Experiments in Moving Baseline Navigation using Autonomous Surface Craft

This paper describes an on-going research effort to achieve real-time cooperative localization of multiple autonomous underwater vehicles. We describe a series of experiments that utilize autonomous surface craft (ASC), equiped with undersea acoustic modems, GPS, and 802.11b wireless ethernet communications, to acquire data and develop software for cooperative localization of distributed vehicle networks. Our experiments demonstrate the capability of the Woods Hole acoustic modems to provide accurate round-trip and one-way range measurements, as well as data transfer, for a fully mobile network of vehicles in formation flight. Finally, we present preliminary results from initial experiments involving cooperative operation of an Odyssey III AUV and two ASCs, demonstrating ranging and data transfer from the ASCs to the Odyssey III

Cooperative Acoustic Navigation Scheme for Heterogenous Autonomous Underwater Vehicles

International audienceCooperative Acoustic Navigation Scheme for Heterogenous Autonomous Underwater Vehicle

Cooperative AUV Navigation using a Single Maneuvering Surface Craft

In this paper we describe the experimental implementation of an online algorithm for cooperative localization of submerged autonomous underwater vehicles (AUVs) supported by an autonomous surface craft. Maintaining accurate localization of an AUV is difficult because electronic signals, such as GPS, are highly attenuated by water. The usual solution to the problem is to utilize expensive navigation sensors to slow the rate of dead-reckoning divergence. We investigate an alternative approach that utilizes the position information of a surface vehicle to bound the error and uncertainty of the on-board position estimates of a low-cost AUV. This approach uses the Woods Hole Oceanographic Institution (WHOI) acoustic modem to exchange vehicle location estimates while simultaneously estimating inter-vehicle range. A study of the system observability is presented so as to motivate both the choice of filtering approach and surface vehicle path planning. The first contribution of this paper is to the presentation of an experiment in which an extended Kalman filter (EKF) implementation of the concept ran online on-board an OceanServer Iver2 AUV while supported by an autonomous surface vehicle moving adaptively. The second contribution of this paper is to provide a quantitative performance comparison of three estimators: particle filtering (PF), non-linear least-squares optimization (NLS), and the EKF for a mission using three autonomous surface craft (two operating in the AUV role). Our results indicate that the PF and NLS estimators outperform the EKF, with NLS providing the best performance.United States. Office of Naval Research (Grant N000140711102)United States. Office of Naval Research. Multidisciplinary University Research InitiativeSingapore. National Research FoundationSingapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Monitorin

Underwater Robots Part I: Current Systems and Problem Pose

International audienceThis paper constitutes the first part of a general overview of underwater robotics. The second part is titled: Underwater Robots Part II: existing solutions and open issues

VLBL autonomous underwater vehicle navigation using a single transponder

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis (Nav. E. and S.M. in Ocean Systems Management)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (leaves 73-75).(cont.) Therefore, accurate underwater navigation using a single location transponder would provide dramatic time and cost savings for underwater vehicle operations. This thesis presents a simulation of autonomous underwater vehicle navigation using a single transponder to create a virtual long baseline (VLBL). Similarly to LBL systems, ranges in a VLBL are calculated between the vehicle and the transponder, but the vehicle position is determined by advancing multiple ranges from a single transponder along the vehicles dead reckoning track. Vehicle position is then triangulated using these successive ranges in a manner analogous to a 'running fix' in surface ship navigation. Navigation data from bottom survey operations of an underwater vehicle called the Autonomous Benthic Explorer (ABE) were used in the simulation. The results of this simulation are presented along with a discussion of the benefits, limitations, and implications of its extension to real-time operations. A cost savings analysis was also conducted based both on the idea that a single surveyed beacon could be deployed for underwater navigation and on the further extension of this problem that the 'single beacon' used for navigation could be located on the ship itself.Acoustic long baseline (LBL) navigation systems are often used for precision underwater vehicle navigation. LBL systems triangulate the position of the vehicle by calculating the range between the vehicle and multiple transponders with known locations. A typical LBL system incorporates between two and twelve acoustic transponders. The vehicle interrogates the beacons acoustically, calculates the range to each beacon based on the roundtrip travel time of the signal, and uses the range data from two or more of the acoustic transponders at any point in time to determine its position. However, for accurate underwater navigation, the location of each deployed transponder in the array must be precisely surveyed prior to conducting autonomous vehicle operations. Surveying the location of the transponders is a costly and time-consuming process, especially in cases where underwater vehicles are used in mapping operations covering a number of different locations in succession. During these extended mapping operations, the transponders need to be deployed, surveyed, and retrieved in each location, adding significant time and, consequently, significant cost to any operation.by Cara E.G. LaPointe.Nav.E.and S.M.in Ocean Systems Managemen

