1,177 research outputs found
Optimal path shape for range-only underwater target localization using a Wave Glider
Underwater localization using acoustic signals is one of the main components in a navigation system for an autonomous underwater vehicle (AUV) as a more accurate alternative to dead-reckoning techniques. Although different methods based on the idea of multiple beacons have been studied, other approaches use only one beacon, which reduces the system’s costs and deployment complexity. The inverse approach for single-beacon navigation is to use this method for target localization by an underwater or surface vehicle. In this paper, a method of range-only target localization using a Wave Glider is presented, for which simulations and sea tests have been conducted to determine optimal parameters to minimize acoustic energy use and search time, and to maximize location accuracy and precision. Finally, a field mission is presented, where a Benthic Rover (an autonomous seafloor vehicle) is localized and tracked using minimal human intervention. This mission shows, as an example, the power of using autonomous vehicles in collaboration for oceanographic research.Peer ReviewedPostprint (author's final draft
Infrastructure Wi-Fi for connected autonomous vehicle positioning : a review of the state-of-the-art
In order to realize intelligent vehicular transport networks and self driving cars, connected autonomous vehicles (CAVs) are required to be able to estimate their position to the nearest centimeter. Traditional positioning in CAVs is realized by using a global navigation satellite system (GNSS) such as global positioning system (GPS) or by fusing weighted location parameters from a GNSS with an inertial navigation systems (INSs). In urban environments where Wi-Fi coverage is ubiquitous and GNSS signals experience signal blockage, multipath or non line-of-sight (NLOS) propagation, enterprise or carrier-grade Wi-Fi networks can be opportunistically used for localization or “fused” with GNSS to improve the localization accuracy and precision. While GNSS-free localization systems are in the literature, a survey of vehicle localization from the perspective of a Wi-Fi anchor/infrastructure is limited. Consequently, this review seeks to investigate recent technological advances relating to positioning techniques between an ego vehicle and a vehicular network infrastructure. Also discussed in this paper is an analysis of the location accuracy, complexity and applicability of surveyed literature with respect to intelligent transportation system requirements for CAVs. It is envisaged that hybrid vehicular localization systems will enable pervasive localization services for CAVs as they travel through urban canyons, dense foliage or multi-story car parks
Cooperative Consensus Simultaneous Localization And Mapping For Multi Blimp System
Navigation in an ocean environment with few static features and dynamic water
background is an adventurous field to be explored by multi-agent system. This is
because of its non-uniform availability of measurement on the ocean surface since the
spatial feature distribution is greatly varied. Thus, it is desirable to design a cooperative
localisation and mapping framework that is capable to handle spurious detection,
reduce the localisation uncertainty of an agent and achieve fast and good decision. The
main objective of this research is to design a cooperative simultaneous localisation and
mapping method for multi blimp system involving the dynamic water surface as the
background and small flock consensus as the group decision method. A new
cooperative framework for the multi blimp system consisting of three blimps and
buoys was developed and designed for this purpose. The simultaneous localisation and
mapping were designed by integrating three methods which are the Extended Kalman
Filter, the enhanced Scale Invariant Feature Transform and Received Signal Strength
Indicator to improve the data association process. The group perception of direction
based on small flock of animal consensus was taken into the data association process.
It was discovered that this cooperative consensus simultaneous localisation and
mapping was able to reduce the number of feature points and detect the desired features
in clear and dark water environments. In addition, based on cooperative consensus
benchmarking, this method was able to achieve faster consensus to up to 8.3 % and 42
% than the scale free model and klemm-eguilez model respectively. On top of these,
its heading accuracy was found to be more accurate to up to 30 % and 76 % than the
scale free model and klemm-eguilez model respectively. Overall, the proposed
approach has achieved its prominent results and it is proven to be significantly reliable
and applicable to be implemented in the ocean observation monitoring system
Bearing-based formation control with second-order agent dynamics
We consider the distributed formation control problem for a network of agents using visual measurements. We propose solutions that are based on bearing (and optionally distance) measurements, and agents with double integrator dynamics. We assume that a subset of the agents can track, in addition to their neighbors, a set of static features in the environment. These features are not considered to be part of the formation, but they are used to asymptotically control the velocity of the agents. We analyze the convergence properties of the proposed protocols analytically and through simulations.Published versionSupporting documentatio
Cooperative localization for autonomous underwater vehicles
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2009Self-localization of an underwater vehicle is particularly challenging due to the absence
of Global Positioning System (GPS) reception or features at known positions
that could otherwise have been used for position computation. Thus Autonomous
Underwater Vehicle (AUV) applications typically require the pre-deployment of a set
of beacons.
This thesis examines the scenario in which the members of a group of AUVs
exchange navigation information with one another so as to improve their individual
position estimates.
We describe how the underwater environment poses unique challenges to vehicle
navigation not encountered in other environments in which robots operate and how
cooperation can improve the performance of self-localization. As intra-vehicle communication
is crucial to cooperation, we also address the constraints of the communication
channel and the effect that these constraints have on the design of cooperation
strategies.
The classical approaches to underwater self-localization of a single vehicle, as
well as more recently developed techniques are presented. We then examine how
methods used for cooperating land-vehicles can be transferred to the underwater
domain. An algorithm for distributed self-localization, which is designed to take the
specific characteristics of the environment into account, is proposed.
