Dissertations / Theses on the topic 'Autonomous surface vehicle (ASV)'

Mapping is an enabler for further actions. With the map of an area available, it is possible to plan ahead. Maps of landmasses and heavily used deep waters have been produced and are in use but many shallow waters have been largely unmapped. This thesis proposes and examines two methods of control to produce depth maps of shallow waters using multiple autonomous surface vehicles. Assumptions about the environment are kept to a minimum and agents are expected to explore and map inside a given polygonal boundary. Gaussian process regression is used to guide the agents to areas with large uncertainty. A group of autonomous surface vehicles are used for experimental evaluation. Existing works in this area are compared with the method proposed in this thesis to evaluate map quality and time needed to create the map. Results show that one of the proposed methods is best suited for fast and raw map generation while the other strikes a good balance between accuracy and speed.
Att ha tillgång till en karta över ett område är en förutsättning för många olika aktiviteter, och därför har det skapats allt mer exakta kartor över de flesta landområden. För hav och sjöar har man skapat mer ungefärliga djupkartor för att undvika grundstötningar för sjöfart. Grundare områden har däremot ofta undvikits av stora djupmätningsfartyg, och är därför i hög grad okarterade.I denna rapport föreslås och analyseras en metod för att kartera djupet i grunda områden med hjälp av en grupp autonoma ytfarkoster. Givet en polygon inom vilken man vill ha botten karterad skall gruppen autonomt söka av området med få ytterligare antaganden. Gaussiska processer används för att styra farkosterna mot områden med stora mätosäkerheter, och algoritmen utvärderas i riktiga experiment.Resultaten jämförs med befintliga metoders prestanda, med avseende på kartkvalitet och tid för kartering. Resultaten visar att en av de föreslagna metoderna är snabb men mindre noggrann, medan den andra ger en bättre avvägning mellan kvalitet och uppdragstid.

Due to the wide field of view, omnidirectional cameras have been extensively used in many applications, including surveillance and autonomous navigation. In order to implement a fully autonomous system, one of the essential problems is construction of an accurate, dynamic environment model. In Computer Vision this is called structure from stereo or motion (SFSM). The work in this dissertation addresses omnidirectional vision based SFSM for the navigation of an autonomous surface vehicle (ASV), and implements a vision system capable of locating stationary obstacles and detecting moving objects in real time. The environments where the ASV navigates are complex and fully of noise, system performance hence is a primary concern. In this dissertation, we thoroughly investigate the performance of range estimation for our omnidirectional vision system, regarding to different omnidirectional stereo configurations and considering kinds of noise, for instance, disturbances in calibration, stereo configuration, and image processing. The result of performance analysis is very important for our applications, which not only impacts the ASVâ s navigation, also guides the development of our omnidirectional stereo vision system. Another big challenge is to deal with noisy image data attained from riverine environments. In our vision system, a four-step image processing procedure is designed: feature detection, feature tracking, motion detection, and outlier rejection. The choice of point-wise features and outlier rejection based method makes motion detection and stationary obstacle detection efficient. Long run outdoor experiments are conducted in real time and show the effectiveness of the system.
Ph. D.

The Geographic Information System (GIS) is a crucial part of any land navigation system. Autonomous ground vehicles should have access to stored geographic data and the ability to manipulate it for routing purposes. Since there is no human interaction involved in operating these vehicles, data that a human driver would use to make decisions must be stored in the GIS. The data which represent the earthâ s surface become a series of factors and constraints which translate to friction in terms of mobility. Factors need to be weighted appropriately, but require a sensitivity analysis before designating these weights. Constraints do not require any weight because they represent absolute barriers which cannot be traveled upon. All GIS layers are incorporated into the raster environment, so that an accumulated surfaces can be built on which a least-cost path can be located. The sensitivity analysis allows generation of many routes which can be field tested for the appropriate weight selection for each factor. Ultimately, the entire process would select an optimal path and output closely spaced waypoints which the vehicle can follow.
Master of Science

The use of composite materials has been common in small craft boat building for a long time. In recent years, there has been a huge push in the development of different types of appendages such as hydrofoils. These hydrofoils are commonly manufactured in carbon fibre composites, due to high requirements in weight and stiffness. These appendages can be difficult to develop and complex to manufacture since manufacturing methods for composites are complex. KTH Royal Institute of Technology is developing a hydrofoil concept for a small autonomous vessel. The hydrofoil is designed to be built in carbon fibre composite. It requires to have control surfaces in order to maintain a stable flight and the electrical propulsion is located on it as well. This makes this hydrofoil one of a kind and the parts that build up the hydrofoil have more specifications than just to be designed from a hydrodynamic and structural point of view like a conventional hydrofoil. This thesis investigated what manufacturing methods should be used when building a hydrofoil like this. Existing manufacturing methods such as vacuum infusion and different types of prepreg moulding have been reviewed and are presented early in the report. The methods have been analyzed from the perspective of the components of the hydrofoil, resulting in an initial manufacturing strategy for the different components. The strategy includes everything from a 3D-model of the part to a finished product, including sheet design, mould manufacturing and moulding of the part. Several tests were conducted before a component was successfully manufactured. Each test was evaluated and presented in such a manner that the reader can understand what is needed to be improved and why. The conclusions of each test lead to an improvement of the manufacturing technique and a new test until the final result was acquired. The tests were examined with a microscope to verify the quality of the part. Then a weight fraction analysis was made on these parts. The final conclusions of the thesis gave successful methods to manufacture the different parts of the hydrofoil. A fast manufacturing method for product development of complex parts was achieved. The resulting parts from the tests show good quality from the analysis.

