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
Dr. Feitian
Zhang
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.