Probablistic approaches for intelligent AUV localisation

This thesis studies the problem of intelligent localisation for an autonomous underwater vehicle (AUV). After an introduction about robot localisation and specific issues in the underwater domain, the thesis will focus on passive techniques for AUV localisation, highlighting experimental results and comparison among different techniques. Then, it will develop active techniques, which require intelligent decisions about the steps to undertake in order for the AUV to localise itself. The undertaken methodology consisted in three stages: theoretical analysis of the problem, tests with a simulation environment, integration in the robot architecture and field trials. The conclusions highlight applications and scenarios where the developed techniques have been successfully used or can be potentially used to enhance the results given by current techniques. The main contribution of this thesis is in the proposal of an active localisation module, which is able to determine the best set of action to be executed, in order to maximise the localisation results, in terms of time and efficiency

Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments

Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature. Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization. Another technique developed in this work addresses the problem of tracking an underwater vehicle when the GPS signal suddenly becomes unavailable. The methods include the whitening of the data to reveal the true statistical distance between datapoints and also incorporates physical science data to enhance the topographic map. Simulations were performed at Lake Nighthorse, Colorado, USA, between April 25th and May 2nd 2018 and at Big Fisherman\u27s Cove, Santa Catalina Island, California, USA, on July 13th and July 14th 2016. Different missions were executed on different environments (snow, rain and the presence of plumes). Results showed that these two methodologies for localization and tracking work for reference maps that had been recorded within a week and the accuracy on the average error in localization can be compared to the errors found when using GPS if the time in which the observations were taken are the same period of the day (morning, afternoon or night). The whitening of the data had positive results when compared to localizing without whitening

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

Underwater Localization in Complex Environments

A capacidade de um veículo autónomo submarino (AUV) se localizar num ambiente complexo, bem como de extrair características relevantes do mesmo, é de grande importância para o sucesso da navegação. No entanto, esta tarefa é particularmente desafiante em ambientes subaquáticos devido à rápida atenuação sofrida pelos sinais de sistemas de posicionamento global ou outros sinais de radiofrequência, dispersão e reflexão, sendo assim necessário o uso de processos de filtragem. Ambiente complexo é definido aqui como um cenário com objetos destacados das paredes, por exemplo, o objeto pode ter uma certa variabilidade de orientação, portanto a sua posição nem sempre é conhecida. Exemplos de cenários podem ser um porto, um tanque ou mesmo uma barragem, onde existem paredes e dentro dessas paredes um AUV pode ter a necessidade de se localizar de acordo com os outros veículos na área e se posicionar em relação ao mesmo e analisá-lo. Os veículos autónomos empregam muitos tipos diferentes de sensores para localização e percepção dos seus ambientes e dependem dos computadores de bordo para realizar tarefas de direção autónoma. Para esta dissertação há um problema concreto a resolver, localizar um cabo suspenso numa coluna de água em uma região conhecida do mar e navegar de acordo com ela. Embora a posição do cabo no mundo seja bem conhecida, a dinâmica do cabo não permite saber exatamente onde ele está. Assim, para que o veículo se localize de acordo com este para que possa ser inspecionado, a localização deve ser baseada em sensores ópticos e acústicos. Este estudo explora o processamento e a análise de imagens óticas e acústicas, por meio dos dados adquiridos através de uma câmara e por um sonar de varrimento mecânico (MSIS),respetivamente, a fim de extrair características ambientais relevantes que possibilitem a estimação da localização do veículo. Os pontos de interesse extraídos de cada um dos sensores são utilizados para alimentar um estimador de posição, implementando um Filtro de Kalman Extendido (EKF), de modo a estimar a posição do cabo e através do feedback do filtro melhorar os processos de extração de pontos de interesse utilizados.The ability of an autonomous underwater vehicle (AUV) to locate itself in a complex environment as well as to detect relevant environmental features is of crucial importance for successful navigation. However, it's particularly challenging in underwater environments due to the rapid attenuation suffered by signals from global positioning systems or other radio frequency signals, dispersion and reflection thus needing a filtering process. Complex environment is defined here as a scenario with objects detached from the walls, for example the object can have a certain orientation variability therefore its position is not always known. Examples of scenarios can be a harbour, a tank or even a dam reservoir, where there are walls and within those walls an AUV may have the need to localize itself according to the other vehicles in the area and position itself relative to one to observe, analyse or scan it. Autonomous vehicles employ many different types of sensors for localization and perceiving their environments and they depend on the on-board computers to perform autonomous driving tasks. For this dissertation there is a concrete problem to solve, which is to locate a suspended cable in a water column in a known region in the sea and navigate according to it. Although the cable position in the world is well known, the cable dynamics does not allow knowing where it is exactly. So, in order to the vehicle localize itself according to it so it can be inspected, the localization has to be based on optical and acoustic sensors. This study explores the processing and analysis of optical and acoustic images, through the data acquired through a camera and by a mechanical scanning sonar (MSIS), respectively, in order to extract relevant environmental characteristics that allow the estimation of the location of the vehicle. The points of interest extracted from each of the sensors are used to feed a position estimator, by implementing an Extended Kalman Filter (EKF), in order to estimate the position of the cable and through the feedback of the filter improve the extraction processes of points of interest used