As the mission field for autonomous vehicles expands into a larger variety of territories, the development of autonomous surface vehicles (ASVs) becomes increasingly important. ASVs have the potential to travel for long periods of time in areas that cannot be reached by aerial, ground, or underwater autonomous vehicles. ASVs are useful for a variety of missions, including bathymetric mapping, communication with other autonomous vehicles, military reconnaissance and surveillance, and environmental data collecting. Critical to an ASV's ability to maneuver without human intervention is its ability to detect obstacles, including the shoreline. Prior topological knowledge of the environment is not always available or, in dynamic environments, reliable. While many existing obstacle detection systems can only detect 3D obstacles at close range via a laser or radar signal, vision systems have the potential to detect obstacles both near and far, including "flat" obstacles such as the shoreline. The challenge lies in processing the images acquired by the vision system and extracting useful information. While this thesis does not address the issue of processing the images to locate the pixel positions of the obstacles, we assume that we have these processed images available. We present an algorithm that takes these processed images and, by incorporating the kinematic model of the ASV, maps the pixel locations of the obstacles into a global coordinate system. An Extended Kalman Filter is used to localize the ASV and the surrounding obstacles.
Master of Science

In the pursuit of research in remote areas, robots may be employed to deploy sensor networks. These robots need a method of classifying a surface to determine if it is a suitable installation site. Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. We design this system with user configurable parameters for choosing a high accuracy, efficient classifier. In support of this system, we also develop an algorithm for evaluating the effectiveness of individual features as indicators of the variable of interest. Motivating our work is a prior project that manually developed a surface classifier using an accelerometer; we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool.

In the past several years, the Defense Advanced Research Projects Agency (DARPA) has sponsored two Grand Challenges, races among autonomous ground vehicles in rural environments. These vehicles must follow a course delineated by Global Positioning System waypoints using no human guidance. Airborne LIDAR data and GIS can play a significant role in identifying barriers and least cost paths for such vehicles. Least cost paths minimize the sum of impedance across a surface. Impedance can be measured by steepness of slope, impenetrable barriers such as vegetation and buildings, fence lines and streams, or other factors deemed important to the vehicleâ s success at navigating the terrain. This research aims to provide accurate least cost paths for those vehicles using airborne LIDAR data. The concepts of barrier identification and least cost path generation are reviewed and forty-five least cost paths created with their performance compared to corresponding Euclidean paths. The least cost paths were found superior to the corresponding Euclidean paths in terms of impedance as they avoid barriers, follow roads and pass across relatively gentler slopes.
Master of Science

Approved for public release; distribution is unlimited
Reissued 30 May 2017 with correction to student's affiliation on title page.
Autonomous vehicle teams have great potential in a wide range of maritime sensing applications, including mine countermeasures (MCM). A key enabler for successfully employing autonomous vehicles in MCM missions is motion planning, a collection of algo-rithms for designing trajectories that vehicles must follow. For maximum utility, these algorithms must consider the capabilities and limitations of each team member. At a minimum, they should incorporate dynamic and operational constraints to ensure trajectories are feasible. Another goal is maximizing sensor performance in the presence of uncertainty. Optimal control provides a useful frame-work for solving these types of motion planning problems with dynamic constraints and di_x000B_erent performance objectives, but they usually require numerical solutions. Recent advances in numerical methods have produced a general mathematical and computational framework for numerically solving optimal control problems with parameter uncertainty—generalized optimal control (GenOC)— thus making it possible to numerically solve optimal search problems with multiple searcher, sensor, and target models. In this dissertation, we use the GenOC framework to solve motion planning problems for di_x000B_erentMCMsearch missions conducted by autonomous surface and underwater vehicles. Physics-based sonar detection models are developed for operationally relevant MCM sensors, and the resulting optimal search trajectories improve mine detection performance over conventional lawnmower survey patterns—especially under time or resource constraints. Simulation results highlight the flexibility of this approach for optimal mo-tion planning and pre-mission analysis. Finally, a novel application of this framework is presented to address inverse problems relating search performance to sensor design, team composition, and mission planning for MCM CONOPS development.

Unmanned aerial vehicle (UAV) is an expanding interest in numerous industries for various applications. Increasing development of UAVs is happening worldwide, where various sensor attachments and functions are being added. The multi-function UAV can be used within areas where they have not been managed before. Because of their accessibility, cheap purchase, and easy-to-use, they replace expensive systems such as helicopters- and airplane-surveillance. UAV are also being applied into surveillance, combing object detection to video-surveillance and mobility to finding an object from the air without interfering with vehicles or humans ground. In this thesis, we solve the problem of using UAV on autonomous sites, finding an object and critical situation, support autonomous site operators with an extra safety layer from UAVs camera. After finding an object on such a site, uses GPS-coordinates from the UAV to see and place the detected object on the site onto a gridmap, leaving a coordinate-map to the operator to see where the objects are and see if the critical situation can occur. Directly under the object detection, reporting critical situations can be done because of safety-distance-circle leaving warnings if objects come to close to each other. However, the system itself only supports the operator with extra safety and warnings, leaving the operator with the choice of pressing emergency stop or not. Object detection uses You only look once (YOLO) as main object detection Neural Network (NN), mixed with edge-detection for gaining accuracy during bird-eye-views and motion-detection for supporting finding all object that is moving on-site, even if UAV cannot find all the objects on site. Result proofs that the UAV-surveillance on autonomous site is an excellent way to add extra safety on-site if the operator is out of focus or finding objects on-site before startup since the operator can fly the UAV around the site, leaving an extra-safety-layer of finding humans on-site before startup. Also, moving the UAV to a specific position, where extra safety is needed, informing the operator to limit autonomous vehicles speed around that area because of humans operation on site. The use of single object detection limits the effects but gathered object detection methods lead to a promising result while printing those objects onto a global positions system (GPS) map has proposed a new field to study. It leaves the operator with a viewable interface outside of object detection libraries.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 113-120).
This thesis describes the development of an independent, on-board visual servoing system which allows a computationally impoverished aerial vehicle to autonomously identify and track a dynamic surface target. Image segmentation and target tracking algorithms are developed for the specific task of monitoring whales at sea. The computer vision algorithms' estimates prove to be accurate enough for quadrotor stabilization while being computationally fast enough to be processed on-board the platform. This differs from current techniques which require off-board processing of images for vehicle localization and control. The vision algorithm is evaluated on video footage to validate its performance and robustness. The quadrotor is then modeled to motivate and guide the development of Linear Quadratic Regulator (LQR) controllers for maneuvering the quadrotor. The controllers are tuned using a motion capture system which provides ground truth state measurements. The vision system is integrated into the control scheme to allow the quadrotor to track an iCreate. Additionally, an Extended Kalman Filter (EKF) fuses the vision system position estimates with attitude and acceleration measurements from an on-board Inertial Measurement Unit (IMU) to allow the quadrotor to track a moving target without external localization.
by William Clayton Selby.
S.M.

