Universal Memory Architectures for Autonomous Machines

We propose a self-organizing memory architecture (UMA) for perceptual experience provably capable of supporting autonomous learning and goal-directed problem solving in the absence of any prior information about the agent’s environment. The architecture is simple enough to ensure (1) a quadratic bound (in the number of available sensors) on space requirements, and (2) a quadratic bound on the time-complexity of the update-execute cycle. At the same time, it is sufficiently complex to provide the agent with an internal representation which is (3) minimal among all representations which account for every sensory equivalence class consistent with the agent’s belief state; (4) capable, in principle, of recovering a topological model of the problem space; and (5) learnable with arbitrary precision through a random application of the available actions. These provable properties — both the trainability and the operational efficacy of an effectively trained memory structure — exploit a duality between weak poc sets — a symbolic (discrete) representation of subset nesting relations — and non-positively curved cubical complexes, whose rich convexity theory underlies the planning cycle of the proposed architecture

Policy-Based Planning for Robust Robot Navigation

This thesis proposes techniques for constructing and implementing an extensible navigation framework suitable for operating alongside or in place of traditional navigation systems. Robot navigation is only possible when many subsystems work in tandem such as localization and mapping, motion planning, control, and object tracking. Errors in any one of these subsystems can result in the robot failing to accomplish its task, oftentimes requiring human interventions that diminish the benefits theoretically provided by autonomous robotic systems. Our first contribution is Direction Approximation through Random Trials (DART), a method for generating human-followable navigation instructions optimized for followability instead of traditional metrics such as path length. We show how this strategy can be extended to robot navigation planning, allowing the robot to compute the sequence of control policies and switching conditions maximizing the likelihood with which the robot will reach its goal. This technique allows robots to select plans based on reliability in addition to efficiency, avoiding error-prone actions or areas of the environment. We also show how DART can be used to build compact, topological maps of its environments, offering opportunities to scale to larger environments. DART depends on the existence of a set of behaviors and switching conditions describing ways the robot can move through an environment. In the remainder of this thesis, we present methods for learning these behaviors and conditions in indoor environments. To support landmark-based navigation, we show how to train a Convolutional Neural Network (CNN) to distinguish between semantically labeled 2D occupancy grids generated from LIDAR data. By providing the robot the ability to recognize specific classes of places based on human labels, not only do we support transitioning between control laws, but also provide hooks for human-aided instruction and direction. Additionally, we suggest a subset of behaviors that provide DART with a sufficient set of actions to navigate in most indoor environments and introduce a method to learn these behaviors from teleloperated demonstrations. Our method learns a cost function suitable for integration into gradient-based control schemes. This enables the robot to execute behaviors in the absence of global knowledge. We present results demonstrating these behaviors working in several environments with varied structure, indicating that they generalize well to new environments. This work was motivated by the weaknesses and brittleness of many state-of-the-art navigation systems. Reliable navigation is the foundation of any mobile robotic system. It provides access to larger work spaces and enables a wide variety of tasks. Even though navigation systems have continued to improve, catastrophic failures can still occur (e.g. due to an incorrect loop closure) that limit their reliability. Furthermore, as work areas approach the scale of kilometers, constructing and operating on precise localization maps becomes expensive. These limitations prevent large scale deployments of robots outside of controlled settings and laboratory environments. The work presented in this thesis is intended to augment or replace traditional navigation systems to mitigate concerns about scalability and reliability by considering the effects of navigation failures for particular actions. By considering these effects when evaluating the actions to take, our framework can adapt navigation strategies to best take advantage of the capabilities of the robot in a given environment. A natural output of our framework is a topological network of actions and switching conditions, providing compact representations of work areas suitable for fast, scalable planning.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144073/1/rgoeddel_1.pd

