The overall focus of this research was to achieve the first detailed understanding of temporal and geographical distributions of inorganic-nitrogen-nutrients within an oligotrophic euphotic zone. In addition to low supply of nutrients, the uptake of nutrients by phytoplankton within the euphotic zone draws the nutrient concentrations down, resulting in very low concentrations of these nutrients and results in these regions being classified as oligotrophic. The site selected for the research was the West Florida Shelf (WFS). There were two main challenges. One was that the detection limits of the standard chemistries used to determine inorganic nitrogen nutrients are not low enough to permit the evaluation of the concentration of these nutrients within an oligotrophic euphotic zone. The other challenge was the adaptation and design of highly sensitive, robust, and simple instrumentation to resolve and evaluate horizontal nutrient distributions within the euphotic zone for both ship-based and Autonomous Underwater Vehicle (AUV) based platforms in near real time. With these obstacles in mind three major goals were set. First was the development of a simple and robust chemistry that could detect nitrite and nitrate with a suitably low detection limit (approximately 10 nanomolar) and could also be coupled with a highly sensitive chemistry previously developed for ammonium with the same characteristics, and incorporate these chemistries into a single laboratory analyzer designed to monitor the surface distribution of these nutrients in the water sampled with a ships flow-through system.

Examensarbetet utvärderar undervattensfarkosten Stursk som byggts av studenter på Linköpings universitet och förbättrar sedan ett flertal komponenter på farkosten, samt lämnar förslag på framtida förbättringar. Rapporten behandlar motorkapslingen, lyftpunkterna, strömbrytaren, elektroniken, tryckskrovet, varvtalsmätning, tryckgivare och viktigast av allt, granskar farkostens manövreringsförmåga närmare.

Underwater vehicles that use differential thrust for surge and yaw motion control have the advantage of increased maneuverability. Unfortunately, such vehicles usually don’t have thrusters/actuators to control the lateral movements. Hence, they fall into the underactuated vehicle category. The goal of the work in this thesis is to develop an autonomous control system for a differential thrust underwater remotely operated vehicle (ROV) to track predefined position trajectories. This is challenging because the mathematical model for underwater vehicles is highly nonlinear and the environmental disturbances are usually strong and unpredictable. These factors make the design of the control system very difficult. In this work, we use the VideoRay Pro III micro ROV as the test platform, on which we design an autonomous control system. We first present the development and analysis of a hydrodynamic model of the VideoRay Pro III using both analytical and experimental approaches. Based on this model, a state estimator is then designed using the unscented Kalman filter, which yields better estimates of the system states and their uncertainty level in a highly nonlinear system than the commonly used extended Kalman filter. In the controller design, the integrator backstepping technique is used to achieve a Lyapunov stable trajectory tracking controller based on the work by A. P. Aguiar et al. We extended their work by further considering the quadratic drag terms in the vehicle’s hydrodynamic model. The sliding mode control is used to design the bearing and depth controller. Finally, the autonomous control system is validated by simulation and experimental tests. It is shown that the VideoRay Pro III is able to track the predefined trajectory within error range of 0.5 meters.

As the largest unexplored area on earth, the underwater world has unlimited at traction to marine scientists. Due to the complexity of the underwater environment and the limitations of human divers, underwater exploration has been facilitated by the use of submarines, Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). In recent years, use of autonomous control systems being integrated with visual sensors has increased substantially, especially in marine applications involving guidance of AUVs. In this work, autonomous fish-tracking via AUV with vision servoing control system is studied with the purpose of assisting marine biologists in gathering detailed information about the behaviors, habits, mobility, and local and global distributions of particular fish species. The main goal of this work in this thesis is to develop an AUV sensing system, including both video and auxiliary sonar, which has the ability to carry out visually guided autonomous tracking of a particular species of fish, Large Mouth Bass. A key in enabling fish-tracking involves the development of a vision-processing algorithm to measure the position of the vehicle relative to the fish. It is challenging because of the complex nature of the underwater environment including dynamic and varied lighting conditions, turbulent water, suspended organic particles and various underwater plants and animals, and the deformable body of fish while swimming. These issues cause target fish identification by computer vision processing extremely difficult. In automated fish-tracking work, we provide two valid and efficient segmentation and recognition vision algorithms to identify a fish from the natural underwater environment: one is a feature extraction algorithm based on Gabor filter texture segmentation and a new approach that we call projection curve recognition. It is able to extract the feature on the fish tail and body and successfully describe the fish as two straight line segments. The second algorithm is SIFT based fish recognition algorithm. The SIFT approach introduced by David Lowe in 1999 extracts distinctive invariant features to scaling, illumination, rotation or translation of the image. The reliable keypoints matching in the database of keypoints from target fish is implemented by Best-Bin-First (BBF) algorithm. Clustering keypoints that agree on the possible object with Hough transform are identified as the object fish, reliable recognition is possible with as few as 3 features. Finally, a dynamic recognition process was designed using continuously updated fish model to match and recognize the target fish from a series of video frames. The SIFT Based recognition algorithm is effective and efficient in identifying Large Mouth Bass in a natural cluttering underwater environment. For a monocular camera system, the depth of field is extremely hard to obtain by vision processing. Hence, the system is augmented with a forward-looking digital image micro sonar. With the sonar image processing algorithm, the target fish is recognized. Sonar can not only provide the relative range between the fish and AUV, but also assist in identifying the target. Finally, the relative position and orientation of the fish in the image plane is estimated using an image processing method, transforming the coordinates between camera, sonar and AUV, and applying the estimation algorithm. The results of o off-line data processing taken from a natural Lake environment shows these computer vision algorithms for identifying fish and state estimation are efficient and successful. The proposed system has potential to enable a vision servo control system of AUV to reliably track a target fish in natural underwater environment.

