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

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Hybrid mapping for static and non-static indoor environments

Mención Internacional en el título de doctorIndoor environments populated by humans, such as houses, offices or universities, involve a great complexity due to the diversity of geometries and situations that they may present. Apart from the size of the environment, they can contain multiple rooms distributed into floors and corridors, repetitive structures and loops, and they can get as complicated as one can imagine. In addition, the structure and situations that the environment present may vary over time as objects could be moved, doors can be frequently opened or closed and places can be used for different purposes. Mobile robots need to solve these challenging situations in order to successfully operate in the environment. The main tools that a mobile robot has for dealing with these situations relate to navigation and perception and comprise mapping, localization, path planning and map adaptation. In this thesis, we try to address some of the open problems in robot navigation in non-static indoor environments. We focus on house-like environments as the work is framed into the HEROITEA research project that aims attention at helping elderly people with their everyday-life activities at their homes. This thesis contributes to HEROITEA with a complete robotic mapping system and map adaptation that grants safe navigation and understanding of the environment. Moreover, we provide localization and path planning strategies within the resulting map to further operate in the environment. The first problem tackled in this thesis is robot mapping in static indoor environments. We propose a hybrid mapping method that structures the information gathered from the environment into several maps. The hybrid map contains diverse knowledge of the environment such as its structure, the navigable and blocked paths, and semantic knowledge, such as the objects or scenes in the environment. All this information is separated into different components of the hybrid map that are interconnected so the system can, at any time, benefit from the information contained in every component. In addition to the conceptual conception of the hybrid map, we have also developed building procedures and an exploration algorithm to autonomous build the hybrid map. However, indoor environments populated by humans are far from being static as the environment may change over time. For this reason, the second problem tackled in this thesis is the adaptation of the map to non-static environments. We propose an object-based probabilistic map adaptation that calculates the likelihood of moving or remaining in its place for the different objects in the environment. Finally, a map is just a description of the environment whose importance is mostly related to how the map is used. In addition, map representations are more valuable as long as they offer a wider range of applications. Therefore, the third problem that we approach in this thesis is exploiting the intrinsic characteristics of the hybrid map in order to enhance the performance of localization and path planning methods. The particular objectives of these approaches are precision for robot localization and efficiency for path planning in terms of execution time and traveled distance. We evaluate our proposed methods in a diversity of simulated and real-world indoor environments. In this extensive evaluation, we show that hybrid maps can be efficiently built and maintained over time and they open up for new possibilities for localization and path planning. In this thesis, we show an increase in localization precision and robustness and an improvement in path planning performance. In sum, this thesis makes several contributions in the context of robot navigation in indoor environments, and especially in hybrid mapping. Hybrid maps offer higher efficiency during map building and other applications such as localization and path planning. In addition, we highlight the necessity of dealing with the dynamics of indoor environments and the benefits of combining topological, semantic and metric information to the autonomy of a mobile robot.Los entornos de interiores habitados por personas, como casas, oficinas o universidades, entrañan una gran complejidad por la diversidad de geometrías y situaciones que pueden ocurrir. Aparte de las diferencias en tamaño, estos entornos pueden contener muchas habitaciones organizadas en diferentes plantas o pasillos, pueden presentar estructuras repetitivas o bucles de tal forma que los entornos pueden llegar a ser tan complejos como uno se pueda imaginar. Además, la estructura y el estado del entorno pueden variar con el tiempo, ya que los objetos pueden moverse, las puertas pueden estar cerradas o abiertas y diferentes espacios pueden ser usados para diferentes propósitos. Los robots móviles necesitan resolver estas situaciones difíciles para poder funcionar de una forma satisfactoria. Las principales herramientas que tiene un robot móvil para manejar estas situaciones están relacionadas con la navegación y la percepción y comprenden el mapeado, la localización, la planificación de trayectorias y la adaptación del mapa. En esta tesis, abordamos algunos de los problemas sin resolver de la navegación de robots móviles en entornos de interiores no estáticos. Nos centramos en entornos tipo casa ya que este trabajo se enmarca en el proyecto de investigación HEROITEA que se enfoca en ayudar a personas ancianas en tareas cotidianas del hogar. Esta tesis contribuye al proyecto HEROITEA con un sistema completo de mapeado y adaptación del mapa que asegura una navegación segura y la comprensión del entorno. Además, aportamos métodos de localización y planificación de trayectorias usando el mapa construido para realizar nuevas tareas en el entorno. El primer problema que se aborda en esta tesis es el mapeado de entornos de interiores estáticos por parte de un robot. Proponemos un método de mapeado híbrido que estructura la información capturada en varios mapas. El mapa híbrido contiene información sobre la estructura del entorno, las trayectorias libres y bloqueadas y también incluye información semántica, como los objetos y escenas en el entorno. Toda esta información está separada en diferentes componentes del mapa híbrido que están interconectados de tal forma que el sistema puede beneficiarse en cualquier momento de la información contenida en cada componente. Además de la definición conceptual del mapa híbrido, hemos desarrollado unos procedimientos para construir el mapa y un algoritmo de exploración que permite que esta construcción se realice autónomamente. Sin embargo, los entornos de interiores habitados por personas están lejos de ser estáticos ya que pueden cambiar a lo largo del tiempo. Por esta razón, el segundo problema que intentamos solucionar en esta tesis es la adaptación del mapa para entornos no estáticos. Proponemos un método probabilístico de adaptación del mapa basado en objetos que calcula la probabilidad de que cada objeto en el entorno haya sido movido o permanezca en su posición anterior. Para terminar, un mapa es simplemente una descripción del entorno cuya importancia está principalmente relacionada con su uso. Por ello, los mapas más valiosos serán los que ofrezcan un rango mayor de aplicaciones. Para abordar este asunto, el tercer problema que intentamos solucionar es explotar las características intrínsecas del mapa híbrido para mejorar el desempeño de métodos de localización y de planificación de trayectorias usando el mapa híbrido. El objetivo principal de estos métodos es aumentar la precisión en la localización del robot y la eficiencia en la planificación de trayectorias en relación al tiempo de ejecución y la distancia recorrida. Hemos evaluado los métodos propuestos en una variedad de entornos de interiores simulados y reales. En esta extensa evaluación, mostramos que los mapas híbridos pueden construirse y mantenerse en el tiempo de forma eficiente y que dan lugar a nuevas posibilidades en cuanto a localización y planificación de trayectorias. En esta tesis, mostramos un aumento en la precisión y robustez en la localización y una mejora en el desempeño de la planificación de trayectorias. En resumen, esta tesis lleva a cabo diversas contribuciones en el ámbito de la navegación de robots móviles en entornos de interiores, y especialmente en mapeado híbrido. Los mapas híbridos ofrecen más eficiencia durante la construcción del mapa y en otras tareas como la localización y la planificación de trayectorias. Además, resaltamos la necesidad de tratar los cambios en entornos de interiores y los beneficios de combinar información topológica, semántica y métrica para la autonomía del robot.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Javier González Jiménez.- Vocal: Nancy Marie Amat

A tesselated probabilistic representation for spatial robot perception and navigation

The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations