Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2009Self-localization of an underwater vehicle is particularly challenging due to the absence
of Global Positioning System (GPS) reception or features at known positions
that could otherwise have been used for position computation. Thus Autonomous
Underwater Vehicle (AUV) applications typically require the pre-deployment of a set
of beacons.
This thesis examines the scenario in which the members of a group of AUVs
exchange navigation information with one another so as to improve their individual
position estimates.
We describe how the underwater environment poses unique challenges to vehicle
navigation not encountered in other environments in which robots operate and how
cooperation can improve the performance of self-localization. As intra-vehicle communication
is crucial to cooperation, we also address the constraints of the communication
channel and the effect that these constraints have on the design of cooperation
strategies.
The classical approaches to underwater self-localization of a single vehicle, as
well as more recently developed techniques are presented. We then examine how
methods used for cooperating land-vehicles can be transferred to the underwater
domain. An algorithm for distributed self-localization, which is designed to take the
specific characteristics of the environment into account, is proposed.
We also address how correlated position estimates of cooperating vehicles can lead
to overconfidence in individual position estimates.
Finally, key to any successful cooperative navigation strategy is the incorporation
of the relative positioning between vehicles. The performance of localization
algorithms with different geometries is analyzed and a distributed algorithm for the
dynamic positioning of vehicles, which serve as dedicated navigation beacons for a
fleet of AUVs, is proposed.This work was funded by Office of Naval Research grants N00014-97-1-0202,
N00014-05-1-0255, N00014-02-C-0210, N00014-07-1-1102 and the ASAP MURI
program led by Naomi Leonard of Princeton University