Le domaine des véhicules autonomes a suscité un grand intérêt ces dernières années. Afin de garantir au passager une expérience sûre et confortable sur les véhicules autonomes, des systèmes d'obstacles avancés doivent être mis en œuvre. Bien que les solutions actuelles de détection d'obstacles aient montré de bonnes performances, elles doivent être encore améliorées pour une sécurité accrue des véhicules autonomes sur route, de jour comme de nuit. En particulier, les véhicules autonomes dans la vie réelle peuvent rencontrer de la glace, de la neige ou des flaques d'eau, qui peuvent être la cause de collisions graves et d'accidents de la circulation. Les systèmes de détection doivent donc permettre de détecter les changements d'état de la route pour anticiper la réaction du véhicule et/ou désactiver les fonctions automatisées. L'objectif de cette thèse est de proposer un système pour les véhicules autonomes afin de détecter les conditions de chaussée induites par la météo. Après une étude approfondie de l'état de l'art, un système proche infrarouge (NIR) basé sur des LED et un système d'apprentissage automatique sont proposés pour la détection diurne et nocturne. Le système NIR a été conçu puis validé expérimentalement et, les spécifications techniques du système ont été définies. Le système d'apprentissage automatique est de plus proposé comme solution complémentaire au système NIR. Différents modèles d'apprentissage ont été testés et comparés en termes de performance. Enfin, les résultats sont discutés et une combinaison des deux systèmes est proposée afin de garantir une performance accrue pour la reconnaissance des conditions de route
The field of autonomous vehicles has aroused great interest in recent years. In order to ensure the passenger to get a safe and comfortable experience on autonomous vehicles, advanced obstacle systems have to be implemented. Although current solutions for detecting obstacles have shown quite good performances, they have to be improved for an increased safety of autonomous vehicles on road, both in day-time and night-time conditions. In particular, autonomous vehicles in real life may encounter ice, snow or water puddles, which may be the cause of severe crashes and traffic accidents. The detection systems must hence allow detecting changes in road conditions to anticipate the vehicle reaction and/or deactivate the automated functions. The aim of this thesis is to propose a system implemented on the autonomous vehicles in order to detect the road surface conditions induced by the weather. After deep investigation of the state of art, a near infrared (NIR) system based on LEDs and a machine learning system were proposed for daytime and night-time detection. The NIR systems with three LEDs were investigated with experimental validations. In addition, the specifications of the NIR systems are carefully discussed. Furthermore, the machine learning system is proposed as a supplementary system. The performance of different models is compared in terms of classification accuracy and model complexity. Finally, the results are discussed and a combination of the two systems is proposed