Underwater vehicles that use differential thrust for surge and yaw motion control have the advantage of increased maneuverability. Unfortunately, such vehicles usually don’t have thrusters/actuators to control the lateral movements. Hence, they fall into the underactuated vehicle category. The goal of the work in this thesis is to develop an autonomous control system for a differential thrust underwater remotely operated vehicle (ROV) to track predefined position trajectories. This is challenging because the mathematical model for underwater vehicles is highly nonlinear and the environmental disturbances are usually strong and unpredictable. These factors make the design of the control system very difficult. In this work, we use the VideoRay Pro III micro ROV as the test platform, on which we design an autonomous control system. We first present the development and analysis of a hydrodynamic model of the VideoRay Pro III using both analytical and experimental approaches. Based on this model, a state estimator is then designed using the unscented Kalman filter, which yields better estimates of the system states and their uncertainty level in a highly nonlinear system than the commonly used extended Kalman filter. In the controller design, the integrator backstepping technique is used to achieve a Lyapunov stable trajectory tracking controller based on the work by A. P. Aguiar et al. We extended their work by further considering the quadratic drag terms in the vehicle’s hydrodynamic model. The sliding mode control is used to design the bearing and depth controller. Finally, the autonomous control system is validated by simulation and experimental tests. It is shown that the VideoRay Pro III is able to track the predefined trajectory within error range of 0.5 meters.

As the largest unexplored area on earth, the underwater world has unlimited at traction to marine scientists. Due to the complexity of the underwater environment and the limitations of human divers, underwater exploration has been facilitated by the use of submarines, Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). In recent years, use of autonomous control systems being integrated with visual sensors has increased substantially, especially in marine applications involving guidance of AUVs. In this work, autonomous fish-tracking via AUV with vision servoing control system is studied with the purpose of assisting marine biologists in gathering detailed information about the behaviors, habits, mobility, and local and global distributions of particular fish species. The main goal of this work in this thesis is to develop an AUV sensing system, including both video and auxiliary sonar, which has the ability to carry out visually guided autonomous tracking of a particular species of fish, Large Mouth Bass. A key in enabling fish-tracking involves the development of a vision-processing algorithm to measure the position of the vehicle relative to the fish. It is challenging because of the complex nature of the underwater environment including dynamic and varied lighting conditions, turbulent water, suspended organic particles and various underwater plants and animals, and the deformable body of fish while swimming. These issues cause target fish identification by computer vision processing extremely difficult. In automated fish-tracking work, we provide two valid and efficient segmentation and recognition vision algorithms to identify a fish from the natural underwater environment: one is a feature extraction algorithm based on Gabor filter texture segmentation and a new approach that we call projection curve recognition. It is able to extract the feature on the fish tail and body and successfully describe the fish as two straight line segments. The second algorithm is SIFT based fish recognition algorithm. The SIFT approach introduced by David Lowe in 1999 extracts distinctive invariant features to scaling, illumination, rotation or translation of the image. The reliable keypoints matching in the database of keypoints from target fish is implemented by Best-Bin-First (BBF) algorithm. Clustering keypoints that agree on the possible object with Hough transform are identified as the object fish, reliable recognition is possible with as few as 3 features. Finally, a dynamic recognition process was designed using continuously updated fish model to match and recognize the target fish from a series of video frames. The SIFT Based recognition algorithm is effective and efficient in identifying Large Mouth Bass in a natural cluttering underwater environment. For a monocular camera system, the depth of field is extremely hard to obtain by vision processing. Hence, the system is augmented with a forward-looking digital image micro sonar. With the sonar image processing algorithm, the target fish is recognized. Sonar can not only provide the relative range between the fish and AUV, but also assist in identifying the target. Finally, the relative position and orientation of the fish in the image plane is estimated using an image processing method, transforming the coordinates between camera, sonar and AUV, and applying the estimation algorithm. The results of o off-line data processing taken from a natural Lake environment shows these computer vision algorithms for identifying fish and state estimation are efficient and successful. The proposed system has potential to enable a vision servo control system of AUV to reliably track a target fish in natural underwater environment.

