Asymptotically-Optimal Topological Nearest-Neighbor Filtering

Nearest-neighbor finding is a major bottleneck for sampling-based motion planning algorithms. The cost of finding nearest neighbors grows with the size of the roadmap, leading to a significant computational bottleneck for problems which require many configurations to find a solution. In this work, we develop a method of mapping configurations of a jointed robot to neighborhoods in the workspace that supports fast search for configurations in nearby neighborhoods. This expedites nearest-neighbor search by locating a small set of the most likely candidates for connecting to the query with a local plan. We show that this filtering technique can preserve asymptotically-optimal guarantees with modest requirements on the distance metric. We demonstrate the method’s efficacy in planning problems for rigid bodies and both fixed and mobile-base manipulators

Improving Sampling-Based Motion Planning Using Library of Trajectories

Plánování pohybu je jedním z podstatných problémů robotiky. Tato práce kombinuje pokroky v plánování pohybu a hodnocení podobnosti objektů za účelem zrychlení plánování ve statických prostředích. První část této práce pojednává o současných metodách používaných pro hodnocení podobnosti objektů a plánování pohybu. Prostřední část popisuje, jak jsou tyto metody použity pro zrychlení plánování s využitím získaných znalostí o prostředí. V poslední části jsou navržené metody porovnány s ostatními plánovači v nezávislém testu. Námi navržené algoritmy se v experimentech ukázaly být často rychlejší v porovnání s ostatními plánovači. Také často nacházely cesty v prostředích, kde ostatní plánovače nebyly schopny cestu nalézt.Motion planning is one of the fundamental problems in robotics. This thesis combines the advances in motion planning and shape matching to improve planning speeds in static environments. The first part of this thesis covers current methods used in object similarity evaluation and motion planning. The middle part describes how these methods are used together to improve planning speeds by utilizing prior knowledge about the environment, along with additional modifications. In the last part, the proposed methods are tested against other state-of-the-art planners in an independent benchmarking facility. The proposed algorithms are shown to be faster than other planners in many cases, often finding paths in environments where the other planners are unable to