The study of the seas is a field of science that is in constant development. At the technological level, in recent years, important advances have been made in the design of platforms and measurement systems for oceanographic variables with significant improvements in their sensitivity, as well as in their capacity for spatial and temporal resolution. Currently, given the high costs of traditional techniques (oceanographic vessels), vehicles such as the ROV (Remotely Operated Vehicle), the ASV (Autonomous Surface Vehicle), the AUV (Autonomous Underwater Vehicle) and the AUV Glider are being used instead. From the point of view of the realization of oceanographic studies, the AUV offers more benefits due to its maneuverability and autonomy. An example of this would be the case study of the behavior of marine species based on environmental variables. In this situation, it is essential to carry out a good spatial resolution of multiple parameters, such as salinity, at various depths of the same water column. For the industrial and environmental conservation sectors, there is no specific use of the AUV. However, they can be used in order to obtain bathymetric maps and in the monitoring the specific physical-chemical characteristics of seawater. The motivation of this work is focused on the adaptation of the Guanay II vehicle as a platform for oceanographic measurements, with the ability to navigate in immersion, as well as reducing its possibility of collision with other vehicles or marine structures through its design and implementation of a detection and obstacle avoidance system. This adaptation would allow in the future to use the Guanay II vehicle to perform the detection and monitoring of polluting discharges of hydrocarbons in the sea, as well as monitoring oceanographic data for use in predictive models of the displacement of the spill. The Guanay II is a hybrid vehicle between AUV and ASV, that is, it sails on the surface and performs vertical dives at programmed points. Based on the motivation described, mechanical and electronic modifications have been made to the vehicle, accompanied by a process of study, analysis and mathematical development to obtain a hydrodynamic modeling uncoupled from the vehicle on the vertical plane. A vector propulsion immersion system has been designed and implemented using the lateral thrusters to control the vehicle's inclination during the dive. This system has been simulated in Matlab and implemented in the vehicle, which has allowed testing in the Olympic channel of Castelldefels, the results obtained in these tests have been satisfactory, allowing the vehicle to enter in immersion, remain in immersion during the time defined and then return to surface. On the other hand, in parallel to the design and implementation of this system. A new design has been carried out with the implementation of an obstacle detection and reactive avoidance system incorporated with a fuzzy system, based on a SONAR MK3. This system has been simulated and implemented in the control unit of the vehicle. This system has allowed multiple field tests, which were performed at the Olympic Castelldefels canal. The results obtained in these tests have been satisfactory, achieving in all cases the avoidance of the obstacles present in the navigation environment.
El estudio de los mares es un campo de la ciencia que se encuentra en constante desarrollo. A nivel tecnológico, en los últimos años, se han producido importantes avances en el diseño de plataformas y sistemas de medición de variables oceanográficas con importantes mejoras en su sensibilidad, así como en su capacidad de resolución espacial y temporal. Actualmente, debido a los altos costos de las técnicas tradicionales (barcos oceanográficos) se ha incrementado el uso de vehículos no tripulados como los ROV (Remotely Operated Vehicle), los ASV (Autonomous Surface Vehicle), los AUV (Autonomous Underwater Vehicle) y los AUV Glider. Desde el punto de vista de la realización de estudios oceanográficos, los AUV ofrecen mayores beneficios por su mayor maniobrabilidad y autonomía. Un ejemplo de este caso es el estudio del comportamiento de las especies marinas en función de variables ambientales. En estos estudios es indispensable la medida con buena resolución espacial de múltiples parámetros, como la salinidad, a diversas profundidades de una misma columna de agua. Para los sectores industriales y de conservación del medio ambiente no hay un uso específico de los AUV. Pueden ser utilizados en la obtención de mapas batimétricos y en la monitorización de las características físico-químicas especificas del agua del mar. La motivación de este trabajo se centra en la adaptación del vehículo Guanay II como plataforma de mediciones oceanográficas, con la capacidad de navegar en inmersión, así como la reducción de la posibilidad de colisión contra otros vehículos o estructuras marinas mediante el diseño e implantación de un sistema de detección y evasión de obstáculos. Esta adaptación permitiría en el futuro utilizar el vehículo Guanay para realizar la detección y seguimiento de vertidos contaminantes de hidrocarburos en el mar, así como la monitorización de datos oceanográficos para su utilización en modelos predictivos del desplazamiento del vertido. El Guanay II es un vehículo híbrido entre AUV y ASV, es decir, navega en superficie y realiza inmersiones verticales en puntos programados. Con base en la motivación descrita se han realizado modificaciones mecánicas y electrónicas en el vehículo, acompañadas de un proceso de estudio, análisis y de desarrollo matemático para obtener un modelado hidrodinámico desacoplado del vehículo sobre el plano vertical. Se ha diseñado e implementado un sistema de inmersión por propulsión vectorial utilizando los propulsores laterales para controlar la inclinación del vehículo durante la inmersión. Este sistema ha sido simulado en Matlab e implementado en el vehículo, lo que ha permitido probarlo en el canal olímpico de Castelldefels, Los resultados obtenidos en estas pruebas han sido satisfactorios, permitiendo que el vehículo entre en inmersión, se mantenga en inmersión durante el tiempo definido y posteriormente vuelva a superficie. Por otra parte, en paralelo al diseño e implementación de este sistema, se ha llevado a cabo el diseño e implementación de un sistema de detección y evasión de obstáculos de arquitectura reactiva implementado bajo un sistema fuzzy, basado en un sensor SONAR MK3. Este sistema ha sido simulado e implementado en la unidad de control del vehículo. Esta implementación ha permitido realizar múltiples pruebas de campo, las cuales se realizaron en el canal olímpico de Castelldefels. Los resultados obtenidos en estas pruebas han sido satisfactorios, logrando en todos casos la evasión de los obstáculos presentes en el entorno de navegación.

