Expert system framework for fault detection and fault tolerance in robotics

Abstract

Fault tolerance is of increasing importance for modern robots. The ability to detect and tolerate failures enables robots to effectively cope with internal failures and continue performing assigned tasks without the need for immediate human intervention. To monitor fault tolerance actions performed by lower level routines and to provide higher level information about a robot;s recovery capabilities, we present an expert system and critic which together form a novel and intelligent fault tolerance framework integrating fault detection and tolerance routines with dynamic fault tree analysis. A higher level, operating system inspired critic layer provides a buffer between robot fault tolerant operations and the user. The expert system gives the framework the modularity and flexibility to quickly convert between a variety of robot structures and tasks. It also provides a standard interface to the fault detection and tolerance software and a more intelligent means of monitoring the progress of failure and recovery throughout the robot system. The expert system further allows for prioritization of tasks so that recovery can take precedence over less pressing goals. Fault trees are used as a standard database to reveal the components essential to fault detection and tolerance within a system and detail the interconnection between failures in the system. The trees are also used quantitatively to provide a dynamic estimate of the probability of failure of the entire system or various subsystems.