3D mapping and path planning from range data

This thesis reports research on mapping, terrain classification and path planning. These are classical problems in robotics, typically studied independently, and here we link such problems by framing them within a common proprioceptive modality, that of three-dimensional laser range scanning. The ultimate goal is to deliver navigation paths for challenging mobile robotics scenarios. For this reason we also deliver safe traversable regions from a previously computed globally consistent map. We first examine the problem of registering dense point clouds acquired at different instances in time. We contribute with a novel range registration mechanism for pairs of 3D range scans using point-to-point and point-to-line correspondences in a hierarchical correspondence search strategy. For the minimization we adopt a metric that takes into account not only the distance between corresponding points, but also the orientation of their relative reference frames. We also propose FaMSA, a fast technique for multi-scan point cloud alignment that takes advantage of the asserted point correspondences during sequential scan matching, using the point match history to speed up the computation of new scan matches. To properly propagate the model of the sensor noise and the scan matching, we employ first order error propagation, and to correct the error accumulation from local data alignment, we consider the probabilistic alignment of 3D point clouds using a delayed-state Extended Information Filter (EIF). In this thesis we adapt the Pose SLAM algorithm to the case of 3D range mapping, Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and where sensor data is solely used to produce relative constraints between robot poses. These dense mapping techniques are tested in several scenarios acquired with our 3D sensors, producing impressively rich 3D environment models. The computed maps are then processed to identify traversable regions and to plan navigation sequences. In this thesis we present a pair of methods to attain high-level off-line classification of traversable areas, in which training data is acquired automatically from navigation sequences. Traversable features came from the robot footprint samples during manual robot motion, allowing us to capture terrain constrains not easy to model. Using only some of the traversed areas as positive training samples, our algorithms are tested in real scenarios to find the rest of the traversable terrain, and are compared with a naive parametric and some variants of the Support Vector Machine. Later, we contribute with a path planner that guarantees reachability at a desired robot pose with significantly lower computation time than competing alternatives. To search for the best path, our planner incrementally builds a tree using the A* algorithm, it includes a hybrid cost policy to efficiently expand the search tree, combining random sampling from the continuous space of kinematically feasible motion commands with a cost to goal metric that also takes into account the vehicle nonholonomic constraints. The planer also allows for node rewiring, and to speed up node search, our method includes heuristics that penalize node expansion near obstacles, and that limit the number of explored nodes. The method book-keeps visited cells in the configuration space, and disallows node expansion at those configurations in the first full iteration of the algorithm. We validate the proposed methods with experiments in extensive real scenarios from different very complex 3D outdoors environments, and compare it with other techniques such as the A*, RRT and RRT* algorithms.Esta tesis reporta investigación sobre el mapeo, clasificación de terreno y planificación de trayectorias. Estos son problemas clásicos en robótica los cuales generalmente se estudian de forma independiente, aquí se vinculan enmarcandolos con una modalidad propioceptiva común: un láser de rango 3D. El objetivo final es ofrecer trayectorias de navegación para escenarios complejos en el marco de la robótica móvil. Por esta razón también entregamos regiones transitables en un mapa global consistente calculado previamente. Primero examinamos el problema de registro de nubes de puntos adquiridas en diferentes instancias de tiempo. Contribuimos con un novedoso mecanismo de registro de pares de imagenes de rango 3D usando correspondencias punto a punto y punto a línea, en una estrategia de búsqueda de correspondencias jerárquica. Para la minimización optamos por una metrica que considera no sólo la distancia entre puntos, sino también la orientación de los marcos de referencia relativos. También proponemos FAMSA, una técnica para el registro rápido simultaneo de multiples nubes de puntos, la cual aprovecha las correspondencias de puntos obtenidas durante el registro secuencial, usando inicialmente la historia de correspondencias para acelerar el cálculo de las correspondecias en los nuevos registros de imagenes. Para propagar adecuadamente el modelo del ruido del sensor y del registro de imagenes, empleamos la propagación de error de primer orden, y para corregir el error acumulado del registro local, consideramos la alineación probabilística de nubes de puntos 3D utilizando un Filtro Extendido de Información de estados retrasados. En esta tesis adaptamos el algóritmo Pose SLAM para el caso de mapas con imagenes de rango 3D, Pose SLAM es la variante de SLAM donde solamente se estima la trayectoria del robot, usando los datos del sensor como restricciones relativas entre las poses robot. Estas técnicas de mapeo se prueban en varios escenarios adquiridos con nuestros sensores 3D produciendo modelos 3D impresionantes. Los mapas obtenidos se procesan para identificar regiones navegables y para planificar secuencias de navegación. Presentamos un par de métodos para lograr la clasificación de zonas transitables fuera de línea. Los datos de entrenamiento se adquieren de forma automática usando secuencias de navegación obtenidas manualmente. Las características transitables se captan de las huella de la trayectoria del robot, lo cual permite capturar restricciones del terreno difíciles de modelar. Con sólo algunas de las zonas transitables como muestras de entrenamiento positivo, nuestros algoritmos se prueban en escenarios reales para encontrar el resto del terreno transitable. Los algoritmos se comparan con algunas variantes de la máquina de soporte de vectores (SVM) y una parametrizacion ingenua. También, contribuimos con un planificador de trayectorias que garantiza llegar a una posicion deseada del robot en significante menor tiempo de cálculo a otras alternativas. Para buscar el mejor camino, nuestro planificador emplea un arbol de busqueda incremental basado en el algoritmo A*. Incluimos una póliza de coste híbrido para crecer de manera eficiente el árbol, combinando el muestro aleatorio del espacio continuo de comandos cinemáticos del robot con una métrica de coste al objetivo que también concidera las cinemática del robot. El planificador además permite reconectado de nodos, y, para acelerar la búsqueda de nodos, se incluye una heurística que penaliza la expansión de nodos cerca de los obstáculos, que limita el número de nodos explorados. El método conoce las céldas que ha visitado del espacio de configuraciones, evitando la expansión de nodos en configuraciones que han sido vistadas en la primera iteración completa del algoritmo. Los métodos propuestos se validán con amplios experimentos con escenarios reales en diferentes entornos exteriores, asi como su comparación con otras técnicas como los algoritmos A*, RRT y RRT*.Postprint (published version

Interoperability of Traffic Infrastructure Planning and Geospatial Information Systems