A cooperative navigation system with distributed architecture for multiple unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have been widely used in many applications due to, among other features, their versatility, reduced operating cost, and small size. These applications increasingly demand that features related to autonomous navigation be employed, such as mapping. However, the reduced capacity of resources such as, for example, battery and hardware (memory and processing units) can hinder the development of these applications in UAVs. Thus, the collaborative use of multiple UAVs for mapping can be used as an alternative to solve this problem, with a cooperative navigation system. This system requires that individual local maps be transmitted and merged into a global map in a distributed manner. In this scenario, there are two main problems to be addressed: the transmission of maps among the UAVs and the merging of the local maps in each UAV. In this context, this work describes the design, development, and evaluation of a cooperative navigation system with distributed architecture to be used by multiple UAVs. This system uses proposed structures to store the 3D occupancy grid maps. Furthermore, maps are compressed and transmitted between UAVs using algorithms specially proposed for these purposes. Then the local 3D maps are merged in each UAV. In this map merging system, maps are processed before and merged in pairs using suitable algorithms to make them compatible with the 3D occupancy grid map data. In addition, keypoints orientation properties are obtained from potential field gradients. Some proposed filters are used to improve the parameters of the transformations among maps. To validate the proposed solution, simulations were performed in six different environments, outdoors and indoors, and with different layout characteristics. The obtained results demonstrate the effectiveness of thesystemin the construction, sharing, and merging of maps. Still, from the obtained results, the extreme complexity of map merging systems is highlighted.Os veículos aéreos não tripulados (VANTs) têm sidoamplamenteutilizados em muitas aplicações devido, entre outrosrecursos,à sua versatilidade, custo de operação e tamanho reduzidos. Essas aplicações exigem cadavez mais que recursos relacionados à navegaçãoautônoma sejam empregados,como o mapeamento. No entanto, acapacidade reduzida de recursos como, por exemplo, bateria e hardware (memória e capacidade de processamento) podem atrapalhar o desenvolvimento dessas aplicações em VANTs.Assim, o uso colaborativo de múltiplosVANTs para mapeamento pode ser utilizado como uma alternativa para resolvereste problema, criando um sistema de navegaçãocooperativo. Estesistema requer que mapas locais individuais sejam transmitidos efundidos em um mapa global de forma distribuída.Nesse cenário, há doisproblemas principais aserem abordados:a transmissão dosmapas entre os VANTs e afusão dos mapas locais em cada VANT. Nestecontexto, estatese apresentao projeto, desenvolvimento e avaliaçãode um sistema de navegação cooperativo com arquitetura distribuída para ser utilizado pormúltiplos VANTs. Este sistemausa estruturas propostas para armazenaros mapasdegradedeocupação 3D. Além disso, os mapas são compactados e transmitidos entre os VANTs usando os algoritmos propostos. Em seguida, os mapas 3D locais são fundidos em cada VANT. Neste sistemade fusão de mapas, os mapas são processados antes e juntados em pares usando algunsalgoritmos adequados para torná-los compatíveiscom os dados dos mapas da grade de ocupação 3D. Além disso, as propriedadesde orientação dos pontoschave são obtidas a partir de gradientes de campos potenciais. Alguns filtros propostos são utilizadospara melhorar as indicações dos parâmetros dastransformações entre mapas. Paravalidar a aplicação proposta, foram realizadas simulações em seis ambientes distintos, externos e internos, e com características construtivas distintas. Os resultados apresentados demonstram a efetividade do sistema na construção, compartilhamento e fusão dos mapas. Ainda, a partir dos resultados obtidos, destaca-se a extrema complexidade dos sistemas de fusão de mapas