Towards basin-scale in-situ characterization of sea-ice using an Autonomous Underwater Glider

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2020.This thesis presents an Autonomous Underwater Glider (AUG) architecture that is intended for basin-scale unattended survey of Arctic sea-ice. The distinguishing challenge for AUG operations in the Arctic environment is the presence of year-round sea-ice cover which prevents vehicle surfacing for localization updates and shore-side communication. Due to the high cost of operating support vessels in the Arctic, the proposed AUG architecture minimizes external infrastructure requirements to brief and infrequent satellite updates on the order of once per day. This is possible by employing onboard acoustic sensing for sea-ice observation and navigation, along with intelligent management of onboard resources. To enable unattended survey of Arctic sea-ice with an AUG, this thesis proposes a hierarchical acoustics-based sea-ice characterization scheme to perform science data collection and assess environment risk, a multi-factor terrain-aided navigation method that leverages bathymetric features and active ocean current sensing to limit localization error, and a set of energy-optimal propulsive and hotel policies that react to evolving environmental conditions to improve AUG endurance. These methods are evaluated with respect to laboratory experiments and preliminary field data, and future Arctic sea-ice survey mission concepts are discussed.Support for this research was provided through the National Science Foundation Navigating the New Arctic Grant #1839063 and the NASA PSTAR Grant #NNX16AL08G. Additionally, this research was supported by the Walter A. Rosenblith Presidential Fellowship

