Abstract:
Flow estimation plays an important role in the control and navigation of autonomous
underwater robots. It is challenging for underwater robots because of the complex and
dynamic
fluid environment. Scientists and engineers have been making great efforts in
improving
flow estimation capability of underwater robots over the past years. There are two
main methods to sense the
flow field: (1) using
flow sensors to measure
flow fields directly;
and (2) assimilating other sensor measurements (e.g, pressure) through
flow estimation
algorithms to estimate the
flow field. Since the existing
flow measurement equipment, such
as pitometer log, is hulking, research about using on board sensors to do the
flow estimation
has attracted more and more attention. However, most of these algorithms can only be used
for a specified shape of underwater vehicle.
This thesis presents a novel
flow estimation approach that assimilates distributed pressure
measurements through coalescing recursive Bayesian estimation and
flow model reduction
using proper orthogonal decomposition (POD). The proposed
flow estimation approach
does not rely on any analytical
flow model and is thus applicable to many and various complicated flow fields for arbitrarily shaped underwater robots while most of the existing
flow
estimation methods apply only to well-structured
flow fields with simple robot geometry.
This thesis also analyzes and discusses the
flow estimation design in terms of reduced order
model accuracy, relationship with conventional
flow parameters, and distributed senor
placement. To demonstrate the effectiveness of the proposed distributed
flow estimation approach,
two simulation studies, one with a circular-shaped robot and one with a Joukowskifoil-
shaped robot, are presented. The application of
flow estimation in closed-loop angle-ofattack
regulation is also investigated through simulation.
