Using Platform Models for a Guided Explanatory Diagnosis Generation for Mobile Robots

Plan execution on a mobile robot is inherently error-prone, as the robot needs to act in a physical world which can never be completely controlled by the robot. If an error occurs during execution, the true world state is unknown, as a failure may have unobservable consequences. One approach to deal with such failures is diagnosis, where the true world state is determined by identifying a set of faults based on sensed observations. In this paper, we present a novel approach to explanatory diagnosis, based on the assumption that most failures occur due to some robot hardware failure. We model the robot platform components with state machines and formulate action variants for the robots' actions, modelling different fault modes. We apply diagnosis as planning with a top-k planning approach to determine possible diagnosis candidates and then use active diagnosis to find out which of those candidates is the true diagnosis. Finally, based on the platform model, we recover from the occurred failure such that the robot can continue to operate. We evaluate our approach in a logistics robots scenario by comparing it to having no diagnosis and diagnosis without platform models, showing a significant improvement to both alternatives.