Modelos baseados em funções Kernel para localização de veículos autônomos subaquáticos

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2015.Esta tese aborda a análise do sistema de localização acústica de veículos subaquáticos em uma configuração dita de base longa auxiliada por sensores inerciais. Para o processo de filtragem de dados, o filtro de Kalman, em sua versão estendida EKF (Extended Kalman Filter), é utilizado de modo a aproveitar toda informação relacionada aos estados do veículo proveniente dos sensores. O foco do trabalho está no processo de aprendizagem a partir de dados com vistas à identificação das medições errôneas do tempo de chegada da sonda sonora e à correção das mesmas. As técnicas exploradas para essas finalidades são o AAKR (AutoAssociative Kernel Regression) e o SVDD (Support Vector Data Description). O objetivo é melhorar a estimação dos estados do veículo (posição, velocidade e orientação) provenientes dos sensores inerciais, aproveitando um conjunto de medições corretas obtidas durante a navegação ou em missões anteriores à atual. A melhoria no desempenho do sistema de localização foi analisado por simulação utilizando dados experimentais obtidos em missões com veículos de baixo custo e modelos de propagação acústica que inserem desvios factíveis aos tempos de chegada medidos. Os resultados são comparados ao desempenho obtido com uma solução clássica para a localização acústica de veículos autônomos em ambiente subaquático. Destaca-se ainda que a arquitetura proposta não se apresenta firmemente acoplada ou fortemente dependente de qualquer outro algoritmo presente no veículo, o que a caracteriza como uma solução bastante modular com a possibilidade de estendê-la a outras aplicações.Abstract : This thesis deals with the analysis of an acoustic localization system in a long baseline configuration for navigation of underwater vehicles aided by inertial sensors. For the process of filtering data, the Extended Kalman Filter (EKF) is used in order to take advantage of all information related to the states of the vehicle from the sensors. The focus is on the process of learning from data with a view to identify erroneous measurements of the time of flight of the acoustic signal and correct eventual deviations in this quantity. The techniques used for these purposes are AAKR (AutoAssociative Kernel Regression) and SVDD (Support Vector Data Description). The objective is to improve the accuracy of the estimates of the vehicle s states (position, velocity and orientation) coming from the inertial sensors, taking advantage of a set of correct measurements obtained during prior navigations or in the current missions. The improved performance of the tracking system is evidenced by the data obtained using in field missions with low-cost vehicles and acoustic propagation models that insert feasible deviations to arrival times measured. The results are compared with the performance obtained with other classical solution to acoustic localization task in underwater environment of autonomous vehicles. It is highlighted also that the proposed architecture is not tightly coupled to any other algorithm running in the vehicle , which characterizes the approach as a very modular and cost-effective computing solution

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Precision autonomous underwater navigation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2003.Includes bibliographical references (p. 175-185).Deep-sea archaeology, an emerging application of autonomous underwater vehicle (AUV) technology, requires precise navigation and guidance. As science requirements and engineering capabilities converge, navigating in the sensor-limited ocean remains a fundamental challenge. Despite the logistical cost, the standards of archaeological survey necessitate using fixed acoustic transponders - an instrumented navigation environment. This thesis focuses on the problems particular to operating precisely within such an environment by developing a design method and a navigation algorithm. Responsible documentation, through remote sensing images, distinguishes archaeology from salvage, and fine-resolution imaging demands precision navigation. This thesis presents a design process for making component and algorithm level tradeoffs to achieve system-level performance satisfying the archaeological standard. A specification connects the functional requirements of archaeological survey with the design parameters of precision navigation. Tools based on estimation fundamentals - the Cram6r-Rao lower bound and the extended Kalman filter - predict the system-level precision of candidate designs. Non-dimensional performance metrics generalize the analysis results. Analyzing a variety of factors and levels articulates the key tradeoffs: sensor selection, acoustic beacon configuration, algorithm selection, etc. The abstract analysis is made concrete by designing a survey and navigation system for an expedition to image the USS Monitor. Hypothesis grid (Hgrid) is both a representation of the sensed environment and an algorithm for building the representation. Range observations measuring the line-of-sight distance between two acoustic transducers are subject to multipath errors and spurious returns.The quality of this measurement is dependent on the location of the estimator. Hgrids characterize the measurement quality by generating a priori association probabilities - the belief that subsequent measurements will correspond to the direct-path, a multipath, or an outlier - as a function of the estimated location. The algorithm has three main components: the mixed-density sensor model using Gaussian and uniform probability distributions, the measurement classification and multipath model identification using expectation-maximization (EM), and the grid-based spatial representation. Application to data from an autonomous benthic explorer (ABE) dive illustrates the algorithm and shows the feasibility of the approach.by Brian Steven Bingham.Ph.D