Autonomous underwater vehicles

This thesis investigates geomagnetic survey methodology in support of the development of a geophysical navigation system for an Autonomous Underwater Vehicle (AUV). Traditional AUV navigation methods are susceptible to cumulative errors and often rely on external infrastructure, limiting their effectiveness in complex underwater environments. This research leverages geomagnetic field anomalies as an additional navigational reference to these traditional systems, particularly in the absence of Global Positioning System (GPS) and acoustics navigation systems. Geomagnetic surveys were conducted over known shipwreck sites off the coast of Fort Lauderdale, Florida, to validate the system's ability to detect and map magnetic anomalies. Data from these surveys were processed to develop high-resolution geomagnetic contour maps, which were then analyzed for accuracy, reliability, and modeling in identifying geomagnetic features.

The capability to navigate in the proximity of solid surfaces while avoiding collision and maintaining high efficiency is essential for the effective design and operation of underwater vehicles. The underlying capability involves a variety of challenges, and a potential approach to overcome such obstacles is to rely on biomimetic or bio-inspired design. Through evolution, organisms have developed methods of locomotion optimized for their specific environment. One of the common forms of locomotion found in underwater organisms is undulatory swimming. These undulatory swimmers display different swimming behaviors based on the flow conditions in their environment. These behaviors take advantage of changes in the flow field caused by the presence of obstructions and obstacles upstream or adjacent to the swimmer. For example, a free swimmer in near-proximity to a flat plane can experience changes in lift and drag during locomotion. The reduced drag can benefit the swimmer, however, changes in lift may lead to a collision with obstacles. Despite the abundance of qualitative data from observing these undulatory swimmers, there is a lack of quantitative data, creating a disconnect in understanding how these organisms have evolved to exploit the presence of walls and obstacles. By employing a combination of traditional computational fluid dynamics and novel neural network-based techniques it is possible to emulate the evolution of learned behavior in biological organisms. The current work uses deep reinforcement learning coupled with two-dimensional numerical simulations of self-propelled swimmers to better understand behavior observed in nature.

Navigation of unmanned underwater vehicles in coastal zones, tight spaces and close to structures such as ports, ship hulls and pipelines remains a difficult challenge. Currently Autonomous Underwater Vehicles (AUVs) use a variety of techniques for motion control, including single thrusters with diving planes or hydrofoils, robotic wrists, or a moving mass. However, these techniques provide limited maneuverability. The objective of this work was to understand the mechanics of elongated fin propulsion for swimming and motion control of underwater vehicles. This bio-inspired propulsion is used by several fishes that swim by undulating a thin and elongated median fin that allow them to perform forward and directional maneuvers. In the first chapter we present the literature review as well as the mathematical formulation using thrust vectoring approach to achieve forward and turning maneuvers. In the second chapter, we used a robotic vessel with elongated fin propulsion to determine the thrust scaling and efficiency. Using precise force and swimming kinematics measurements with the robotic vessel, the thrust generated by the undulating fin was found to scale with the square of the relative velocity between the free streaming flow and the wave speed. In addition, a hydrodynamic efficiency is presented based on propulsive force measurements and a model on the power required to oscillate the fin laterally.

The objective of this thesis is to study the proper placement and denoising of Total Field Magnetometers (TFM) installed on an Autonomous Underwater Vehicle (AUV), in support of a long-term goal to perform geophysical navigation based on total field magnetic sensing. This new form of navigation works by using the magnetic field of the Earth as a source of reference to find the desired heading. The primary tools used in this experiment are a REMUS 100 AUV, a QuSpin scalar magnetometer, and a TwinLeaf vector magnetometer. The Earth’s magnetic field was measured over periods of several hours to determine the range of values it provides under natural conditions. Digital filters were created to digitally reduce fluctuations caused by sources of external interference and sources of internal interference. To mitigate the issue of platform based interference, two methods were examined. These methods involved the use of the Tolles-Lawson model and Wavelet Multiresolution Analysis. The Tolles-Lawson model is used to determine the compensation coefficients from a calibration mission to mitigate the effects from the permanently detected magnetic field, the induced magnetic field, eddy currents. and the geomagnetic field. Wavelet multiresolution analysis follows the same basic steps as Fourier transformations and is used to analyze time series with power sources in motion over a frequency spectrum. Several acquisitions were run with the QuSpin in various locations around and along REMUS, and it was concluded that placing the sensor at the very front of the vessel which is approximately 1.8 [m] from the DC motor, with assistance from wavelet analysis was acceptable for the project.

The primary objective of this research is to investigate the viability of magnetic
anomaly localization with an autonomous underwater vehicle, using a genetic algorithm
(GA). The localization method, first proposed by Sheinker. et al. 2008, is optimized here
for the case of a moving platform. Extensive magnetic field modeling and algorithm
simulation has been conducted and yields promising results. Field testing of the method is
conducted with the use of the Ocean Floor Geophysics Self-Compensating Magnetometer
(SCM). Extensive out-of-water field testing is conducted to validate the ability to
measure a target signal in a uniform NED frame as well as to validate the effectiveness of
the GA. The outcome of the simulation closely matches the results of the conducted field
tests. Additionally, the SCM is fully integrated with FAU’s Remus 100 AUV and
preliminary in-water testing of the system has been conducted.