Building Information Modelling (BIM) as a Model-based design facilitates to investigate multiple solutions in the infrastructure planning process. The most important reason for implementing model-based design is to help designers and to increase communication between different design parties. It decentralizes and coordinates team collaboration and facilitates faster and lossless project data exchange and management across extended teams and external partners in project lifecycle. Infrastructure are fundamental facilities, services, and installations needed for the functioning of a community or society, such as transportation, roads, communication systems, water and power networks, as well as power plants. Geospatial Information Systems (GIS) as the digital representation of the world are systems for maintaining, managing, modelling, analyzing, and visualizing of the world data including infrastructure. High level infrastructure suits mostly facilitate to analyze the infrastructure design based on the international or user defined standards. Called regulation1-based design, this minimizes errors, reduces costly design conflicts, increases time savings and provides consistent project quality, yet mostly in standalone solutions. Tasks of infrastructure usually require both model based and regulation based design packages. Infrastructure tasks deal with cross-domain information. However, the corresponding data is split in several domain models. Besides infrastructure projects demand a lot of decision makings on governmental as well as on private level considering different data models. Therefore lossless flow of project data as well as documents like regulations across project team, stakeholders, governmental and private level is highly important. Yet infrastructure projects have largely been absent from product modelling discourses for a long time. Thus, as will be explained in chapter 2 interoperability is needed in infrastructure processes. Multimodel (MM) is one of the interoperability methods which enable heterogeneous data models from various domains get bundled together into a container keeping their original format. Existing interoperability methods including existing MM solutions can’t satisfactorily fulfill the typical demands of infrastructure information processes like dynamic data resources and a huge amount of inter model relations. Therefore chapter 3 concept of infrastructure information modelling investigates a method for loose and rule based coupling of exchangeable heterogeneous information spaces. This hypothesis is an extension for the existing MM to a rule-based Multimodel named extended Multimodel (eMM) with semantic rules – instead of static links. The semantic rules will be used to describe relations between data elements of various models dynamically in a link-database. Most of the confusion about geospatial data models arises from their diversity. In some of these data models spatial IDs are the basic identities of entities and in some other data models there are no IDs. That is why in the geospatial data, data structure is more important than data models. There are always spatial indexes that enable accessing to the geodata. The most important unification of data models involved in infrastructure projects is the spatiality. Explained in chapter 4 the method of infrastructure information modelling for interoperation in spatial domains generate interlinks through spatial identity of entities. Match finding through spatial links enables any kind of data models sharing spatial property get interlinked. Through such spatial links each entity receives the spatial information from other data models which is related to the target entity due to sharing equivalent spatial index. This information will be the virtual properties for the object. The thesis uses Nearest Neighborhood algorithm for spatial match finding and performs filtering and refining approaches. For the abstraction of the spatial matching results hierarchical filtering techniques are used for refining the virtual properties. These approaches focus on two main application areas which are product model and Level of Detail (LoD). For the eMM suggested in this thesis a rule based interoperability method between arbitrary data models of spatial domain has been developed. The implementation of this method enables transaction of data in spatial domains run loss less. The system architecture and the implementation which has been applied on the case study of this thesis namely infrastructure and geospatial data models are described in chapter 5. Achieving afore mentioned aims results in reducing the whole project lifecycle costs, increasing reliability of the comprehensive fundamental information, and consequently in independent, cost-effective, aesthetically pleasing, and environmentally sensitive infrastructure design.:ABSTRACT 4 KEYWORDS 7 TABLE OF CONTENT 8 LIST OF FIGURES 9 LIST OF TABLES 11 LIST OF ABBREVIATION 12 INTRODUCTION 13 1.1. A GENERAL VIEW 14 1.2. PROBLEM STATEMENT 15 1.3. OBJECTIVES 17 1.4. APPROACH 18 1.5. STRUCTURE OF THESIS 18 INTEROPERABILITY IN INFRASTRUCTURE ENGINEERING 20 2.1. STATE OF INTEROPERABILITY 21 2.1.1. Interoperability of GIS and BIM 23 2.1.2. Interoperability of GIS and Infrastructure 25 2.2. MAIN CHALLENGES AND RELATED WORK 27 2.3. INFRASTRUCTURE MODELING IN GEOSPATIAL CONTEXT 29 2.3.1. LamdXML: Infrastructure Data Standards 32 2.3.2. CityGML: Geospatial Data Standards 33 2.3.3. LandXML and CityGML 36 2.4. INTEROPERABILITY AND MULTIMODEL TECHNOLOGY 39 2.5. LIMITATIONS OF EXISTING APPROACHES 41 INFRASTRUCTURE INFORMATION MODELLING 44 3.1. MULTI MODEL FOR GEOSPATIAL AND INFRASTRUCTURE DATA MODELS 45 3.2. LINKING APPROACH, QUERYING AND FILTERING 48 3.2.1. Virtual Properties via Link Model 49 3.3. MULTI MODEL AS AN INTERDISCIPLINARY METHOD 52 3.4. USING LEVEL OF DETAIL (LOD) FOR FILTERING 53 SPATIAL MODELLING AND PROCESSING 58 4.1. SPATIAL IDENTIFIERS 59 4.1.1. Spatial Indexes 60 4.1.2. Tree-Based Spatial Indexes 61 4.2. NEAREST NEIGHBORHOOD AS A BASIC LINK METHOD 63 4.3. HIERARCHICAL FILTERING 70 4.4. OTHER FUNCTIONAL LINK METHODS 75 4.5. ADVANCES AND LIMITATIONS OF FUNCTIONAL LINK METHODS 76 IMPLEMENTATION OF THE PROPOSED IIM METHOD 77 5.1. IMPLEMENTATION 78 5.2. CASE STUDY 83 CONCLUSION 89 6.1. SUMMERY 90 6.2. DISCUSSION OF RESULTS 92 6.3. FUTURE WORK 93 BIBLIOGRAPHY 94 7.1. BOOKS AND PAPERS 95 7.2. WEBSITES 10