Topological Mapping and Navigation in Real-World Environments

We introduce the Hierarchical Hybrid Spatial Semantic Hierarchy (H2SSH), a hybrid topological-metric map representation. The H2SSH provides a more scalable representation of both small and large structures in the world than existing topological map representations, providing natural descriptions of a hallway lined with offices as well as a cluster of buildings on a college campus. By considering the affordances in the environment, we identify a division of space into three distinct classes: path segments afford travel between places at their ends, decision points present a choice amongst incident path segments, and destinations typically exist at the start and end of routes. Constructing an H2SSH map of the environment requires understanding both its local and global structure. We present a place detection and classification algorithm to create a semantic map representation that parses the free space in the local environment into a set of discrete areas representing features like corridors, intersections, and offices. Using these areas, we introduce a new probabilistic topological simultaneous localization and mapping algorithm based on lazy evaluation to estimate a probability distribution over possible topological maps of the global environment. After construction, an H2SSH map provides the necessary representations for navigation through large-scale environments. The local semantic map provides a high-fidelity metric map suitable for motion planning in dynamic environments, while the global topological map is a graph-like map that allows for route planning using simple graph search algorithms. For navigation, we have integrated the H2SSH with Model Predictive Equilibrium Point Control (MPEPC) to provide safe and efficient motion planning for our robotic wheelchair, Vulcan. However, navigation in human environments entails more than safety and efficiency, as human behavior is further influenced by complex cultural and social norms. We show how social norms for moving along corridors and through intersections can be learned by observing how pedestrians around the robot behave. We then integrate these learned norms with MPEPC to create a socially-aware navigation algorithm, SA-MPEPC. Through real-world experiments, we show how SA-MPEPC improves not only Vulcan’s adherence to social norms, but the adherence of pedestrians interacting with Vulcan as well.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144014/1/collinej_1.pd