We also address how correlated position estimates of cooperating vehicles can lead
to overconfidence in individual position estimates.
Finally, key to any successful cooperative navigation strategy is the incorporation
of the relative positioning between vehicles. The performance of localization
algorithms with different geometries is analyzed and a distributed algorithm for the
dynamic positioning of vehicles, which serve as dedicated navigation beacons for a
fleet of AUVs, is proposed.This work was funded by Office of Naval Research grants N00014-97-1-0202,
N00014-05-1-0255, N00014-02-C-0210, N00014-07-1-1102 and the ASAP MURI
program led by Naomi Leonard of Princeton University
Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition
The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
Reliable localization methods for intelligent vehicles based on environment perception
Mención Internacional en el título de doctorIn the near past, we would see autonomous vehicles and Intelligent Transport
Systems (ITS) as a potential future of transportation. Today, thanks to all the
technological advances in recent years, the feasibility of such systems is no longer a
question. Some of these autonomous driving technologies are already sharing our
roads, and even commercial vehicles are including more Advanced Driver-Assistance
Systems (ADAS) over the years. As a result, transportation is becoming more efficient
and the roads are considerably safer.
One of the fundamental pillars of an autonomous system is self-localization. An
accurate and reliable estimation of the vehicle’s pose in the world is essential to
navigation. Within the context of outdoor vehicles, the Global Navigation Satellite
System (GNSS) is the predominant localization system. However, these systems are
far from perfect, and their performance is degraded in environments with limited
satellite visibility. Additionally, their dependence on the environment can make them
unreliable if it were to change.
Accordingly, the goal of this thesis is to exploit the perception of the environment
to enhance localization systems in intelligent vehicles, with special attention to
their reliability. To this end, this thesis presents several contributions: First, a study
on exploiting 3D semantic information in LiDAR odometry is presented, providing
interesting insights regarding the contribution to the odometry output of each type
of element in the scene. The experimental results have been obtained using a public
dataset and validated on a real-world platform. Second, a method to estimate the
localization error using landmark detections is proposed, which is later on exploited
by a landmark placement optimization algorithm. This method, which has been
validated in a simulation environment, is able to determine a set of landmarks
so the localization error never exceeds a predefined limit. Finally, a cooperative
localization algorithm based on a Genetic Particle Filter is proposed to utilize vehicle
detections in order to enhance the estimation provided by GNSS systems. Multiple
experiments are carried out in different simulation environments to validate the
proposed method.En un pasado no muy lejano, los vehículos autónomos y los Sistemas Inteligentes
del Transporte (ITS) se veían como un futuro para el transporte con gran potencial.
Hoy, gracias a todos los avances tecnológicos de los últimos años, la viabilidad
de estos sistemas ha dejado de ser una incógnita. Algunas de estas tecnologías
de conducción autónoma ya están compartiendo nuestras carreteras, e incluso los
vehículos comerciales cada vez incluyen más Sistemas Avanzados de Asistencia a la
Conducción (ADAS) con el paso de los años. Como resultado, el transporte es cada
vez más eficiente y las carreteras son considerablemente más seguras.
Uno de los pilares fundamentales de un sistema autónomo es la autolocalización.
Una estimación precisa y fiable de la posición del vehículo en el mundo es esencial
para la navegación. En el contexto de los vehículos circulando en exteriores, el
Sistema Global de Navegación por Satélite (GNSS) es el sistema de localización predominante.
Sin embargo, estos sistemas están lejos de ser perfectos, y su rendimiento
se degrada en entornos donde la visibilidad de los satélites es limitada. Además, los
cambios en el entorno pueden provocar cambios en la estimación, lo que los hace
poco fiables en ciertas situaciones.
Por ello, el objetivo de esta tesis es utilizar la percepción del entorno para mejorar
los sistemas de localización en vehículos inteligentes, con una especial atención a
la fiabilidad de estos sistemas. Para ello, esta tesis presenta varias aportaciones:
En primer lugar, se presenta un estudio sobre cómo aprovechar la información
semántica 3D en la odometría LiDAR, generando una base de conocimiento sobre la
contribución de cada tipo de elemento del entorno a la salida de la odometría. Los
resultados experimentales se han obtenido utilizando una base de datos pública y se
han validado en una plataforma de conducción del mundo real. En segundo lugar,
se propone un método para estimar el error de localización utilizando detecciones
de puntos de referencia, que posteriormente es explotado por un algoritmo de
optimización de posicionamiento de puntos de referencia. Este método, que ha
sido validado en un entorno de simulación, es capaz de determinar un conjunto de
puntos de referencia para el cual el error de localización nunca supere un límite
previamente fijado. Por último, se propone un algoritmo de localización cooperativa
basado en un Filtro Genético de Partículas para utilizar las detecciones de vehículos
con el fin de mejorar la estimación proporcionada por los sistemas GNSS. El método
propuesto ha sido validado mediante múltiples experimentos en diferentes entornos
de simulación.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridSecretario: Joshué Manuel Pérez Rastelli.- Secretario: Jorge Villagrá Serrano.- Vocal: Enrique David Martí Muño
Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.
This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture.
The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks.
In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd
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