Marine ecological research related to the increasing importance which the fisheries sector has reached so far, new methods and tools to study the biological components of our oceans are needed. The capacity to measure different population and environmental parameters of marine species allows a greater knowledge of the human impact, improving exploitation strategies of these resources. For example, the displacement capacity and mobility patterns are crucial to obtain the required knowledge for a sustainable management of fisheries. However, underwater localisation is one of the main problems which must be addressed in subsea exploration, where no Global Positioning System (GPS) is available. In addition to the traditional underwater localisation systems, such as Long BaseLine (LBL) or Ultra-Short BaseLine (USBL), new methods have been developed to increase navigation performance, flexibility, and to reduce deployment costs. For example, the Range-Only and Single-Beacon (ROSB) is based on an autonomous vehicle which localises and tracks different underwater targets using slant range measurements conducted by acoustic modems. In a moving target tracking scenario, the ROSB target tracking method can be seen as a Hidden Markov Model (HMM) problem. Using Bayes' rule, the probability distribution function of the HMM states can be solved by using different filtering methods. Accordingly, this thesis presents different strategies to improve the ROSB localisation and tracking methods for static and moving targets. Determining the optimal parameters to minimize acoustic energy use and search time, and to maximize the localisation accuracy and precision, is therefore one of the discussed aspects of ROSB. Thus, we present and compare different methods under different scenarios, both evaluated in simulations and field tests. The main mathematical notation and performance of each algorithm are presented, where the best practice has been derived. From a methodology point of view, this work advances the understanding of accuracy that can be achieved by using ROSB target tracking methods with autonomous vehicles. Moreover, whereas most of the work conducted during the last years has been focused on target tracking using acoustic modems, here we also present a novel method called the Area-Only Target Tracking (AOTT). This method works with commercially available acoustic tags, thereby reducing the costs and complexity over other tracking systems. These tags do not have bidirectional communication capabilities, and therefore, the ROSB techniques are not applicable. However, this method can be used to track small targets such as jellyfish due to the reduced tag's size. The methodology behind the area-only technique is shown, and results from the first field tests conducted in Monterey Bay area, California, are also presented.
La biologia marina junt amb la importància que ha adquirit el sector pesquer, fa que es requereixin noves eines per a l’estudi dels nostres oceans. La capacitat de mesurar diferents poblacions i paràmetres ambientals d’espècies marines permet millorar el coneixement de l’impacte que l’ésser humà té sobre elles, millorant-ne els mètodes d’explotació. Per exemple, la capacitat de desplaçament i els patrons de moviment són crucials per obtenir el coneixement necessari per a una explotació sostenible de les pescaries involucrades. No obstant, la localització submarina és un dels principals problemes que s’ha de resoldre en l’explotació dels recursos submarins, on el sistema de posició global (GPS) no es pot utilitzar. A part dels mètodes tradicionals de posicionament submarí, com per exemple el Long Base-Line (LBL) o el Ultra-Short Base-Line (USBL), nous mètodes han estat desenvolupats per tal de millorar la navegació, la flexibilitat, i per reduir els costos de desplegament. Per exemple, el Range-Only and Single-Beacon (ROSB) utilitza un vehicle autònom per a localitzar i seguir diferents objectius submarins mitjançant mesures de rang realitzades a partir de mòdems acústics. En un escenari on l’objectiu a seguir és mòbil, el mètode ROSB de seguiment pot ser vist com a un problema de Hidden Markov Model (HMM). Aleshores, utilitzant la regla de Bayes, la funció de distribució de probabilitat dels estats del HMM pot ser solucionat utilitzant diferents mètodes de filtratge. Per tant, s’estudien diferents estratègies per millorar el sistema de localització i seguiment basat en ROSB, tant per objectius estàtics com mòbils. En aquesta tesis, presentem i comparem diferents mètodes utilitzant diferents escenaris, els quals s’han avaluat tant en simulacions com en proves de camp reals. A més, es presenten les principals notacions matemàtiques de cada algoritme i les millors pràctiques a utilitzar. Per tant, des d’un punt de vista metodològic, aquest treball fa un pas endavant en el coneixement de l’exactitud que es pot assolir utilitzant els mètodes de localització i seguiment d’espècies mitjançant algoritmes ROSB i vehicles autònoms. A més a més, mentre molts dels treballs realitzant durant els últims anys es centren en l’ús de mòdems acústics per al seguiment d’objectius submarins, en aquesta tesis es presenta un innovador mètode anomenat Area-Only Target Tracking (AOTT). Aquest sistema utilitza petites etiquetes acústiques comercials (tag), la qual cosa, redueix el cost i la complexitat en comparació amb els altres mètodes. Addicionalment, gràcies a l’ús d’aquests tags de dimensions reduïdes, aquest sistema permet seguir espècies marines com les meduses. La metodologia utilitzada per el mètode AOTT es mostra en aquesta tesis, on també es presenten els primers experiments realitzats a la badia de Monterey a Califòrnia.

Thesis (S.M.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 117-119).
The Acoustic Doppler Current Profiler (ADCP) is a proven technology which is capable of measuring surface wave height and directional information, however it is generally limited to rigid, bottom mounted applications which limit its capabilities for measuring deep water waves. By employing an upward looking ADCP on a moving platform, such as an autonomous underwater vehicle or submerged float, we show that it is possible to remove the wave induced motion of the platform and accurately measure surface ocean wave information. The platform selected for testing was a REMUS-100 vehicle equipped with an upward and downward looking ADCP and high accuracy Kearfott inertial navigation unit. Additionally, a Microstrain 3DM-GX3-25 Attitude Heading Reference System was tested as a low cost alternative to the Kearfott system. An experiment consisting of multiple REMUS deployments was conducted near the Martha's Vineyard Coastal Observatory (MVCO). The wave induced motion was measured by various inertial and acoustic sensors and removed from the ADCP data record. The surface wave height and mean directional estimates were compared against a Datawell MKIII directional Waverider buoy and bottom mounted 1200 kHz upward looking ADCP at the MVCO. Results demonstrate that the non-directional spectrum of wave height and the mean wave direction as a function of frequency can be accurately measured from an underway autonomous underwater vehicle in coastal depth waters using an ADCP.
by Scott Haven.
S.M.

The usage of autonomous vehicles is starting to appear in several different domains and the domain of public safety is no exception. Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) has created a research arena for public safety (WARA-PS) to explore experimental features, usages, and implementation of autonomous vehicles within the domain of public safety. Collaborating in the arena are several companies, universities, and researchers. This thesis examines, in collaboration with Combitech, a company partnered in WARA-PS, how the implementation of autonomous vehicles affects the sociotechnical system of a search and rescue operation during a drifting boat with potential castaways. This is done by creating a case together with domain experts, analyzing the sociotechnical system within the case using cognitive work analysis and then complementing the analyses with the unmanned autonomous vehicles of WARA-PS. This thesis has shown how the WARA-PS vehicles can be implemented in the case of a drifting boat with potential castaways and how the implementation affects the sociotechnical system. Based on the analyses and opinions of domain experts’ future guidelines has been derived to further the work with sociotechnical aspects in WARA-PS.
WARA-PS

