Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems

Abstract— Nowadays careful measurement applications are handed over to Wired and Wireless Sensor Network. Taking the scenario of train location as an example, this would lead to an increase in uncertainty about position related to sensors with long acquisition times like Balises, RFID and Transponders along the track. We take into account the data without any synchronization protocols, for increase the accuracy and reduce the uncertainty after the data fusion algorithms. The case studies, we have analysed, derived from the needs of the project partners: train localization, head of an auger in the drilling sector localization and the location of containers of radioactive material waste in a reprocessing nuclear plant. They have the necessity to plan the maintenance operations of their infrastructure basing through architecture that taking input from the sensors, which are localization and diagnosis, maps and cost, to optimize the cost effectiveness and reduce the time of operation

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

Robot Localization in an Agricultural Environment

Localization and Mapping of autonomous robots in an harsh and unstable environment such as a steep slope vineyard is a challenging research topic. The commonly used Dead Reckoning systems can fail due to the harsh conditions of the terrain and the accurate Global Position System can be considerably noisy or not always available. Agriculture is moving towards a precision agriculture, with advanced monitoring systems and wireless sensors networks. These systems and wireless sensors are installed in the crop field and can be considered relevant landmarks for robot localization using different types of technologies.In this work the performance of Pozyx, a low cost Time-of-flight system with Ultra-Wide Bandwidth (UWB) technology, is studied and implemented on a real robot range-based localization system. Firstly the error of both the range-only system and the embedded localization algorithm of the sensor is characterized. Then the range measurements are filtered with an EKF algorithm to output the robot pose and finally compared with the localization algorithm of the sensor.The obtained results are presented and compared with previous works showing an increased redundancy of the robot localization estimation. The UWB is proved to offer a good solution for a harsh environment as the agricultural one since its range-measurements are not much impacted by the atmospheric conditions. The discussion also allows to present formulations for better results of Beacons Mapping Procedure (BMP) required for accurate and reliable localization systems

Localization and Mapping for Self-Driving Vehicles:A Survey

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains

Stereo Visual SLAM for Mobile Robots Navigation

Esta tesis está enfocada a la combinación de los campos de la robótica móvil y la visión por computador, con el objetivo de desarrollar métodos que permitan a un robot móvil localizarse dentro de su entorno mientras construye un mapa del mismo, utilizando como única entrada un conjunto de imágenes. Este problema se denomina SLAM visual (por las siglas en inglés de "Simultaneous Localization And Mapping") y es un tema que aún continúa abierto a pesar del gran esfuerzo investigador realizado en los últimos años. En concreto, en esta tesis utilizamos cámaras estéreo para capturar, simultáneamente, dos imágenes desde posiciones ligeramente diferentes, proporcionando así información 3D de forma directa. De entre los problemas de localización de robots, en esta tesis abordamos dos de ellos: el seguimiento de robots y la localización y mapeado simultáneo (o SLAM). El primero de ellos no tiene en cuenta el mapa del entorno sino que calcula la trayectoria del robot mediante la composición incremental de las estimaciones de su movimiento entre instantes de tiempo consecutivos. Cuando se usan imágenes para calcular esta trayectoria, el problema toma el nombre de "odometría visual", y su resolución es más sencilla que la del SLAM visual. De hecho, a menudo se integra como parte de un sistema de SLAM completo. Esta tesis contribuye con la propuesta de dos sistemas de odometría visual. Uno de ellos está basado en un solución cerrada y eficiente mientras que el otro está basado en un proceso de optimización no-lineal que implementa un nuevo método de detección y eliminación rápida de espurios. Los métodos de SLAM, por su parte, también abordan la construcción de un mapa del entorno con el objetivo de mejorar sensiblemente la localización del robot, evitando de esta forma la acumulación de error en la que incurre la odometría visual. Además, el mapa construido puede ser empleado para hacer frente a situaciones exigentes como la recuperación de la localización tras la pérdida del robot o realizar localización global. En esta tesis se presentan dos sistemas completos de SLAM visual. Uno de ellos se ha implementado dentro del marco de los filtros probabilísticos no parámetricos, mientras que el otro está basado en un método nuevo de "bundle adjustment" relativo que ha sido integrado con algunas técnicas recientes de visión por computador. Otra contribución de esta tesis es la publicación de dos colecciones de datos que contienen imágenes estéreo capturadas en entornos urbanos sin modificar, así como una estimación del camino real del robot basada en GPS (denominada "ground truth"). Estas colecciones sirven como banco de pruebas para validar métodos de odometría y SLAM visual

