Project ARES: Driverless transportation system. Challenges and approaches in an unstructured road

This article belongs to the Special Issue Intelligent Control of Mobile Robotics.The expansion of electric vehicles in urban areas has paved the way toward the era of autonomous vehicles, improving the performance in smart cities and upgrading related driving problems. This field of research opens immediate applications in the tourism areas, airports or business centres by greatly improving transport efficiency and reducing repetitive human tasks. This project shows the problems derived from autonomous driving such as vehicle localization, low coverage of 4G/5G and GPS, detection of the road and navigable zones including intersections, detection of static and dynamic obstacles, longitudinal and lateral control and cybersecurity aspects. The approaches proposed in this article are sufficient to solve the operational design of the problems related to autonomous vehicle application in the special locations such as rough environment, high slopes and unstructured terrain without traffic rules.Research is supported by the Spanish Government through the CICYT projects (PID2019-104793RB-C31 and RTI2018-096036-B-C21), the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362) and through EAI of the Ministry of Science and Innovation of the Government of Spain project RTI2018-095143-B-C2

Robust monte-carlo localization using adaptive likelihood models

Summary. In probabilistic mobile robot localization, the development of the sensor model plays a crucial role as it directly influences the efficiency and the robustness of the localization process. Sensor models developed for particle filters compute the likelihood of a sensor measurement by assuming that one of the particles accurately represents the true location of the robot. In practice, however, this assumption is often strongly violated, especially when using small sample sets or during global localization. In this paper we introduce a novel, adaptive sensor model that explicitly takes the limited representational power of particle filters into account. As a result, our approach uses smooth likelihood functions during global localization and more peaked functions during position tracking. Experiments show that our technique significantly outperforms existing, static sensor models.

Arquitectura software y de navegación para vehículo autónomo

La importancia de los vehículos autónomos en el sector del transporte durante las próximas décadas es ya un hecho. La implementación a gran escala de estos vehículos supondrá una serie de ventajas entre las que destacan una conducción más segura y por lo tanto una disminución de los accidentes de tráfico, una reducción de las emisiones y del consumo energético y un acortamiento de los tiempos de trayecto. Sin embargo, existen todavía numerosos problemas por resolver de cara a una conducción completamente autónoma y generalizada. Todavía es necesario investigar en distintas tecnologías como percepción, control o navegación. Esta última área, es especialmente crítica ya que el correcto movimiento del vehículo depende de una localización y planificación de trayectorias robustas y fiables, entre otras tareas de navegación. Además, también es necesario estudiar la relación y el funcionamiento conjunto de todos los módulos de estas áreas junto con el hardware y entre ellas, relaciones definidas por la arquitectura. El objetivo de esta tesis es: Por una parte, desarrollar una plataforma de investigación constituida por un vehículo autónomo completamente funcional, en la que se puedan probar distintos algoritmos relacionados con la conducción autónoma. Se investigarán las distintas arquitecturas posibles y se describirá la incorporada al vehículo desarrollado. Por otra parte, esta tesis presenta los avances realizados en el área de la navegación para mejorar la localización del vehículo en entornos mixtos donde métodos convencionales basados en GNSS o la correlación entre un mapa y las lecturas del LiDAR no obtienen resultados precisos, así como los avances en predicción del movimiento de otros vehículos, necesarios para una buena planificación de trayectorias. Además se investigará acerca de la interacción entre peatones y vehículos autónomos, y cómo mejorarla haciendo uso de distintas interfaces de comunicación. Los resultados de los algoritmos desarrollados en localización y predicción de trayectorias han sido obtenidos con bases de datos públicas y comparados con métodos del estado del arte a los que superan en precisión, mientras que los resultados relativos a la interacción entre peatones y vehículos autónomos se ha evaluado mediante experimentos reales. Además, la arquitectura completa del vehículo ha sido probada en distintos experimentos que certifican su correcto funcionamiento.The importance of autonomous vehicles in the transportation sector over the next decades is already a fact. The large-scale implementation of these vehicles will bring several advantages, including safer driving and therefore a decrease of traffic fatalities, lower emissions and energy consumption, and shorter journey times. However, there are still many issues to be solved for fully autonomous and widespread driving. A deeper research is still needed in different technologies such as perception, control and navigation. This last area is especially critical since the correct movement of the vehicle depends on precise localization and a robust and reliable path planning, among other navigation tasks. In addition, it is also necessary to study the relationships and the joint operation of all the modules of these areas together with the hardware and between them, relationships defined by the architecture. The objective of this thesis is: On the one hand, to develop a research platform consisting of a fully functional autonomous vehicle, on which different algorithms related to autonomous driving can be tested. The different possible architectures will be investigated and the one incorporated in the developed vehicle will be described. On the other hand, this thesis presents the advances made in the area of navigation to improve vehicle localization in mixed environments where conventional methods based on GNSS or the correlation between a map and LiDAR readings do not obtain accurate results, as well as advances in predicting the movement of other vehicles, necessary for good trajectory planning. In addition, the interaction between pedestrians and autonomous vehicles will be studied, and how to improve it using different communication interfaces. The results of the developed algorithms in localization and trajectory prediction have been obtained with public databases and compared with state-of-the-art methods which are outperformed in termos of accuracy, while the results related to the interaction between pedestrians and autonomous vehicles have been evaluated by means of real experiments. In addition, the complete vehicle architecture has been tested in different experiments certifying its correct operation.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Ignacio Parra Alonso.- Secretario: Carlos Guindel Gómez.- Vocal: Noelia Hernández Parr