Dissertação de Mestrado Integrado em Engenharia Mecânica apresentada à Faculdade de Ciências e Tecnologia
Autonomous navigation has been successfully presented and developed both on ground and air. Though, autonomy on maritime surface vehicles has proven to be more challenging. In order to have an Autonomous Surface Vehicle (ASV), it ought to have a completely functional Guidance, Navigation and Control (GNC) system.This work presents part of the GNC system for the ASV giving special emphasis on collision avoidance and as the main focus the COLlision REGulationS (COLREGS) mode detection and it's application. For collision avoidance, two submodules were implemented, being the Dynamic Window Approach (DWA) for static obstacles, and a COLREGS compliant algorithm for multi-vehicle encounters, which are set with perimeters of safety.Slights modifications to the original DWA algorithm were introduced in order to better suit the conditions presented, such as the reset of the array of obstacles, when thus are outside of the inner perimeter, in order to reduce computational effort.The COLREGS algorithm implemented is able to detect the COLREGS rule to follow and to generate a new combination of heading and velocity to both, avoid collision and to correspond to the rules requirements.Sensors, such as an IMU and a GNSS system, were responsible to provide total knowledge of the vehicle's position and state, whereas a Lidar and an AIS system, were responsible for the representation of the obstacles and it's attributes. ROS has been employed as the middleware for the ASV, which avoids the need to develop your own code for many aspects of robot control.The computer simulations with Gazebo showed that the collision avoidance system could handle static and dynamic obstacles, with the condition that such dynamic obstacles were vessels with an AIS system integrated. It was demonstrated that the algorithm was capable of detecting the respective rule to follow and thus proceed with a new course which would lead to avoid collision.
Navegação autónoma tem sido desenvolvida e apresentada tanto para a terra como ar. No entanto, autonomia para veículos de superfície marítima tem provado algumas dificuldades. Para desenvolver um veículo autónomo de superfície aquático (ASV), é necessário um sistema de orientação, navegação e controlo (GNC) totalmente operacional. Esta tese apresenta uma parte deste sistema GNC para o ASV com um enfase em algoritmos para evitar colisão, com o maior foco na deteção das regras do mar a seguir, COLREGS, e da sua aplicação.Para os algoritmos para evitar colisão, dois sub-módulos foram implementados, sendo o Dynamic Window Approach (DWA) para obstáculos estáticos, e um algoritmo que segue as regras COLREGS para situações de cruzamento de veículos, sendo definidos com perímetros de segurança. Algumas modificações foram introduzidas relativamente ao algoritmo original de DWA, de modo a melhor responder às condições apresentadas, como um reset da variável de obstáculos, quando estes se encontram fora do perímetro interno, de modo a reduzir o esforço computacional requerido. O algoritmo que segue as regras COLREGS implementado, é capaz de detetar qual a regra a seguir e gera o novo conjunto de orientações e velocidades de modo a evitar colisão e cumprir as regras do mar. Sensores, como um IMU e um sistema GNSS, são responsáveis pela representação das posições e estados do veículo, em que por outro lado, o Lidar e um sistema AIS, são responsáveis pela representação dos obstáculos e seus atributos. ROS foi introduzido como o middleware para o ASV, o que evita a necessidade de desenvolver código para muitos aspetos do controlo do veículo. As simulações realizadas no Gazebo demonstraram que o sistema de evitar colisão consegue dar resposta a obstáculos estáticos e dinâmicos, na condição que os obstáculos dinâmicos sejam veículos com sistemas AIS integrados. Foi demonstrado que este algoritmo é capaz de detetar a respetiva regra a seguir e proceder com uma nova trajetória de modo a evitar colisão.

Autonomous underwater vehicles have been used as tools for underwater operations. Autonomy means the capability to perform deliberative planning on-board without any human interaction. The levels of autonomy of underwater vehicles have been increasing over the years. One essential enabler for autonomous operation concerns docking. Autonomous docking will allow vehicles to operate independently of human interaction in remote areas. Docking concepts range from underwater docking stations to launch and recovery from autonomous surface vessels.The Underwater Systems and Technologies Laboratory (LSTS) has been developing and using this type of vehicles for more than twenty years and therefore the need of automatic docking of vehicles has emerged. This dissertation aims to develop an approach for docking between two existing vehicles from the LSTS fleet. The vehicles would be an Autonomous Surface Vehicle (ASV) \textit{Caravela} and a Light Autonomous Underwater Vehicle (LAUV).For this reason, we study and discuss the derivation and simplification of motion equations for sub-aquatic and surface vehicles and known approaches for docking, between a fixed or a moving dock. We studied a way to have localization between the two vehicles using Global Positioning System (GPS) and/or Ultra-Short Baseline (USBL) and the internal Inertial Measurement Unit (IMU), currently available on the systems. Several state machines where implemented in order to have an optimized and fail-safe docking maneuver even having external disturbances like sea-currents on the docking approach.The whole approach was implemented using the tool-chain developed in LSTS (Dune, IMC and Neptus).

Autonomous underwater vehicles have been used as tools for underwater operations. Autonomy means the capability to perform deliberative planning on-board without any human interaction. The levels of autonomy of underwater vehicles have been increasing over the years. One essential enabler for autonomous operation concerns docking. Autonomous docking will allow vehicles to operate independently of human interaction in remote areas. Docking concepts range from underwater docking stations to launch and recovery from autonomous surface vessels.The Underwater Systems and Technologies Laboratory (LSTS) has been developing and using this type of vehicles for more than twenty years and therefore the need of automatic docking of vehicles has emerged. This dissertation aims to develop an approach for docking between two existing vehicles from the LSTS fleet. The vehicles would be an Autonomous Surface Vehicle (ASV) \textit{Caravela} and a Light Autonomous Underwater Vehicle (LAUV).For this reason, we study and discuss the derivation and simplification of motion equations for sub-aquatic and surface vehicles and known approaches for docking, between a fixed or a moving dock. We studied a way to have localization between the two vehicles using Global Positioning System (GPS) and/or Ultra-Short Baseline (USBL) and the internal Inertial Measurement Unit (IMU), currently available on the systems. Several state machines where implemented in order to have an optimized and fail-safe docking maneuver even having external disturbances like sea-currents on the docking approach.The whole approach was implemented using the tool-chain developed in LSTS (Dune, IMC and Neptus).

