Online Learning Techniques for Improving Robot Navigation in Unfamiliar Domains

Many mobile robot applications require robots to act safely and intelligently in complex unfamiliar
environments with little structure and limited or unavailable human supervision. As a
robot is forced to operate in an environment that it was not engineered or trained for, various aspects
of its performance will inevitably degrade. Roboticists equip robots with powerful sensors
and data sources to deal with uncertainty, only to discover that the robots are able to make only
minimal use of this data and still find themselves in trouble. Similarly, roboticists develop and
train their robots in representative areas, only to discover that they encounter new situations that
are not in their experience base. Small problems resulting in mildly sub-optimal performance are
often tolerable, but major failures resulting in vehicle loss or compromised human safety are not.
This thesis presents a series of online algorithms to enable a mobile robot to better deal with
uncertainty in unfamiliar domains in order to improve its navigational abilities, better utilize
available data and resources and reduce risk to the vehicle. We validate these algorithms through
extensive testing onboard large mobile robot systems and argue how such approaches can increase
the reliability and robustness of mobile robots, bringing them closer to the capabilities
required for many real-world applications.