The different between Remotely Operated Vehicle (ROV) and Autonomous Underwater Vehicle (AUV) is that ROV is connected with the main computer by the electronic cable, so the operator can control the vehicle depending on the environment showing on the monitor; However, AUV is dependent on the received data to autonomously respond the condition via controlling program. In our research, we wanted to use the General Purpose Controller, which had been developed in the previous experiment, in the mission-mode to construct our AUV system for remaining the original ROV controlling system and switching mode between AUV system and ROV system. The mission was divided into primary and secondary mission written by the txt file which is known as mission script, including execute time, target, and mission type etc. In addition, we used the Watch Dog Timer (WDT) in our AUV for the security procedure. When the mission is failed or over the setting time, the AUV will change to the security mode and go forward to the water surface. The other topic in this research wanted to use the Seafloor Laser Scanner (SLS), which was mounted on the AUV, to improve the scanning efficiency. However, when the scanner was working, the AUV had to maintain the stable altitude to the sea floor, so the accurately output power of thruster is needed to be considered and tested. In this part, we found out the properly controlling way in the small water tank first, and then checked the attitude and scanning system in the swimmer pool and towing tank in NCKU respectively, to prove the ability of SLS of AUV system.

This thesis develops a Bayesian inversion algorithm for autonomous underwater vehicle localization, and carries out a study of several factors contributing to localization accuracy in an underwater acoustic positioning system. Specifically, a ray-based algorithm is described that estimates target position through the linearized inversion of transmission arrival time differences, and provides linearized uncertainty estimates for model parameters. Factors contributing to source localization uncertainty considered here included: (1) modelling transmission paths accounting for refraction due to a depth-varying SSP instead of using a constant sound-speed approximation and straight-line propagation, (2) inverting for a potential bias in the measured sound-speed profile, (3) accounting for errors in hydrophone position by including these positions as unknown parameters in the inversion, and (4) applying path-dependent timing correction factors to account for lateral variability in the sound-speed profile. In each case, nonlinear Monte Carlo analysis is applied in which a large number of noisy data sets are considered, to obtain statistical measures of the localization improvement that results by addressing these factors. / Graduate

Underwater localization is a difficult task when it comes to making the system autonomous due to the unpredictable environment. The fact that radio signals such as GPS cannot be transmitted through water makes autonomous movement and localization underwater even more challenging. One specific method that is widely used for autonomous underwater navigation applications is Simultaneous Localization and Mapping (SLAM), a technique in which a map is created and updated while localizing the vehicle within the map. In SLAM, feature detection is used in landmark extraction and data association by examining each pixel and differentiating landmarks pixels from those of the background. Previous research on the performance of different feature detection methods have been done in environments such as cisterns and caverns where the effects of the ocean are reduced. Our objective, however, is to achieves robust localization in the open ocean environment of the Cal Poly pier. This thesis performs a comparative study between different feature detection methods including Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented FAST and Rotated BRIEF (ORB) on different sensors. We evaluate the feature detection and matching performance of these algorithms in a simulated open-ocean environment.

The focus of this work is to design and construct a version of the secondary propulsion units used on US Navy submarines for the Virginia Tech 690 autonomous underwater vehicle. These units were used to demonstrate a control system developed in a separate study which allowed the vehicle to autonomously perform advance maneuvers such as course-keeping, mooring and obstacle avoidance. The study of the miniaturized thrusters prompted an in-depth look into two thruster designs. The first was a retractable rimdriven propeller design which was found to be too power inefficient for implementation. The final design was an azimuthing ducted propeller capable of vectoring thrust 360 degrees. Two body sections containing an implementation of the ducted propeller design were constructed and mounted to the 690 vehicle. Tests were successfully conducted in a pool. / Master of Science