Online Learning Techniques for Improving Robot Navigation in Unfamiliar Domains

Abstract

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.