Michael Beetz and Henrik Grosskreutz
Temporal projection, the process of predicting what will happen when a robot executes its plan, is essential for autonomous service robots to successfully plan their missions. This paper describes a causal model of the behavior exhibited by the mobile robot RHINO when running concurrent reactive plans for performing office delivery jobs. The model represents aspects of robot behavior that cannot be represented by most action models used in AI planning: it represents the temporal structure of continuous control processes, several modes of their interferences, and various kinds of uncertainty. This enhanced expressiveness enables XFRM, a robot planning system, to predict, and therefore forestall, various kinds of behavior flaws including missed deadlines whilst exploiting incidental opportunities. The proposed causal model is experimentally validated using the robot and its simulator.