Near-Optimal Motion Planning Algorithms Via A Topological and Geometric Perspective

Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among motion planning paradigms because they capture the topology of space into a roadmap. These planners have successfully solved high-dimensional planning problems with a probabilistic-complete guarantee, i.e., it guarantees to find a path if one exists as the number of vertices goes to infinity. Despite their progress, these methods have failed to optimize the sub-region information of the environment for reuse by other planners. This results in re-planning overhead at each execution, affecting the performance complexity for computation time and memory space usage. In this research, we address the problem by focusing on the theoretical foundation of the algorithmic approach that leverages the strengths of sampling-based motion planners and the Topological Data Analysis methods to extract intricate properties of the environment. The work contributes a novel algorithm to overcome the performance shortcomings of existing motion planners by capturing and preserving the essential topological and geometric features to generate a homotopy-equivalent roadmap of the environment. This roadmap provides a mathematically rich representation of the environment, including an approximate measure of the collision-free space. In addition, the roadmap graph vertices sampled close to the obstacles exhibit advantages when navigating through narrow passages and cluttered environments, making obstacle-avoidance path planning significantly more efficient. The application of the proposed algorithms solves motion planning problems, such as sub-optimal planning, diverse path planning, and fault-tolerant planning, by demonstrating the improvement in computational performance and path quality. Furthermore, we explore the potential of these algorithms in solving computational biology problems, particularly in finding optimal binding positions for protein-ligand or protein-protein interactions. Overall, our work contributes a new way to classify routes in higher dimensional space and shows promising results for high-dimensional robots, such as articulated linkage robots. The findings of this research provide a comprehensive solution to motion planning problems and offer a new perspective on solving computational biology problems

Trajectory bundle estimation For perception-driven planning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 113-122).When operating in unknown environments, autonomous vehicles must perceive and understand the environment ahead in order to make effective navigation decisions. Long range perception can enable a vehicle to choose actions that take it directly toward its goal, avoiding dead ends. In addition, the perception range is critically important for ensuring the safety of vehicles with constrained dynamics. In general, the faster a vehicle moves, the more constrained its dynamics become due to acceleration limits imposed by its actuators. This means that the speed at which an autonomous agent can safely travel is often governed by its ability to perceive and understand the environment ahead. Overall, perception range is one of the most important factors that determines the performance of an autonomous vehicle. Today, autonomous vehicles tend to rely exclusively on metric representations built using range sensors to plan paths. However, such sensors are limited by their maximum range, field of view, and occluding obstacles in the foreground. Together, these limitations make up what we call the metric sensing horizon of the vehicle. The first two limitations are generally determined by the weight, size, power, and cost budget allocated to sensing. However, range sensors will always be limited by occlusions. If we wish to develop autonomous vehicles that are able to navigate directly toward a goal at high speeds through unknown environments, then we must move beyond the simple range-sensor based techniques. We must develop algorithms that enable autonomous agents to harness knowledge about the structure of the world to interpret additional sensor information (such as appearance information provided by cameras), and make inferences about parts of the world that cannot be directly observed. We develop a new representation based around trajectory bundles, that makes this challenging task more tractable. Rather than attempt to explicitly model the geometry of the world in front of the vehicle (which can be incredibly complex), we reason about the world in terms of what the vehicle can and cannot do. Trajectory bundles are designed to capture an abstract concept such as the command "go straight and then turn towards the right" in a concrete and actionable manner. We employ a library of trajectory bundles to reason about the layout of obstacles in the environment based on which bundles in the library are predicted to be feasible. Trajectory bundles provide a lens through which we can look at perception tasks, allowing us to leverage machine learning tools in much more effective ways for navigation. In this thesis we introduce trajectory bundles, and develop algorithms that use them to enable perception-driven planning. We develop a trajectory clustering algorithm that enables us to construct a set of trajectory bundles. We then develop a Bayesian filtering framework that enables us to estimate a belief over which trajectory bundles are feasible based on the history of actions and observations of the vehicle. We test our algorithms by using them to navigate a simulated fixed wing air vehicle at high speeds through an unknown environment using a monocular camera sensor.by Abraham Galton Bachrach.Ph.D

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available