Mapping and Semantic Perception for Service Robotics

Para realizar una tarea, los robots deben ser capaces de ubicarse en el entorno. Si un robot no sabe dónde se encuentra, es imposible que sea capaz de desplazarse para alcanzar el objetivo de su tarea. La localización y construcción de mapas simultánea, llamado SLAM, es un problema estudiado en la literatura que ofrece una solución a este problema. El objetivo de esta tesis es desarrollar técnicas que permitan a un robot comprender el entorno mediante la incorporación de información semántica. Esta información también proporcionará una mejora en la localización y navegación de las plataformas robóticas. Además, también demostramos cómo un robot con capacidades limitadas puede construir de forma fiable y eficiente los mapas semánticos necesarios para realizar sus tareas cotidianas.El sistema de construcción de mapas presentado tiene las siguientes características: En el lado de la construcción de mapas proponemos la externalización de cálculos costosos a un servidor en nube. Además, proponemos métodos para registrar información semántica relevante con respecto a los mapas geométricos estimados. En cuanto a la reutilización de los mapas construidos, proponemos un método que combina la construcción de mapas con la navegación de un robot para explorar mejor un entorno y disponer de un mapa semántico con los objetos relevantes para una misión determinada.En primer lugar, desarrollamos un algoritmo semántico de SLAM visual que se fusiona los puntos estimados en el mapa, carentes de sentido, con objetos conocidos. Utilizamos un sistema monocular de SLAM basado en un EKF (Filtro Extendido de Kalman) centrado principalmente en la construcción de mapas geométricos compuestos únicamente por puntos o bordes; pero sin ningún significado o contenido semántico asociado. El mapa no anotado se construye utilizando sólo la información extraída de una secuencia de imágenes monoculares. La parte semántica o anotada del mapa -los objetos- se estiman utilizando la información de la secuencia de imágenes y los modelos de objetos precalculados. Como segundo paso, mejoramos el método de SLAM presentado anteriormente mediante el diseño y la implementación de un método distribuido. La optimización de mapas y el almacenamiento se realiza como un servicio en la nube, mientras que el cliente con poca necesidad de computo, se ejecuta en un equipo local ubicado en el robot y realiza el cálculo de la trayectoria de la cámara. Los ordenadores con los que está equipado el robot se liberan de la mayor parte de los cálculos y el único requisito adicional es una conexión a Internet.El siguiente paso es explotar la información semántica que somos capaces de generar para ver cómo mejorar la navegación de un robot. La contribución en esta tesis se centra en la detección 3D y en el diseño e implementación de un sistema de construcción de mapas semántico.A continuación, diseñamos e implementamos un sistema de SLAM visual capaz de funcionar con robustez en entornos poblados debido a que los robots de servicio trabajan en espacios compartidos con personas. El sistema presentado es capaz de enmascarar las zonas de imagen ocupadas por las personas, lo que aumenta la robustez, la reubicación, la precisión y la reutilización del mapa geométrico. Además, calcula la trayectoria completa de cada persona detectada con respecto al mapa global de la escena, independientemente de la ubicación de la cámara cuando la persona fue detectada.Por último, centramos nuestra investigación en aplicaciones de rescate y seguridad. Desplegamos un equipo de robots en entornos que plantean múltiples retos que implican la planificación de tareas, la planificación del movimiento, la localización y construcción de mapas, la navegación segura, la coordinación y las comunicaciones entre todos los robots. La arquitectura propuesta integra todas las funcionalidades mencionadas, asi como varios aspectos de investigación novedosos para lograr una exploración real, como son: localización basada en características semánticas-topológicas, planificación de despliegue en términos de las características semánticas aprendidas y reconocidas, y construcción de mapas.In order to perform a task, robots need to be able to locate themselves in the environment. If a robot does not know where it is, it is impossible for it to move, reach its goal and complete the task. Simultaneous Localization and Mapping, known as SLAM, is a problem extensively studied in the literature for enabling robots to locate themselves in unknown environments. The goal of this thesis is to develop and describe techniques to allow a service robot to understand the environment by incorporating semantic information. This information will also provide an improvement in the localization and navigation of robotic platforms. In addition, we also demonstrate how a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. The mapping system as built has the following features. On the map building side we propose the externalization of expensive computations to a cloud server. Additionally, we propose methods to register relevant semantic information with respect to the estimated geometrical maps. Regarding the reuse of the maps built, we propose a method that combines map building with robot navigation to better explore a room in order to obtain a semantic map with the relevant objects for a given mission. Firstly, we develop a semantic Visual SLAM algorithm that merges traditional with known objects in the estimated map. We use a monocular EKF (Extended Kalman Filter) SLAM system that has mainly been focused on producing geometric maps composed simply of points or edges but without any associated meaning or semantic content. The non-annotated map is built using only the information extracted from an image sequence. The semantic or annotated parts of the map –the objects– are estimated using the information in the image sequence and the precomputed object models. As a second step we improve the EKF SLAM presented previously by designing and implementing a visual SLAM system based on a distributed framework. The expensive map optimization and storage is allocated as a service in the Cloud, while a light camera tracking client runs on a local computer. The robot’s onboard computers are freed from most of the computation, the only extra requirement being an internet connection. The next step is to exploit the semantic information that we are able to generate to see how to improve the navigation of a robot. The contribution of this thesis is focused on 3D sensing which we use to design and implement a semantic mapping system. We then design and implement a visual SLAM system able to perform robustly in populated environments due to service robots work in environments where people are present. The system is able to mask the image regions occupied by people out of the rigid SLAM pipeline, which boosts the robustness, the relocation, the accuracy and the reusability of the geometrical map. In addition, it estimates the full trajectory of each detected person with respect to the scene global map, irrespective of the location of the moving camera at the point when the people were imaged. Finally, we focus our research on rescue and security applications. The deployment of a multirobot team in confined environments poses multiple challenges that involve task planning, motion planning, localization and mapping, safe navigation, coordination and communications among all the robots. The architecture integrates, jointly with all the above-mentioned functionalities, several novel features to achieve real exploration: localization based on semantic-topological features, deployment planning in terms of the semantic features learned and recognized, and map building.<br /