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios

Cooperative Localization in Mobile Underwater Acoustic Sensor Networks

Die großflächige Erkundung und Überwachung von Tiefseegebieten gewinnt mehr und mehr an Bedeutung für Industrie und Wissenschaft. Diese schwer zugänglichen Areale in der Tiefsee können nur mittels Teams unbemannter Tauchbote effizient erkundet werden. Aufgrund der hohen Kosten, war bisher ein Einsatz von mehreren autonomen Unterwasserfahrzeugen (AUV) wirtschaftlich undenkbar, wodurch AUV-Teams nur in Simulationen erforscht werden konnten. In den letzten Jahren konnte jedoch eine Entwicklung hin zu günstigeren und robusteren AUVs beobachtet werden. Somit wird der Einsatz von AUV-Teams in Zukunft zu einer realen Option. Die wachsende Nachfrage nach Technologien zur Unterwasseraufklärung und Überwachung konnte diese Entwicklung noch zusätzlich beschleunigen. Eine der größten technischen Hürden für tief tauchende AUVs ist die Unterwasserlokalisierug. Satelitengestützte Navigation ist in der Tiefe nicht möglich, da Radiowellen bereits nach wenigen Metern im Wasser stark an Intensität verlieren. Daher müssen neue Ansätze für die Unterwasserlokalisierung entwickelt werden die sich auch für Fahrzeugenverbände skalieren lassen. Der Einsatz von AUV-Teams ermöglicht nicht nur völlig neue Möglichkeiten der Kooperation, sondern erlaubt auch jedem einzelnen AUV von den Navigationsdaten der anderen Fahrzeuge im Verband zu profitieren, um die eigene Lokalisierung zu verbessern. In dieser Arbeit wird ein kooperativer Lokalisierungsansatz vorgestellt, welcher auf dem Nachrichtenaustausch durch akustische Ultra-Short Base-Line (USBL) Modems basiert. Ein akustisches Modem ermöglicht die Übertragung von Datenpaketen im Wasser, wärend ein USBL-Sensor die Richtung einer akustischen Quelle bestimmen kann. Durch die Kombination von Modem und Sensor entsteht ein wichtiges Messinstrument für die Unterwasserlokalisierung. Wenn ein Fahrzeug ein Datenpaket mit seiner eignen Position aussendet, können andere Fahrzeuge mit einem USBL-Modem diese Nachricht empfangen. In Verbindung mit der Richtungsmessung zur Quelle, können diese Daten von einem Empfangenden AUV verwendet werden, um seine eigene Positionsschatzung zu verbessern. Diese Arbeit schlägt einen Ansatz zur Fusionierung der empfangenen Nachricht mit der Richtungsmessung vor, welcher auch die jeweiligen Messungenauigkeiten berücksichtigt. Um die Messungenauigkeit des komplexen USBL-Sensors bestimmen zu können, wurde zudem ein detailliertes Sensormodell entwickelt. Zunächst wurden existierende Ansätze zur kooperativen Lokalisierung (CL) untersucht, um daraus eine Liste von erwünschten Eigenschaften für eine CL abzuleiten. Darauf aufbauend wurde der Deep-Sea Network Lokalisation (DNL) Ansatz entwickelt. Bei DNL handelt es sich um eine CL Methode, bei der die Skalierbarkeit sowie die praktische Anwendbarkeit im Fokus stehen. DNL ist als eine Zwischenschicht konzipiert, welche USBL-Modem und Navigationssystem miteinander verbindet. Es werden dabei Messwerte und Kommunikationsdaten des USBL zu einer Standortbestimmung inklusive Richtungsschätzung fusioniert und an das Navigationssystem weiter geleitet, ähnlich einem GPS-Sensor. Die Funktionalität von USBL-Modell und DNL konnten evaluiert werden anhand von Messdaten aus Seeerprobungen in der Ostsee sowie im Mittelatlantik. Die Qualität einer CL hangt häufig von vielen unterschiedlichen Faktoren ab. Die Netzwerktopologie muss genauso berücksichtig werden wie die Lokalisierungsfähigkeiten jedes einzelnen Teilnehmers. Auch das Kommunikationsverhalten der einzelnen Teilnehmer bestimmt, welche Informationen im Netzwerk vorhanden sind und hat somit einen starken Einfluss auf die CL. Um diese Einflussfaktoren zu untersuchen, wurden eine Reihe von Szenarien simuliert, in denen Kommunikationsverhalten und Netzwerktopologie für eine Gruppe von AUVs variiert wurden. In diesen Experimenten wurden die AUVs durch ein Oberflächenfahrzeug unterstützt, welches seine geo-referenzierte Position über DNL an die getauchten Fahrzeuge weiter leitete. Anhand der untersuchten Topologie können die Experimente eingeteilt werden in Single-Hop und Multi-Hop. Single-Hop bedeutet, dass jedes AUV sich in der Sendereichweite des Oberflächenfahrzeugs befindet und dessen Positionsdaten auf direktem Wege erhält. Wie die Ergebnisse der Single-Hop Experimente zeigen, kann der Lokalisierungsfehler der AUVs eingegrenzt werden, wenn man DNL verwendet. Dabei korreliert der Lokalisierungsfehler mit der kombinierten Ungenauigkeit von USBL-Messung und Oberflächenfahrzeugposition. Bei den Multi-Hop Experimenten wurde die Topologie so geändert, dass sich nur eines der AUVs in direkter Sendereichweite des Oberflächenfahrzeugs befindet. Dieses AUV verbessert seine Position mit den empfangen Daten des Oberflächenfahrzeugs und sendet wiederum seine verbesserte Position an die anderen AUVs. Auch hier konnte gezeigt werden, dass sich der Lokalisierungfehler der Gruppe mit DNL einschränken lässt. Ändert man nun das Schema der Kommunikation so, dass alle AUVs zyklisch ihre Position senden, zeigte sich eine Verschlechterung der Lokalisierungsqualität der Gruppe. Dieses unerwartet Ergebnis konnte auf einen Teil des DNL-Algorithmus zurück geführt werden. Da die verwendete USBL-Klasse nur die Richtung eines Signals misst, nicht jedoch die Entfernung zum Sender, wird in der DNL-Schicht eine Entfernungsschatzung vorgenommen. Wenn die Kommunikation nicht streng unidirektional ist, entsteht eine Ruckkopplungsschleife, was zu fehlerhaften Entfernungsschatzungen führt. Im letzten Experiment wird gezeigt wie sich dieses Problem vermeiden lasst, mithilfe einer relativ neue USBL-Klasse, die sowohl Richtung als auch Entfernung zum Sender misst. Die zwei wesentlichen Beiträge dieser Arbeit sind das USBL-Model zum einen und zum Anderen, der neue kooperative Lokalisierungsansatz DNL. Mithilfe des Sensormodels lassen sich nicht nur Messabweichungen einer USBL-Messung bestimmen, es kann auch dazu genutzt werden, einige Fehlereinflüsse zu korrigieren. Mit DNL wurde eine skalierbare CL-Methode entwickelt, die sich gut für den den Einsatz bei mobilen Unterwassersensornetzwerken eignet. Durch das Konzept als Zwischenschicht, lasst sich DNL einfach in bestehende Navigationslösungen integrieren, um die Langzeitstabilität der Navigation für große Verbände von tiefgetauchten Fahrzeugen zu gewährleisten. Sowohl USBL-Model als auch DNL sind dabei so ressourcenschonend, dass sie auf dem Computer eines Standard USBL laufen können, ohne die ursprüngliche Funktionalität einzuschränken, was den praktischen Einsatz zusätzlich vereinfacht

