Autonomous underwater vehicles have become a popular countermeasure to naval mines. Saab’s AUV62-MR detects, locates and identifies mine-like objects through three phases. By extracting functionality from the AUV62-MR and placing it on a second vehicle, it is suggested that the second and third phases can be performed in parallel. This thesis investigates how to design the second vehicle so that the runtime of the mine reconnaissance process is minimized. A simulation framework is implemented to simulate the second and third phases of the mine reconnaissance process in order to test various design choices. The vehicle design choices in focus are the size and the route planning of the second vehicle. The route-planning algorithms investigated in this thesis are a nearest neighbour algorithm, a simulated annealing algorithm, an alternating algorithm, a genetic algorithm and a proposed Dubins simulated annealing algorithm. The algorithms are evaluated both in a static environment and in the simulation framework. Two different vehicle sizes are investigated, a small and a large, by evaluating their performances in the simulation framework. This thesis takes into account the limited travelling distance of the vehicle and implements a k-means clustering algorithm to help the route planner determine which mine-like objects can be scanned without exceeding the distance limit. The simulation framework is also used to evaluate whether parallel execution of the second and third phases outperforms the current sequential execution. The performance evaluation shows that a major reduction in runtime can be gained by performing the two phases in parallel. The Dubins simulated annealing algorithm on average produces the shortest paths and is considered the preferred route-planning algorithm according to the performance evaluation. It also indicates that a small vehicle size results in a reduced runtime compared to a larger vehicle.

When working under water at great depths, or in tough environments, remotely controlled underwater vehicles, so called ROV:s, are a good tool too use. With the help of these robots it is possible to work for longer periods of time and at larger depths then with ordinary divers. Ocean Robotics is a company from Linköping, Sweden, that has over 40 years of experience with ROV:s. They produce several different models for different kind of jobs. The purpose of this project was to develop a concept of a device that keeps the tension constant on the tether that connects the ROV with the operator. The tension needs to be constant when the tether is being rolled in on and out from the winch. The work was executed on behalf of Ocean Robotics and a prerequisite was that it should be possible to integrate the device with the winch system they have today. The work have followed Ulrich and Eppingers method for product development and together with requirements from the contractor several concepts was generated. The concepts were validated in a decision matrix and the concepts with the highest score was modeled in the CAD-program CREO Parametrics. The concepts got further developed and was compared against each other once again. Which generated the final concept. Furthermore a couple of friction tests were executed to find appropriate materials for the construction. The final concept will be mounted on the winch and connected to Ocean Robotics self reversing screw. To measure the tension on the tether a load cell is used and the tether is fed by a rubberized wheel that is driven by an electric motor. To adjust the force around the tether a trapezoidal thread is driven by another electric motor, which adjust the pinch wheels height. The tether goes as a tangent between the two wheels that has a profile that insures that the force goes around the tether.

