Project
title: Optimal Sampling Strategy for
Advection-Diffusion Parameter Estimation using Vehicles with
Nonholomonic Dynamics
Andrew
White
Abstract
There exist many
physical transport phenomenon that are governed by
advection-diffusion processes. Some of these processes occur
naturally due to local climate conditions, such as the growth and
spread of harmful algae blooms. Others occur without natural cause,
such as the release of toxic chemicals. Due to the harmful nature of
some of these processes, researchers have been developing methods to
track the source of such advection-diffusion processes. In this
paper, we present an optimal sensing strategy for a group of mobile
sensing agents to estimate the parameters of an advection-diffusion
process with an impulse chemical source. Our approach to solving this
problem is to use a nonlinear least-square algorithm on the
advection-diffusion model over a spatially averaged velocity field.
The advection-diffusion process parameters will be averaged over a
control volume. The control volume will contain the sensing agents.
To control the position and spacing between each of the sensing
agents a consensus algorithm will be used. The concept will be tested
using two-wheeled vehicles with nonholonomic dynamics. Nonlinear
control synthesis techniques will be used to stabilize the
nonholonomic dynamics of the two-wheeled vehicles. Comparisons of
performance between the methods used will be made.