In-situ characterization of sea state with improved navigation on an Autonomous Underwater Glider

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2022.This thesis presents an Autonomous Underwater Glider (AUG) architecture with improved onboard navigation and acoustics-based sensing intended to enable basin-scale unattended surveys of our Earth’s most remote oceans. Traditional AUGs have long-been an important platform for oceanographic surveys due to their high endurance and autonomy, yet lack the operational flexibility to operate in many regions of scientific interest and the sensing capability to capture scientific data at the air-sea interface. Particularly of interest is the marginal ice zone (MIZ) in the Arctic and the Southern Ocean, as both are vitally important to understanding global climate trends, yet prohibitively expensive to persistently monitor with support vessels. To fill this observational gap, the sensing, navigation, and adaptability of AUGs must be improved. This is possible by employing onboard acoustic sensing for sea state observation and navigation, as well as incorporating vehicle improvements targeting maneuverability and intelligent adaptability to evolving environmental states. To enable persistent monitoring of both the water-column and air-sea interface, this thesis proposes an improved vehicle architecture for a more capable AUG, a real-time DVLaided navigation process that leverages ocean current sensing to limit localization error, and a subsea acoustics-based sea state characterization method capable of analyzing wave spectra under-ice and with zero surface expression. These methods are evaluated with respect to extensive laboratory experiments and field data collected during in-situ implementation.Support for this research was provided through grants from the National Science Foundation (NSF) Navigating the New Arctic Grant (NNA #1839063) and the National Ocean Partnership Program (NOPP) Enhanced Propulsion Integrated Capability - Deep Autonomous Underwater Glider (EPIC-DAUG) grant (NA19OAR0110408)