<div>Exploring and surveilling the marine environment away from shore is critical for scientific, economic, and military purposes as we progress through the 21st century. Until recently, these missions far from shore were only possible using manned surface vehicles. Over the past decade, advances in energy density, actuators, electronics, and controls have enabled great improvements in vehicle endurance, yet, no solution is capable of supporting persistent operation especially when considering power hungry scientific surveys. This dissertation summarizes contributions related to the development of an adaptable underwater docking station and associated navigation solutions to allow applications in the wide range of maritime missions. The adaptable docking system is a novel approach to the standard funnel shaped docking station design that enables the dock to be collapsible, portable, and support a wide range of vehicles. It has been optimized and tested extensively in simulation. Field experiments in both pool and open water validate the simulation results. The associated control strategies for approach and terminal homing are also introduced and studied in simulation and field trials. These strategies are computationally efficient and enable operation in a variety of scenarios and conditions. Combined, the adaptable docking system and associated navigation strategies can form a baseline for future extended endurance missions away from manned support.</div>

Visual Odometry (VO) is the process of estimating the relative motion of a vehicle by using solely image data gathered from the camera. In underwater environments, VO becomes extremely challenging but valuable since ordinary sensors for on-road localization are usually unpractical in these hostile environments. For years, VO methods have been purely based on Computer Vision (CV) principles. However, the recent advances in Deep Learning (DL) have ushered in a new era for VO approaches. These novel methods have achieved impressive performance with state-of-the-art results on urban datasets. Nevertheless, little effort has been made to push learning-based research towards natural environments, such as underwater. Consequently, this work aims to bridge the research gap by evaluating the effectiveness of the learning-based approach in the navigation of Autonomous Underwater Vehicles (AUVs). We compare two learning-based methods with a traditional feature-based method on the Underwater Caves dataset, a very challenging dataset collected in the unstructured environment of an underwater cave complex. Extensive experiments are thus conducted training the models on this dataset. Moreover, we investigate different aspects and propose several improvements, such as sub-sampling the video clips to emphasize the camera motion between consecutive frames, or training exclusively on images with relevant content discarding those with dark borders and representing solely sandy bottoms. Finally, during the training, we also leverage underwater images from other datasets, hence acquired from different cameras. However, the best improvement is obtained by penalizing rotations around the x-axis of the camera coordinate system. The three methods are evaluated on test sequences that cover different lighting conditions. In the most favorable environments, although learning-based methods are not up to par with the feature-based method, the results show great potential. Furthermore, in extreme lighting conditions, where the feature-based baseline sharply fails to bootstrap, one of the two learning-based methods produces instead qualitatively good trajectory results, revealing the power of the learning-based approach in this peculiar context. / Visuell Odometri (VO) används för att uppskatta den relativa rörelsen för ett fordon med hjälp av enbart bilddata från en eller flera kameror. I undervattensmiljöer blir VO extremt utmanande men värdefullt eftersom vanliga sensorer för lokalisering vanligtvis är opraktiska i dessa svåra miljöer. I åratal har VO-metoder enbart baserats på klassisk datorseende. De senaste framstegen inom djupinlärning har dock inlett en ny era för VO-metoder. Dessa nya metoder har uppnått imponerande prestanda på dataset urbana miljöer. Trots detta har ganska lite gjorts för att driva den inlärningsbaserad forskningen mot naturliga miljöer, till exempel under vattnet. Följaktligen syftar detta arbete till att överbrygga forskningsgapet genom att utvärdera effektiviteten hos det inlärningsbaserade tillvägagångssättet vid navigering av autonoma undervattensfordon (AUV). Vi jämför två inlärningsbaserade metoder med en traditionell nyckelpunktsbaserad metod som referens. Vi gör jämförelsen på Underwater Caves-datasetet, ett mycket utmanande dataset som samlats in i den ostrukturerade miljön i ett undervattensgrottkomplex. Omfattande experiment utförs för att träna modellerna på detta dataset. Vi undersöker också olika aspekter och föreslår flera förbättringar, till exempel, att delsampla videoklippen för att betona kamerarörelsen mellan på varandra följande bildrutor, eller att träna på en delmängd av datasetet bestående uteslutande på bilder med relevant innehåll för att förbättra skattningen av rörelsen. Under träningen utnyttjar vi också undervattensbilder från andra datamängder, och därmed från olika kameror. Den bästa förbättringen uppnås dock genom att straffa skattningar av stora rotationer runt kamerakoordinatsystemets x-axel. De tre metoderna utvärderas på testsekvenser som täcker olika ljusförhållanden. I de mest gynnsamma miljöerna visar resultaten stor potential, även om de inlärningsbaserade metoder inte är i nivå med den traditionella referensmetoden. Vid extrema ljusförhållanden, där referensmetoden misslyckas att ens initialisera, ger en av de två inlärningsbaserade metoderna istället kvalitativt bra resultat, vilket demonstrerar kraften i det inlärningsbaserade tillvägagångssättet i detta specifika sammanhang.

