(Georgia Institute of Technology, 2013)
Szwaykowska, Klementyna; Zhang, Fumin
In this paper we model the controlled Lagrangian
particle tracking (CLPT) error for marine vehicles moving in an
ocean flow field, with guidance from ocean models. We linearize
the error about the nominal modeled trajectory of the system
and derive an exact expression for the linearized error in the
case of constant modeled ocean flow. We show that this simple
error model can be used to estimate error in predicted positions
of autonomous vehicles, using data from a field deployment of
autonomous underwater gliders in Long Bay, SC, in winter
2012.
(Georgia Institute of Technology, 2012-12-18)
Szwaykowska, Klementyna; Zhang, Fumin
This paper establishes the method of controlled Lagrangian particle tracking (CLPT) to analyse the
offsets between physical positions of marine robots in the ocean and simulated positions of controlled
particles in an ocean model. The offset, which we term the CLPT error, demonstrates distinguished
characteristics not previously seen in drifters and floats that cannot be actively controlled. The CLPT
error growth over time is exponential until it reaches a turning point that only depends on the resolution
of the ocean model. After this turning point, the error growth slows down significantly to polynomial
functions of time. In the ideal case, a theoretical upper threshold on exponential growth of CLPT
error can be derived. These characteristics are proved theoretically, verified via simulation, and justified
with ocean experimental data. The method of CLPT may be applied to improve the accuracy of ocean
circulation models and the performance of navigation algorithms for marine robots.
(Georgia Institute of Technology, 2010-12)
Szwaykowska, Klementyna; Zhang, Fumin
Autonomous underwater vehicles are flexible mobile
platforms for ocean sampling and surveillance missions.
However, navigation of these vehicles in unstructured, highly
variable ocean environments poses a significant challenge.
Model-based prediction of vehicle position may be used to
improve navigation capability, but prediction error exists due
to limited resolution and accuracy of flow values obtained
from ocean models that calculate flow velocity at discrete grid
points. We present a theoretical lower bound on the steady-state
error in position prediction for underwater vehicles using
ocean model flow data and show that it is determined by the
gridsize used by the ocean models. Our conclusions are justified
by simulation and data collected during an ocean experiment.