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

Abstract

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.