<p>Small, low cost Autonomous underwater vehicles (AUVs) provide ideal platforms for shallow water survey, as they are capable of unmanned navigation and can be programmed to acquire data at constant depth, or constant altitude above the seabed. AUVs can be deployed under most sea states and are unaffected by vessel motions that often degrade sonar and magnetometer data quality. The integration of sonar and magnetometer sensors on AUV’s is challenging, however, due to limited payload and strong magnetic fields produced by the vehicle motor.</p> <p>In this study, a Marine Magnetics Explorer Overhauser magnetometer was mated to a portable AUV (OceanServer Iver2) creating the first practical AUV- deployed magnetic survey system. To eliminate magnetic interference from the AUV, the magnetometer was tethered to the AUV with a 5 m tow cable, as determined by static and dynamic instrument testing. The results of the magnetic tests are presented, along with field data from a shallow water test area in Lake Ontario near Toronto, Canada. AUV-acquired magnetic survey data were compared directly with a conventional boat-towed magnetic survey of the same area. The AUV magnetic data were of superior quality despite being collected in rough weather conditions that would have made conventional survey impossible. The resulting high-resolution total magnetic intensity and analytic signal maps clearly identify several buried and surface ferrometallic targets that were verified in 500-kHz side- scan sonar imaging and visual inspection by divers.</p> / Master of Science (MSc)

Dans ce travail, l’optimisation de la propulsion et de la commande des AUV (Autonomous Underwater Vehicles en anglais) est développée. Le modèle hydrodynamique de l’AUV est examiné. Egalement, son système de propulsion est étudié et des modèles pour des solutions de propulsion différentes (fixe et vectorielle) sont développés dans le cadre de la mobilité autonome.Le modèle et l’identification de la technologie de propulsion dite fixe sont basés sur un propulseur disponible commercialement. Le système de propulsion vectoriel est basé sur un prototype de propulseur magneto-couplé reconfigurable (PMCR) développé à l’IRDL-ENIB.Une méthode de commande non linéaire utilisant le modèle hydrodynamique de l’AUV est développée et son adaptation à deux systèmes de propulsion est présentée. Des analyses portant sur la commandabilité du robot et l’application de cette commande à différents systèmes sont proposées. L’optimisation globale est utilisée pour trouver des topologies propulsives et des paramètres de commande adaptés à la réalisation de tâches robotiques spécifiques. L’optimisation réalisée permet de trouver des solutions capables d’assurer le suivi de trajectoire et de minimiser la consommation énergétique du robot. L’optimisation utilise un algorithme génétique (algorithme évolutionnaire), une méthode d’optimisation stochastique appliquée ici à la conception orientée tâche de l’AUV. Les résultats de cette optimisation peuvent être utilisés comme une étape préliminaire dans la conception des AUVs, afin de donner des pistes pour améliorer les capacités de la propulsion.La technique d’optimisation est également appliquée au robot RSM (fabriqué au sein de l’IRDL-ENIB) en modifiant seulement quelques paramètres de sa topologie propulsive. Cela afin d’obtenir des configurations de propulsion adaptées au cours d’une seule et même mission aux spécificités locomotrices des tâches rencontrées : reconfiguration dynamique de la propulsion de l’AUV. / In this PhD thesis, the optimization of the propulsion and control of AUVs is developed. The hydrodynamic model of the AUVs is examined. Additionally, AUV propulsion topologies are studied and models for fixed and vectorial technology are developed. The fixed technology model is based on an off the shelf device, while the modeled vectorial propulsive system is based on a magnetic coupling thruster prototype developed in IRDL (Institut de Recherche Dupuy de Lôme) at ENI Brest. A control method using the hydrodynamic model is studied, its adaptation to two AUV topologies is presented and considerations about its applicability will be discussed. The optimization is used to find suitable propulsive topologies and control parameters in order to execute given robotic tasks, speeding up the convergence and minimizing the energy consumption. This is done using a genetic algorithm, which is a stochastic optimization method used for task-based design.The results of the optimization can be used as a preliminary stage in the design process of an AUV, giving ideas for enhanced propulsive configurations. The optimization technique is also applied to an IRDL existing robot, modifying only some of the propulsive topology parameters in order to readily adapt it to different tasks, making the AUV dynamically reconfigurable.