Autonomía adaptable por predicción individualizada en CARMEN

El objetivo de esta tesis es presentar una arquitectura de control híbrida para sillas de rueda asistivas, completamente centrada en el usuario. La principal novedad de este trabajo es que realiza una adaptación para el usuaro de todo el proceso de navegación mediante el aprendizaje. La población objetivo incluye personas con discapacidades físicas y cognitivas muy diversas. Proporcionar la cantidad justa de asistencia nos permite evitar frustración, pérdida de habilidades residuales y ADLs

Location models for visual place recognition

Cette thèse traite de la cartographie et de la reconnaissance de lieux par vision en robotique mobile. Les recherches menées visent à identifier comment les modèles de localisation peuvent être améliorés en enrichissant les représentations existantes afin de mieux exploiter l'information visuelle disponible. Les problèmes de la cartographie et de la reconnaissance visuelle de lieux présentent un certain nombre de défis : les solutions doivent notamment être robustes vis-à-vis des scènes similaires, des changements de points de vue de d'éclairage, de la dynamique de l'environnement, du bruit des données acquises. La définition de la manière de modéliser et de comparer les observations de lieux est donc un élément crucial de définition d'une solution opérationnelle. Cela passe par la spécification des caractéristiques des images à exploiter, par la définition de la notion de lieu, et par des algorithmes de comparaison des lieux. Dans la littérature, les lieux visuels sont généralement définis par un ensemble ou une séquence d'observations, ce qui ne permet pas de bien traiter des problèmes de similarité de scènes ou de reconnaissance invariante aux déplacements. Dans nos travaux, le modèle d'un lieu exploite la structure d'une scène représentée par des graphes de covisibilité, qui capturent des relations géométriques approximatives entre les points caractéristiques observés. Grâce à cette représentation, un lieu est identifié et reconnu comme un sous-graphe. La reconnaissance de lieux exploite un modèle génératif, dont la sensibilité par rapport aux similarités entre scènes, aux bruits d'observation et aux erreurs de cartographie est analysée. En particulier, les probabilités de reconnaissance sont estimées de manière rigoureuse, rendant la reconnaissance des lieux robuste, et ce pour une complexité algorithme sous-linéaire en le nombre de lieux définis. Enfin les modèles de lieux basés sur des sacs de mots visuels sont étendus pour exploiter les informations structurelles fournies par le graphe de covisibilité, ce qui permet un meilleur compromis entre la qualité et la complexité du processus de reconnaissance.This thesis deals with the task of appearance-based mapping and place recognition for mobile robots. More specifically, this work aims to identify how location models can be improved by exploring several existing and novel location representations in order to better exploit the available visual information. Appearance-based mapping and place recognition presents a number of challenges, including making reliable data-association decisions given repetitive and self-similar scenes (perceptual aliasing), variations in view-point and trajectory, appearance changes due to dynamic elements, lighting changes, and noisy measurements. As a result, choices about how to model and compare observations of locations is crucial to achieving practical results. This includes choices about the types of features extracted from imagery, how to define the extent of a location, and how to compare locations. Along with investigating existing location models, several novel methods are developed in this work. These are developed by incorporating information about the underlying structure of the scene through the use of covisibility graphs which capture approximate geometric relationships between local landmarks in the scene by noting which ones are observed together. Previously, the range of a location generally varied between either using discrete poses or loosely defined sequences of poses, facing problems related to perceptual aliasing and trajectory invariance respectively. Whereas by working with covisibility graphs, scenes are dynamically retrieved as clusters from the graph in a way which adapts to the environmental structure and given query. The probability of a query observation coming from a previously seen location is then obtained by applying a generative model such that the uniqueness of an observation is accounted for. Behaviour with respect to observation errors, mapping errors, perceptual aliasing, and parameter sensitivity are examined, motivating the use of a novel normalization scheme and observation likelihoods representations. The normalization method presented in this work is robust to redundant locations in the map (from missed loop-closures, for example), and results in place recognition which now has sub-linear complexity in the number of locations in the map. Beginning with bag-of-words representations of locations, location models are extended in order to include more discriminative structural information from the covisibility map. This results in various representations ranging between unstructured sets of features and full graphs of features, providing a tradeoff between complexity and recognition performance