Wo bin ich? Beiträge zum Lokalisierungsproblem mobiler Roboter

Self-localization addresses the problem of estimating the pose of mobile robots with respect to a certain coordinate system of their workspace. It is needed for various mobile robot applications like material handling in industry, disaster zone operations, vacuum cleaning, or even the exploration of foreign planets. Thus, self-localization is a very essential capability. This problem has received considerable attention over the last decades. It can be decomposed into localization on a global and local level. Global techniques are able to localize the robot without any prior knowledge about its pose with respect to an a priori known map. In contrast, local techniques aim to correct so-called odometry errors occurring during robot motion. In this thesis, the global localization problem for mobile robots is mainly addressed. The proposed method is based on matching an incremental local map to an a priori known global map. This approach is very time and memory efficient and robust to structural ambiguity as well as with respect to the occurrence of dynamic obstacles in non-static environments. The algorithm consists of several components like ego motion estimation or global point cloud matching. Nowadays most computers feature multi-core processors and thus map matching is performed by applying a parallelized variant of the Random Sample Matching (pRANSAM) approach originally devised for solving the 3D-puzzle problem. pRANSAM provides a set of hypotheses representing alleged robot poses. Techniques are discussed to postprocess the hypotheses, e.g. to decide when the robot pose is determined with a sufficient accuracy. Furthermore, runtime aspects are considered in order to facilitate localization in real-time. Finally, experimental results demonstrate the robustness of the method proposed in this thesis.Das Lokalisierungsproblem mobiler Roboter beschreibt die Aufgabe, deren Pose bezüglich eines gegebenen Weltkoordinatensystems zu bestimmen. Die Fähigkeit zur Selbstlokalisierung wird in vielen Anwendungsbereichen mobiler Roboter benötigt, wie etwa bei dem Materialtransport in der industriellen Fertigung, bei Einsätzen in Katastrophengebieten oder sogar bei der Exploration fremder Planeten. Eine Unterteilung existierender Verfahren zur Lösung des genannten Problems erfolgt je nachdem ob eine Lokalisierung auf lokaler oder auf globaler Ebene stattfindet. Globale Lokalisierungsalgorithmen bestimmen die Pose des Roboters bezüglich eines Weltkoordinatensystems ohne jegliches Vorwissen, wohingegen bei lokalen Verfahren eine grobe Schätzung der Pose vorliegt, z.B. durch gegebene Odometriedaten des Roboters. Im Rahmen dieser Dissertation wird ein neuer Ansatz zur Lösung des globalen Lokalisierungsproblems vorgestellt. Die grundlegende Idee ist, eine lokale Karte und eine globale Karte in Übereinstimmung zu bringen. Der beschriebene Ansatz ist äußerst robust sowohl gegenüber Mehrdeutigkeiten der Roboterpose als auch dem Auftreten dynamischer Hindernisse in nicht-statischen Umgebungen. Der Algorithmus besteht hauptsächlich aus drei Komponenten: Einem Scanmatcher zur Generierung der lokalen Karte, einer Methode zum matchen von lokaler und globaler Karte und einer Instanz, welche entscheidet, wann der Roboter mit hinreichender Sicherheit korrekt lokalisiert ist. Das Matching von lokaler und globaler Karte wird dabei von einer parallelisierten Variante des Random Sample Matching (pRANSAM) durchgeführt, welche eine Menge von Posenhypothesen liefert. Diese Hypothesen werden in einem weiteren Schritt analysiert, um bei hinreichender Eindeutigkeit die korrekte Roboterpose zu bestimmen. Umfangreiche Experimente belegen die Zuverlässigkeit und Genauigkeit des in dieser Dissertation vorgestellten Verfahrens