Approved for public release, distribution is unlimited / In order to plug-in current open sourced, open standard Java programming technology into the building blocks of the US Navy's ForceNet, first, stove-piped systems need to be made extensible to other pertinent applications and then a new paradigm of adopting extensible and cross-platform open technologies will begin to bridge gaps with old and new weapons systems. The battle-space picture in real time and with as much detail, or as little detail needed is now a current vital requirement. Access to this information via wireless laptop technology is here now. Transmission of data to increase the resolution of that battle-space snapshot will invariably be through noisy links. Noisy links such as found in the shallow water littoral regions of interest will be where Autonomous Underwater and Unmanned Underwater Vehicles (AUVs/UUVs) are gathering intelligence for the sea warrior in need of that intelligence. The battle-space picture built from data transmitted within these noisy and unpredictable acoustic regions demands efficiency and reliability features abstract to the user. To realize this efficiency Extensible Markup Language (XML) Schema-based Binary Compression (XSBC), in combination with Vandermode-based Forward Error Correction (FEC) erasure codes, offer the qualities of efficient streaming of plain text XML documents in a highly compressed form, and a data self-healing capability should there be loss of data during transmission in unpredictable transmission mediums. Both the XSBC and FEC libraries detailed in this thesis are open sourced Java Application Program Interfaces (APIs) that can be readily adapted for extensible, cross-platform applications that will be enhanced by these desired features to add functional capability to ForceNet for the sea warrior to access on demand, at sea and in real-time. These features will be presented in the Autonomous Underwater Vehicle (AUV) Workbench (AUVW) Java-based application that will become a valuable tool for warriors involved with Undersea Warfare (UW). / Lieutenant, United States Navy

This project had two primary goals: (1) to explore opportunities to further a deep-ocean vehicle’s reach using alternative pressure spheres, and (2) to implement an existing deep-ocean vehicle (lander) in active scientific research. I gained a greater understanding of the limitations and design choices made for existing pressure spheres using Finite Element Analysis (FEA). My simplified FEA model predicted sphere failure for the existing 30% Fiber Glass 70% Nylon injection molded spheres at an external pressure of 3,954psi or 2,690m ocean-depth (only a 7.38% error compared to the tested minimum failure depth), so I determined it a valid model. I also explored alternative designs and materials that could be used for pressure spheres in deep-sea applications. Existing pressure sphere models filled with an incompressible fluid failed at 12,670psi or 8,621m ocean-depth - over three times the depth of the same sphere filled with air. Next, I varied the sphere thickness of existing spheres to determine its impact on depth rating. While the increased thickness did provide an increase in depth rating, there were diminishing returns as the sphere was made thicker. I deemed both of these design options infeasible for our application. To consider the use of laminated composite spheres, the addition of an equatorial ring was required to manufacture O-ring seals safely and reliably. A simple cylindrical equatorial ring model using a stainless-steel ring had a predicted failure at 3,017psi or 2,053m ocean-depth. While this model predicted failure at 637m shallower than the sphere without the ring, it was the only ring material tested to reach the rated depth for the existing pressure spheres (2km), so I concluded stainless-steel is the best ring material. A spherical stainless-steel equatorial ring design was then analyzed which predicted failure at 3,915psi or 2,664m ocean-depth – only 8.3% less than the original sphere with no ring. Because of its successful performance and near identical results to the original model, I determined a stainless-steel spherical equatorial ring is the best option for laminated composite sphere sealing. Finally, I analyzed three different kinds of laminated composite pressure spheres: two carbon fiber and one fiber glass. Each laminate was designed to be quasi-isotropic and as close to 0.8” thick as possible to keep it consistent with the original sphere design. The sphere made of 584 Carbon Fiber with a