Research in mobile robotics, unmanned systems, and autonomous man-portable vehicles has grown rapidly over the last decade. This push has taken the problems of robot cognition and behavioral control out of the lab and into the field. Two good examples of this are the DARPA Urban Challenge autonomous vehicle race and the RoboCup robot soccer competition. In these challenges, a mobile robot must be capable of completing complex, sophisticated tasks in a dynamic, partially observable and unpredictable environment. Such conditions necessitate a behavioral programming approach capable of performing high-level action selection in the presence of multiple goals of dynamically changing importance, and noisy, incomplete perception data.

In this thesis, an approach to behavioral programming is presented that provides the designer with an intuitive method for building contextual intelligence while preserving the qualities of emergent behavior present in traditional behavior-based programming. This is done by using a modified hierarchical state machine for behavior arbitration in sequence with a command fusion mechanism for cooperative and competitive control. The presented approach is analyzed with respect to portability across platforms, missions, and functional requirements. Specifically, two landmark case-studies, the DARPA Urban Challenge and the International RoboCup Competition are examined.