the LGG. This is the first prototype of a training system using VR, haptics and a real microscope for neurosurgery. This architecture can be also adapted for intra-operative purposes. In this instance, a surgeon needs the basic setup for the Image Guided Therapy (IGT) interventions: microscope, monitors and navigated surgical instruments. The same virtual environment can be AR rendered onto the microscope optics. The objective is to enhance the surgeon\u27s ability for a better intra-operative orientation by giving him a three-dimensional view and other information necessary for a safe navigation inside the patient. The last considerations have served as motivation for the second part of this work which has been devoted to improving a prototype of an AR stereoscopic microscope for neurosurgical interventions, developed in our institute in a previous work. A completely new software has been developed in order to reuse the microscope hardware, enhancing both rendering performances and usability. Since both AR and VR share the same platform, the system can be referred to as Mixed Reality System for neurosurgery. All the components are open source or at least based on a GPL license

A GROWTH-BASED APPROACH TO THE AUTOMATIC GENERATION OF NAVIGATION MESHES

Providing an understanding of space in game and simulation environments is one of the major challenges associated with moving artificially intelligent characters through these environments. The usage of some form of navigation mesh has become the standard method to provide a representation of the walkable space in game environments to characters moving around in that environment. There is currently no standardized best method of producing a navigation mesh. In fact, producing an optimal navigation mesh has been shown to be an NP-Hard problem. Current approaches are a patchwork of divergent methods all of which have issues either in the time to create the navigation meshes (e.g., the best looking navigation meshes have traditionally been produced by hand which is time consuming), generate substandard quality navigation meshes (e.g., many of the automatic mesh production algorithms result in highly triangulated meshes that pose problems for character navigation), or yield meshes that contain gaps of areas that should be included in the mesh and are not (e.g., existing growth-based methods are unable to adapt to non-axis-aligned geometry and as such tend to provide a poor representation of the walkable space in complex environments). We introduce the Planar Adaptive Space Filling Volumes (PASFV) algorithm, Volumetric Adaptive Space Filling Volumes (VASFV) algorithm, and the Iterative Wavefront Edge Expansion Cell Decomposition (Wavefront) algorithm. These algorithms provide growth-based spatial decompositions for navigation mesh generation in either 2D (PASFV) or 3D (VASFV). These algorithms generate quick (on demand) decompositions (Wavefront), use quad/cube base spatial structures to provide more regular regions in the navigation mesh instead of triangles, and offer full coverage decompositions to avoid gaps in the navigation mesh by adapting to non-axis-aligned geometry. We have shown experimentally that the decompositions offered by PASFV and VASFV are superior both in character navigation ability, number of regions, and coverage in comparison to the existing and commonly used techniques of Space Filling Volumes, Hertel-Melhorn decomposition, Delaunay Triangulation, and Automatic Path Node Generation. Finally, we show that our Wavefront algorithm retains the superior performance of the PASFV and VASFV algorithms while providing faster decompositions that contain fewer degenerate and near degenerate regions. Unlike traditional navigation mesh generation techniques, the PASFV and VASFV algorithms have a real time extension (Dynamic Adaptive Space Filling Volumes, DASFV) which allows the navigation mesh to adapt to changes in the geometry of the environment at runtime. In addition, it is possible to use a navigation mesh for applications above and beyond character path planning and navigation. These multiple uses help to increase the return on the investment in creating a navigation mesh for a game or simulation environment. In particular, we will show how to use a navigation mesh for the acceleration of collision detection