Coordinated Sensor-Based Area Coverage and Cooperative Localization of a Heterogeneous Fleet of Autonomous Surface Vessels (ASVs)

Sensor coverage with fleets of robots is a complex task requiring solutions to localization, communication, navigation and basic sensor coverage. Sensor coverage of large areas is a problem that occurs in a variety of different environments from terrestrial to aerial to aquatic. In this thesis we consider the aquatic version of the problem. Given a known aquatic environment and collection of aquatic surface vehicles with known kinematic and dynamic constraints, how can a fleet of vehicles be deployed to provide sensor coverage of the surface of the body of water? Rather than considering this problem in general, in this work we consider the problem given a specific fleet consisting of one very well equipped robot aided by a number of smaller, less well equipped devices that must operate in close proximity to the main robot. A boustrophedon decomposition algorithm is developed that incorporates the motion, sensing and communication constraints imposed by the autonomous fleet. Solving the coverage problem leads to a localization/communication problem. A critical problem for a group of autonomous vehicles is ensuring that the collection operates within a common reference frame. Here we consider the problem of localizing a heterogenous collection of aquatic surface vessels within a global reference frame. We assume that one vessel -- the mother robot -- has access to global position data of high accuracy, while the other vessels -- the child robots -- utilize limited onboard sensors and sophisticated sensors on board the mother robot to localize themselves. This thesis provides details of the design of the elements of the heterogeneous fleet including the sensors and sensing algorithms along with the communication strategy used to localize all elements of the fleet within a global reference frame. Details of the robot platforms to be used in implementing a solution are also described. Simulation of the approach is used to demonstrate the effectiveness of the algorithm, and the algorithm and its components are evaluated using a fleet of ASVs

TRIDENT: A Framework for Autonomous Underwater Intervention

TRIDENT is a STREP project recently approved by the European Commission whose proposal was submitted to the ICT call 4 of the 7th Framework Program. The project proposes a new methodology for multipurpose underwater intervention tasks. To that end, a cooperative team formed with an Autonomous Surface Craft and an Intervention Autonomous Underwater Vehicle will be used. The proposed methodology splits the mission in two stages mainly devoted to survey and intervention tasks, respectively. The project brings together research skills specific to the marine environments in navigation and mapping for underwater robotics, multi-sensory perception, intelligent control architectures, vehiclemanipulator systems and dexterous manipulation. TRIDENT is a three years project and its start is planned by first months of 2010.This work is partially supported by the European Commission through FP7-ICT2009-248497 projec

