(Georgia Institute of Technology, 2007-04-02)
Bakolas, Efstathios
The main objective of this thesis is to present a new path
planning scheme for solving the shortest (collision-free) path
problem for an agent (vehicle) operating in a partially known
environment. We present two novel algorithms to solve the planning
problem. For both of these approaches we assume that the agent has
detailed knowledge of the environment and the obstacles only in
the vicinity of its current position. Far away obstacles or the
final destination are only partially known and may even change
dynamically at each instant of time. The path planning scheme is
based on information gathered on-line by the available on-board
sensor devices. The solution minimizes the total length of the
path with respect to a metric that includes actual path length,
along with a risk-induced metric. In order to obtain an
approximation of the whole configuration space at different levels
of fidelity we use a wavelet approximation scheme. In the first
proposed algorithm, the path-planning problem is solved using a
multi-resolution cell decomposition of the environment obtained
from the wavelet transform. In the second algorithm, we extend the
results of the the first one by using the multiresolution
representation of the environment in conjunction with a conformal
mapping to polar coordinates. By performing the cell decomposition
in polar coordinates, we can naturally incorporate sector-like
cells that are adapted to the data representation collected by the
on-board sensor devices.