ECE PhD Seminar Fengying Dang “Environmental Perception of Autonomous Underwater Vehicles” March 4th, 2021 2:00 PM – 3:30 PM Zoom Webinar Link: https://gmu.zoom.us/j/93823025873 Advisor: Dr. Feitian Zhang Abstract: Autonomous underwater vehicles (AUVs) have been receiving more and more scientific attentiondue to their independent locomotion and long-range operation. Over the past decades, a variety of advanced AUVs have been developed for various applications, such as pipeline fault detection in oil and gas industries, water quality monitoring in aquaculture, and environmental surveillance in maritime safetyand security. Althoughnumerous advances have been achieved in the science and engineering of AUVs, there still exist several roadblocks toward the future of marine autonomy. Particularly, the high attenuation of electromagnetic waves in water and the virtually unknown and dynamic flow fields make environmental perception of AUVs a very challenging problem. A bio-inspired approachfor underwater environmental perception has been increasingly studiedover the past decade, particularly in sensing background flows. Inspired from biological fish's lateral line, scientists and engineers have been designing and developing numerous artificial lateral line systems, aiming to empower AUVs to sense their background flows. Most of the recently developed bio-inspired flow sensing systems use distributed sensors (e.g., pressure sensors) to sample the flow field and then apply estimation algorithms to estimate the whole flow velocity field around AUVs of interest. This talk concentrates on two parts. First, a dynamicmode decomposition (DMD)-based flow sensing methodinspired by fish'slateral line system will be presented. The integration of a DMD-based flow model and a Bayesian filter aims to developa data-driven flow sensing methodwhich is potentially applicable to many and various complex dynamic flow fields for AUVs of arbitrary shapes. Second, aiming to address the flow sensingproblem in flow pattern changingenvironments, a novel fast Fouriertransform (FFT)-assisted background flow sensing algorithmis proposed. Both simulation and experimental results of flow sensing using three testing AUV prototypes will be discussed for validation of the proposed method.