Cooperative methods for vehicle localization

Abstract : Embedded intelligence in vehicular applications is becoming of great interest since the last two decades. Position estimation has been one of the most crucial pieces of information for Intelligent Transportation Systems (ITS). Real time, accurate and reliable localization of vehicles has become particularly important for the automotive industry. The significant growth of sensing, communication and computing capabilities over the recent years has opened new fields of applications, such as ADAS (Advanced driver assistance systems) and active safety systems, and has brought the ability of exchanging information between vehicles. Most of these applications can benefit from more accurate and reliable localization. With the recent emergence of multi-vehicular wireless communication capabilities, cooperative architectures have become an attractive alternative to solving the localization problem. The main goal of cooperative localization is to exploit different sources of information coming from different vehicles within a short range area, in order to enhance positioning system efficiency, while keeping the cost to a reasonable level. In this Thesis, we aim to propose new and effective methods to improve vehicle localization performance by using cooperative approaches. In order to reach this goal, three new methods for cooperative vehicle localization have been proposed and the performance of these methods has been analyzed. Our first proposed cooperative method is a Cooperative Map Matching (CMM) method which aims to estimate and compensate the common error component of the GPS positioning by using cooperative approach and exploiting the communication capability of the vehicles. Then we propose the concept of Dynamic base station DGPS (DDGPS) and use it to generate GPS pseudorange corrections and broadcast them for other vehicles. Finally we introduce a cooperative method for improving the GPS positioning by incorporating the GPS measured position of the vehicles and inter-vehicle distances. This method is a decentralized cooperative positioning method based on Bayesian approach. The detailed derivation of the equations and the simulation results of each algorithm are described in the designated chapters. In addition to it, the sensitivity of the methods to different parameters is also studied and discussed. Finally in order to validate the results of the simulations, experimental validation of the CMM method based on the experimental data captured by the test vehicles is performed and studied. The simulation and experimental results show that using cooperative approaches can significantly increase the performance of the positioning methods while keeping the cost to a reasonable amount.Résumé : L’intelligence embarquée dans les applications véhiculaires devient un grand intérêt depuis les deux dernières décennies. L’estimation de position a été l'une des parties les plus cruciales concernant les systèmes de transport intelligents (STI). La localisation précise et fiable en temps réel des véhicules est devenue particulièrement importante pour l'industrie automobile. Les améliorations technologiques significatives en matière de capteurs, de communication et de calcul embarqué au cours des dernières années ont ouvert de nouveaux champs d'applications, tels que les systèmes de sécurité active ou les ADAS, et a aussi apporté la possibilité d'échanger des informations entre les véhicules. Une localisation plus précise et fiable serait un bénéfice pour ces applications. Avec l'émergence récente des capacités de communication sans fil multi-véhicules, les architectures coopératives sont devenues une alternative intéressante pour résoudre le problème de localisation. L'objectif principal de la localisation coopérative est d'exploiter différentes sources d'information provenant de différents véhicules dans une zone de courte portée, afin d'améliorer l'efficacité du système de positionnement, tout en gardant le coût à un niveau raisonnable. Dans cette thèse, nous nous efforçons de proposer des méthodes nouvelles et efficaces pour améliorer les performances de localisation du véhicule en utilisant des approches coopératives. Afin d'atteindre cet objectif, trois nouvelles méthodes de localisation coopérative du véhicule ont été proposées et la performance de ces méthodes a été analysée. Notre première méthode coopérative est une méthode de correspondance cartographique coopérative (CMM, Cooperative Map Matching) qui vise à estimer et à compenser la composante d'erreur commune du positionnement GPS en utilisant une approche coopérative et en exploitant les capacités de communication des véhicules. Ensuite, nous proposons le concept de station de base Dynamique DGPS (DDGPS) et l'utilisons pour générer des corrections de pseudo-distance GPS et les diffuser aux autres véhicules. Enfin, nous présentons une méthode coopérative pour améliorer le positionnement GPS en utilisant à la fois les positions GPS des véhicules et les distances inter-véhiculaires mesurées. Ceci est une méthode de positionnement coopératif décentralisé basé sur une approche bayésienne. La description détaillée des équations et les résultats de simulation de chaque algorithme sont décrits dans les chapitres désignés. En plus de cela, la sensibilité des méthodes aux différents paramètres est également étudiée et discutée. Enfin, les résultats de simulations concernant la méthode CMM ont pu être validés à l’aide de données expérimentales enregistrées par des véhicules d'essai. La simulation et les résultats expérimentaux montrent que l'utilisation des approches coopératives peut augmenter de manière significative la performance des méthodes de positionnement tout en gardant le coût à un montant raisonnable

Where Am I? SLAM for Mobile Machines on a Smart Working Site

The current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental information, especially the terrain. Due to the dynamic changing of the construction site and the consequent absence of a high definition map, the Simultaneous Localization and Mapping (SLAM) offering the terrain information for construction machines is still challenging. Current SLAM technologies proposed for mobile machines are strongly dependent on costly or computationally expensive sensors, such as RTK GPS and cameras, so that commercial use is rare. In this study, we proposed an affordable SLAM method to create a multi-layer grid map for the construction site so that the machine can have the environmental information and be optimized accordingly. Concretely, after the machine passes by the grid, we can obtain the local information and record it. Combining with positioning technology, we then create a map of the interesting places of the construction site. As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of one IMU and two differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 1.3% in the harsh environment. As an outlook, our SLAM technology provides the cornerstone to activate many efficiency improvement approaches. View Full-Tex