碩士
國立交通大學
電控工程研究所
107
Robotic software and hardware systems of autonomous surface vehicles have been developed in transportation, military, and ocean researches for decades. Motivated from marine robot challenge call RobotX in Hawaii in December 2018, we proposed a surface vehicle with the designs of perception and propulsion systems for various tasks, such as obstacle avoidance, entrance, and exit gate. The MOOS-IvP and ROS are used to integrate hardware and software. This work aims to develop essential functionalities of autonomous navigation, from simulation (Gazebo) to real-world water/ocean environments. In Experiment 1 we evaluate how the vehicle executes 5 pre-defined desired trajectories in Gazebo with different noise settings. In real environment, we evaluate the basic capabilities about velocity and acceleration in Experiment 2. We compared two obstacle avoidance approaches in navigating from point A to B, and show the trade-off of safety (minimum distances from obstacles) and efficiency (the time from A to B). Some of the work will be used for the Team NCTU participations of the RobotX competition in Hawaii in 2018.

Adaptive path planning of an autonomous marine vehicle (surface or subsurface) in the role of a communication and navigation aid (CNA) for multiple autonomous underwater vehicles (AUVs) for survey missions is studied. This path planning algorithm can be run before deployment, based on the planned paths of the survey AUVs, or underway, based on information transmitted by the survey AUVs. The planner considers the relative depth of the CNA and survey AUVs (not previously done) allowing the CNA to better aid survey AUVs that maintain a set distance over the ocean floor while surveying. Results are presented from simulations and in-water trials for both pre-deployment and underway planning modes, the latter being preferred since it can adapt to the survey AUV path during the mission. The necessity of bounding the distance between the CNA and any survey AUV in order to bound survey AUV position error is also described.

Maritime Unmanned Vehicles are tools being developed to explore oceans, rivers, lakes and reservoirs. These tools are essential to completely understand the unknown maritime world, since they have relatively low operation costs and can perform several multipurpose investigations. These main applications can be divided in different operations types like oceanographic and atmosphere measurements. One type of these tools are called Autonomous Surface Vehicles (ASV), which, as the name implies, are autonomous vessels that operate at water’s surface. In this dissertation, different types of geometries for a next-generation bathymetric ASV are studied. The first two are more conventional geometries with the first being a flat bottom hull, and the second being a hybrid one. The last one is a new type that has recently gained recognition: a Small-Waterplane-Area Twin-Hull (SWATH) geometry. This one, consists of an axisymmetric body, two small arms that separates the body from the payload, an elliptical nose and a conical tail. All geometries have been designed taking into account the best ranges of empirical/analytical relations, previously studied by other authors. [...]
Os veículos marítimos não tripulados são ferramentas que têm sido desenvolvidas para explorar oceanos, rios, lagos e reservatórios. Estas ferramentas são essenciais para entender completamente o desconhecido mundo marítimo, uma vez que estas têm custos operacionais relativamente baixos e podem realizar variadíssimas investigações. As principais aplicações podem ser divididas em diferentes tipos de operações, como medições oceanográficas e de atmosfera. Uma dessas ferramentas é chamada de Veículo Autónomo de Superfície (ASV), que, como o nome indica, são embarcações autónomas que operam à superfície da água. Nesta dissertação, são estudadas diferentes tipos de geometrias para um ASV batimétrico da próxima geração. As duas primeiras são geometrias mais convencionais, um casco com um fundo plano e uma híbrida. A última, é um novo tipo que recentemente ganhou reconhecimento: uma geometria Small-Waterplane-Area Twin-Hull (SWATH). Esta, consiste num corpo axissimétrico, dois pequenos braços que separam o corpo da carga útil, um nariz elíptico e uma cauda cónica. Todas as geometrias foram projetadas tendo em conta as melhores gamas de relações empíricas/ analíticas, préviamente estudadas por outros autores. [...]

abstract: The need for incorporating game engines into robotics tools becomes increasingly crucial as their graphics continue to become more photorealistic. This thesis presents a simulation framework, referred to as OpenUAV, that addresses cloud simulation and photorealism challenges in academic and research goals. In this work, OpenUAV is used to create a simulation of an autonomous underwater vehicle (AUV) closely following a moving autonomous surface vehicle (ASV) in an underwater coral reef environment. It incorporates the Unity3D game engine and the robotics software Gazebo to take advantage of Unity3D's perception and Gazebo's physics simulation. The software is developed as a containerized solution that is deployable on cloud and on-premise systems. This method of utilizing Gazebo's physics and Unity3D perception is evaluated for a team of marine vehicles (an AUV and an ASV) in a coral reef environment. A coordinated navigation and localization module is presented that allows the AUV to follow the path of the ASV. A fiducial marker underneath the ASV facilitates pose estimation of the AUV, and the pose estimates are filtered using the known dynamical system model of both vehicles for better localization. This thesis also investigates different fiducial markers and their detection rates in this Unity3D underwater environment. The limitations and capabilities of this Unity3D perception and Gazebo physics approach are examined.
Dissertation/Thesis
Masters Thesis Computer Science 2020

