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Graduate Student Practice Presentations for MTS/IEEE OCEANS Halifax
Dept. of Ocean Engineering
University of New Hampshire
Using Ambient Sensor Data to Characterize and Predict Autonomous Perception Sensor Performance
Hannah Arnholt
Ph.D. Student
With the development of low-cost, small Uncrewed Underwater Vehicles (UUVs), the use of cost-efficient sensor systems imposes its own constraints (e.g., sensor accuracy and precision). This presentation will discuss a methodology, the Standardized Heuristic Algorithm for Reinforced Calculations (SHARC), to predict the accuracy and precision of specific perception sensor measurements in practical field implementation by leveraging the redundancy of several common on-board sensors, to help work around the constraints of these smaller, low-cost systems. To test the SHARC algorithm, the study presented focuses on modeling a Mechanically Scanning Imaging Sonar (MSIS) in the BELLHOP simulation program and uses historic Sound Velocity Profiles (SVPs) to identify how the MSIS is affected by various ambient surroundings. Results show that the SVP shape affects the MSIS range of the probability of detection. It is observed that a change in SVP slope correlates to a reduced MSIS performance range as opposed to that of a more constant SVP depth profile, which increases the MSIS performance range. This presentation will also show some follow-on experimental research that was performed this past summer off the Puget Sound in Washington State.
Mathematical Model of Subcarangiform Robotic Fish
Margaret Enderle
M.S. Student
Abstract
Mathematical modeling of robotic fish creates a simulation environment for the manipulation of design and input parameters without the necessity of manipulating the physical model. Combining two mathematical models, one focusing on pectoral fins and the other concentrated on biomimetic thrust, the authors aim to create a mathematical model to simulate the University of New Hampshire’s Ghost Uncrewed Performance Platform Submersible (GUPPS). This model investigates various pectoral fin inputs and their effect on pitch angle, determining maximum operating parameters to maintain biomimicry, and explores system response to high-frequency fin inputs. In addition to the theoretical work, physical research done on GUPPS such as fin development and implementation will also be presented.
Hannah Arnholt is a Ph.D. student at the University of New Hampshire in Ocean Engineering with a focus on bio-inspired underwater perception for Uncrewed Underwater Vehicles (UUVs). Hannah received her Bachelor of Science in Mechanical Engineering in 2017 from the University of Miami, working on combustion engine intake efficiency for her senior capstone research. After completing her undergraduate degree, Hannah worked as a Software Requirements Systems Engineer for Raytheon Technologies in Massachusetts, before deciding to return to school for her PhD. Aside from her PhD. research, Hannah has also been a graduate advisor for the Marine and Naval Technological Advancements for Robotic Autonomy (MANTA RAY) group since 2020, which consists of not only aiding with developing different marine robotics platforms but mentoring the different students that help with the project. Hannah's Ph.D. degree is currently being funded by the DoD SMART Scholar program, and she will be working at Naval Undersea Warfare Center (NUWC) Keyport, WA upon completion of her degree.
Maggie Enderle is a master’s student at the University of New Hampshire in Ocean Engineering. After completing her bachelor’s degree at UNH with a senior capstone project developing propulsion of robotic fish, she has continued to build on that research in her graduate program. Her current work focuses on pitch control of robotic fish using pectoral fins, and she will be presenting this research at the OCEANS Halifax conference next week.