lay-up of: [[-45/45/0/90]6]s was found to predict failure at 10,000psi or 6,804m ocean-depth, more than 2.5 times that of the original sphere. Next, a model made of 282 Carbon Fiber with a lay-up of: [[-45/45/0/90]11]s predicted failure at 9,242psi or 6,289m ocean-depth – more than 2.3 times as deep as the original pressure spheres. Lastly, a sphere of 7781 Fiber Glass with a lay-up of: [[-45/45/0/90]11]s predicted failure at 6,630psi or 4,511m ocean-depth – about two-thirds the depth of the 584 Carbon Fiber composite, but more than 1.6 times the depth of the original sphere. While real-life applications of these materials would include design modifications and manufacturing imperfections which would lower their maximum depth rating, these results are highly encouraging and show that all three materials could be viable options for future production. Additionally, through partnership with Dr. Crow White and his marine science undergraduate students, I completed numerous deployments for a Before and After Controlled Impact (BACI) study on the area of the proposed windfarm off the coast of Morro Bay, CA. Many modifications were made to the existing lander which enabled it to successfully be implemented in these studies including a new bait containment unit, light color filters, a GPS tracking device, and a large vessel recovery device. A total of 5 pier deployments and 3 boat deployments were conducted by my team over the course of 6-months. Planning for these deployments included accounting for budgeting, weather, permitting, and multi-organizational logistics while working with both NOAA and the Cal Poly marine operations staff.

This master thesis project is focused on the development of an LQR controller and its comparison with other controllers (PID and MPC), in order to successfully control an Autonomous Underwater Vehicle manipulation system. The modelling of the manipulator was performed first in Matlab and later on in Simulink-Simscape. Once the manipulator was integrated with the AUV model, the LQR controller was also developed initially in Matlab and then in Simulink. The controller was then extracted from Simulink as a C-code and verified in Stonefish. After confirming that the LQR code was working in Stonefish, its results from Simulink were compared with PID and MPC results for two different trajectories. The data for comparison and statistical analysis were divided into the two trajectory scenarios (horizontal and vertical) since the weight matrices of both controllers were different. Looking at the system’s overall behavior the Model Predictive Control (MPC) and LQR had similar results, regarding the rise time, overshoot, steady-state error and robustness to disturbances. An anticipated fact for the MPC was that it takes the longest run time for both scenarios. Lastly, as expected the PID had the worst response of all three controllers, in both scenarios. Implementing a PID on a nonlinear system, produced many oscillations without being able to stabilize at the reference value, thus giving a large steady-state error. In addition, it could not counteract the noise disturbances in the signal. / Detta examensarbete är inriktat på utvecklingen av en LQR-styrenhet och dess jämförelse med andra kontroller (PID och MPC), för att framgångsrikt styra ett autonomt undervattensfordon-manipulationssystem. Modelleringen av manipulatorn utfördes först i Matlab och senare i Simulink-Simscape. När manipulatorn väl hade integrerats med AUV modellen, utvecklades LQR styrenheten också inledningsvis i Matlab och sedan i Simulink. Kontrollenheten extraherades sedan från Simulink som en C-kod och verifierades i Stonefish. Efter att ha bekräftat att LQR koden fungerade i Stonefish, jämfördes resultaten från Simulink med PID och MPC resultat för två olika banor. Data för jämförelse och statistisk analys delades in i de två bana-scenarierna (horisontella och vertikala), eftersom viktmatriserna för båda kontrollerna var olika. När man tittar på systemets övergripande beteende hade Model Predictive Controller (MPC) och LQR liknande resultat när det gäller stigningstid, överskott, steady-state fel och robusthet mot störningar. Ett förväntat faktum för MPC var att det tar den längsta körtiden för båda scenarierna. Slutligen, som väntat, hade PID det sämsta svaret av alla tre kontrollerna, i båda scenarierna. Implementering av ett PID på ett olinjärt system gav många oscillationer utan att kunna stabilisera sig vid referensvärdet, vilket gav ett stort steady-state fel. Dessutom kunde den inte motverka bullerstörningarna i signalen.