Continuous Localization and Navigation of Mobile Robots

摘要

A large mobile robot was used as a platform for research in continuous localization and path planning. Continuous localization is a technique that allows a robot to maintain an accurate estimate of its location by performing regular, small corrections to its odometry. Continuous localization utilizes an evidence grid representation, a common representation scheme that is used by other map-dependent processes, such as path planning. Although techniques exist for building evidence grid maps, most are not adaptive to changes in the environment. In this research, the continuous localization technique is extended by adding a learning component. This allows continuous localization to update the long-term map (evidence grid) with current sensor readings. Results show that the addition of the learning behavior to continuous localization allows the system to adapt to changes in its environment without a loss in its ability to remain localized. Continuous localization with the learning behavior was combined with a wavefront propagation path planner to produce a robust navigation system. This system was tested on a Nomad 200 mobile robot.