Developing a person guidance module for hospital robots

This dissertation describes the design and implementation of the Person Guidance Module (PGM) that enables the IWARD (Intelligent Robot Swarm for attendance, Recognition, Cleaning and delivery) base robot to offer route guidance service to the patients or visitors inside the hospital arena. One of the common problems encountered in huge hospital buildings today is foreigners not being able to find their way around in the hospital. Although there are a variety of guide robots currently existing on the market and offering a wide range of guidance and related activities, they do not fit into the modular concept of the IWARD project. The PGM features a robust and foolproof non-hierarchical sensor fusion approach of an active RFID, stereovision and cricket mote sensor for guiding a patient to the X-ray room, or a visitor to a patient’s ward in every possible scenario in a complex, dynamic and crowded hospital environment. Moreover, the speed of the robot can be adjusted automatically according to the pace of the follower for physical comfort using this system. Furthermore, the module performs these tasks in any unconstructed environment solely from a robot’s onboard perceptual resources in order to limit the hardware installation costs and therefore the indoor setting support. Similar comprehensive solution in one single platform has remained elusive in existing literature. The finished module can be connected to any IWARD base robot using quick-change mechanical connections and standard electrical connections. The PGM module box is equipped with a Gumstix embedded computer for all module computing which is powered up automatically once the module box is inserted into the robot. In line with the general software architecture of the IWARD project, all software modules are developed as Orca2 components and cross-complied for Gumstix’s XScale processor. To support standardized communication between different software components, Internet Communications Engine (Ice) has been used as middleware. Additionally, plug-and-play capabilities have been developed and incorporated so that swarm system is aware at all times of which robot is equipped with PGM. Finally, in several field trials in hospital environments, the person guidance module has shown its suitability for a challenging real-world application as well as the necessary user acceptance

A Novel Approach To Intelligent Navigation Of A Mobile Robot In A Dynamic And Cluttered Indoor Environment

The need and rationale for improved solutions to indoor robot navigation is increasingly driven by the influx of domestic and industrial mobile robots into the market. This research has developed and implemented a novel navigation technique for a mobile robot operating in a cluttered and dynamic indoor environment. It divides the indoor navigation problem into three distinct but interrelated parts, namely, localization, mapping and path planning. The localization part has been addressed using dead-reckoning (odometry). A least squares numerical approach has been used to calibrate the odometer parameters to minimize the effect of systematic errors on the performance, and an intermittent resetting technique, which employs RFID tags placed at known locations in the indoor environment in conjunction with door-markers, has been developed and implemented to mitigate the errors remaining after the calibration. A mapping technique that employs a laser measurement sensor as the main exteroceptive sensor has been developed and implemented for building a binary occupancy grid map of the environment. A-r-Star pathfinder, a new path planning algorithm that is capable of high performance both in cluttered and sparse environments, has been developed and implemented. Its properties, challenges, and solutions to those challenges have also been highlighted in this research. An incremental version of the A-r-Star has been developed to handle dynamic environments. Simulation experiments highlighting properties and performance of the individual components have been developed and executed using MATLAB. A prototype world has been built using the WebotsTM robotic prototyping and 3-D simulation software. An integrated version of the system comprising the localization, mapping and path planning techniques has been executed in this prototype workspace to produce validation results

Real-time simulator of collaborative and autonomous vehicles

Durant ces dernières décennies, l’apparition des systèmes d’aide à la conduite a essentiellement été favorisée par le développement des différentes technologies ainsi que par celui des outils mathématiques associés. Cela a profondément affecté les systèmes de transport et a donné naissance au domaine des systèmes de transport intelligents (STI). Nous assistons de nos jours au développement du marché des véhicules intelligents dotés de systèmes d’aide à la conduite et de moyens de communication inter-véhiculaire. Les véhicules et les infrastructures intelligents changeront le mode de conduite sur les routes. Ils pourront résoudre une grande partie des problèmes engendrés par le trafic routier comme les accidents, les embouteillages, la pollution, etc. Cependant, le bon fonctionnement et la fiabilité des nouvelles générations des systèmes de transport nécessitent une parfaite maitrise des différents processus de leur conception, en particulier en ce qui concerne les systèmes embarqués. Il est clair que l’identification et la correction des défauts des systèmes embarqués sont deux tâches primordiales à la fois pour la sauvegarde de la vie humaine, à la fois pour la préservation de l’intégrité des véhicules et des infrastructures urbaines. Pour ce faire, la simulation numérique en temps réel est la démarche la plus adéquate pour tester et valider les systèmes de conduite et les véhicules intelligents. Elle présente de nombreux avantages qui la rendent incontournable pour la conception des systèmes embarqués. Par conséquent, dans ce projet, nous présentons une nouvelle plateforme de simulation temps-réel des véhicules intelligents et autonomes en conduite collaborative. Le projet se base sur deux principaux composants. Le premier étant les produits d’OPAL-RT Technologies notamment le logiciel RT-LAB « en : Real Time LABoratory », l’application Orchestra et les machines de simulation dédiées à la simulation en temps réel et aux calculs parallèles, le second composant est Pro-SiVIC pour la simulation de la dynamique des véhicules, du comportement des capteurs embarqués et de l’infrastructure. Cette nouvelle plateforme (Pro-SiVIC/RT-LAB) permettra notamment de tester les systèmes embarqués (capteurs, actionneurs, algorithmes), ainsi que les moyens de communication inter-véhiculaire. Elle permettra aussi d’identifier et de corriger les problèmes et les erreurs logicielles, et enfin de valider les systèmes embarqués avant même le prototypage