Unmanned Underwater Vehicles (UUVs), such as Autonomous Underwater Vehicles (AUVs) and Remote Operated Vehicles (ROVs) are versatile tools, suitable for many activities in different fields. Besides the obvious scientific interest in ocean exploration by itself, UUVs can be useful in industrial applications such as monitoring underwater pipelines and communication lines and may also be used for military purposes. Even though their main advantage is avoiding risk to human lives, they also allow the simplification of missions' logistics and the reduction of their costs. The performance of any underwater vehicle in any given task is deeply affected by the precision of its localization system. The main challenge in underwater localization is the significant attenuation of any Radio Frequency (RF) signal underwater, which prevents the use of many common location methods such as the Global Positioning System (GPS). In order to overcome this difficulty, multiple localization techniques have been developed, based on different technologies. To increase the number of potential UUV applications, continued development and improvement of every subsystem is needed. The aforementioned localization system is one with much room for improvement, with new solutions frequently arising. Cooperative Navigation (CN) is one of these solutions. In CN, a UUV determines its position relatively to another vehicle that can determine its own position with certainty, like a surface vehicle equipped with GPS. This vehicle is called a Communication and Navigation Aid (CNA) vehicle. CN localization methods using only one support vehicle are appealing, to lower costs and logistics, but they require good positioning of the CNA vehicle to maintain observability and achieve good performances.The purpose of this dissertation is to develop a navigation system for a CNA surface vehicle. In a CN localization system, the UUV estimates its own pose with Dead Reckoning (DR) techniques and periodically obtains ranges from CNA vehicles to improve this estimate. The direction of the obtained ranges is particularly important, hence the need to adapt the surface vehicle's trajectory to the requirements of the UUV. The navigation system developed should be able to determine in real time the trajectory of the CNA that minimizes the position uncertainty of the UUV.

Unmanned Underwater Vehicles (UUVs), such as Autonomous Underwater Vehicles (AUVs) and Remote Operated Vehicles (ROVs) are versatile tools, suitable for many activities in different fields. Besides the obvious scientific interest in ocean exploration by itself, UUVs can be useful in industrial applications such as monitoring underwater pipelines and communication lines and may also be used for military purposes. Even though their main advantage is avoiding risk to human lives, they also allow the simplification of missions' logistics and the reduction of their costs. The performance of any underwater vehicle in any given task is deeply affected by the precision of its localization system. The main challenge in underwater localization is the significant attenuation of any Radio Frequency (RF) signal underwater, which prevents the use of many common location methods such as the Global Positioning System (GPS). In order to overcome this difficulty, multiple localization techniques have been developed, based on different technologies. To increase the number of potential UUV applications, continued development and improvement of every subsystem is needed. The aforementioned localization system is one with much room for improvement, with new solutions frequently arising. Cooperative Navigation (CN) is one of these solutions. In CN, a UUV determines its position relatively to another vehicle that can determine its own position with certainty, like a surface vehicle equipped with GPS. This vehicle is called a Communication and Navigation Aid (CNA) vehicle. CN localization methods using only one support vehicle are appealing, to lower costs and logistics, but they require good positioning of the CNA vehicle to maintain observability and achieve good performances.The purpose of this dissertation is to develop a navigation system for a CNA surface vehicle. In a CN localization system, the UUV estimates its own pose with Dead Reckoning (DR) techniques and periodically obtains ranges from CNA vehicles to improve this estimate. The direction of the obtained ranges is particularly important, hence the need to adapt the surface vehicle's trajectory to the requirements of the UUV. The navigation system developed should be able to determine in real time the trajectory of the CNA that minimizes the position uncertainty of the UUV.

abstract: In the sport of competitive water skiing, the skill of a human boat driver can affect athletic performance. Driver influence is not necessarily inhibitive to skiers, however, it reduces the fairness and credibility of the sport overall. In response to the stated problem, this thesis proposes a vision-based real-time control system designed specifically for tournament waterski boats. The challenges addressed in this thesis include: one, the segmentation of floating objects in frame sequences captured by a moving camera, two, the identification of segmented objects which fit a predefined model, and three, the accurate and fast estimation of camera position and orientation from coplanar point correspondences. This thesis discusses current ideas and proposes new methods for the three challenges mentioned. In the end, a working prototype is produced.
Dissertation/Thesis
Masters Thesis Computer Science 2014

碩士
國立臺灣大學
工程科學及海洋工程學研究所
107
Due to the potential of the offshore wind energy in the Taiwan Strait, the development of the offshore wind farm is cost-effective. As the green energy concept growing, Taiwan are actively promoting the project “Thousand Wind Turbines” from 2012. Up to now, there are two mono-pile foundation wind turbines completed in 2016. The objective is to build up to 800 offshore wind turbines, and 450 onshore wind turbines before 2030. However, the expected develop area for potential offshore wind farms has overlapped with the habitat of the Sousa chinensis (also called Indo-Pacific Humpback Dolphin or Chinese White Dolphin). The impact noise from the pile driving process have shown to cause Temporary Threshold Shift (TTS) even Permanent Threshold Shift (PTS) to marine mammals at specific range from pile driving spot. Furthermore, out of the PTS and TTS range, noises from the construction are still high enough to disturb and even change the behavior of marine mammals. As a result, to know if there are dolphins in the construction area is the most important. This paper provides a process for checking if there are dolphin in the specific area and using unmanned surface vehicle to track the source and find the area they are. This process has 4 main parts, including real-time dolphin whistle detection, real-time shore side monitoring system, underwater acoustic source localization and source tracking behavior of the surface vehicle. This thesis will go into the details of those parts.