Learning of Unknown Environments in Goal-Directed Guidance and Navigation Tasks: Autonomous Systems and Humans

University of Minnesota Ph.D. dissertation. December 2017. Major: Aerospace Engineering. Advisor: Berenice Mettler. 1 computer file (PDF); xvi, 176 pages.Guidance and navigation in unknown environments requires learning of the task environment simultaneous to path planning. Autonomous guidance in unknown environments requires a real-time integration of environment sensing, mapping, planning, trajectory generation, and tracking. For brute force optimal control, the spatial environment should be mapped accurately. The real-world environments are in general cluttered, complex, unknown, and uncertain. An accurate model of such environments requires to store an enormous amount of information and then that information has to be processed in optimal control formulation, which is not computationally cheap and efficient for online operations of autonomous guidance systems. On the contrary, humans and animals are in general able to navigate efficiently in unknown, complex, and cluttered environments. Like autonomous guidance systems, humans and animals also do not have unlimited information processing and sensing capacities due to their biological and physical constraints. Therefore, it is relevant to understand cognitive mechanisms that help humans learn and navigate efficiently in unknown environments. Such understanding can help to design planning algorithms that are computationally efficient as well as better understand how to improve human-machine interfaces in particular between operators and autonomous agents. This dissertation is organized in three parts: 1) computational investigation of environment learning in guidance and navigation (chapters 3 and 4), 2) investigation of human environment learning in guidance tasks (chapters 5 and 6), and 3) autonomous guidance framework based on a graph representation of environment using subgoals that are invariants in agent-environment interactions (chapter 7). In the first part, the dissertation presents a computational framework for learning autonomous guidance behavior in unknown or partially known environments. The learning framework uses a receding horizon trajectory optimization associated with a spatial value function (SVF). The SVF describes optimal (e.g. minimum time) guidance behavior represented as cost and velocity at any point in geographical space to reach a specified goal state. For guidance in unknown environments, a local SVF based on current vehicle state is updated online using environment data from onboard exteroceptive sensors. The proposed learning framework has the advantage in that it learns information directly relevant to the optimal guidance and control behavior enabling optimal trajectory planning in unknown or partially known environments. The learning framework is evaluated by measuring performance over successive runs in a 3-D indoor flight simulation. The test vehicle in the simulations is a Blade-Cx2 coaxial miniature helicopter. The environment is a priori unknown to the learning system. The dissertation investigates changes in performance, dynamic behavior, SVF, and control behavior in body frame, as a result of learning over successive runs. In the second part, the dissertation focuses on modeling and evaluating how a human operator learns an unknown task environment in goal-directed navigation tasks. Previous studies have showed that human pilots organize their guidance and perceptual behavior using the interaction patterns (IPs), i.e., invariants in their sensory-motor processes in interactions with the task space. However, previous studies were performed in known environments. In this dissertation, the concept of IPs is used to build a modeling and analysis framework to investigate human environment learning and decision-making in navigation of unknown environments. This approach emphasizes the agent dynamics (e.g., a vehicle controlled by a human operator), which is not typical in simultaneous navigation and environment learning studies. The framework is applied to analyze human data from simulated first-person guidance experiments in an obstacle field. Subjects were asked to perform multiple trials and find minimum-time routes between prespecified start and goal locations without priori knowledge of the environment. They used a joystick to control flight behavior and navigate in the environment. In the third part, the subgoal graph framework used to model and evaluate humans is extended to an autonomous guidance algorithm for navigation in unknown environments. The autonomous guidance framework based on subgoal graph is an improvement to the SVF based guidance and learning framework presented in the first part. The latter uses a grid representation of the environment, which is computationally costly in comparison to the graph based guidance model