Reactive Planning in a Motivated Behavioral Architecture

To operate in natural environmental settings, autonomous mobile robots need more than just the ability to navigate in the world, react to perceived situations or follow pre-determined strategies: they must be able to plan and to adapt those plans according to the robot’s capabilities and the situations encountered. Navigation, simultaneous localization and mapping, perception, motivations, planning, etc., are capabilities that contribute to the decision-making processes of an autonomous robot. How can they be integrated while preserving their underlying principles, and not make the planner or other capabilities a central element on which everything else relies on? In this paper, we address this question with an architectural methodology that uses a planner along with other independent motivational sources to influence the selection of behaviorproducing modules. Influences of the planner over other motivational sources are demonstrated in the context of the AAAI Challenge.