Fault detection and fault tolerance methods for robotics

Abstract

Fault tolerance is increasingly important in modern autonomous or industrial robots.The ability to detect and tolerate failures allows robots to e ectively cope with internal failures and continue performing designated tasks without the need for immediate human intervention.To support these fault tolerant capabilities, methods of detecting and isolating failures must be perfected.This thesis presents new fault detection algorithms which detect failures in robot components using analytical redundancy relations.The robot components critical to fault detection are revealed using an extended fault tree analysis.The thesis validates the algorithms using a simulated robot failure testbed.An intelligent fault tolerance framework is proposed in which a fault tree database and the detection algorithms work together to detect and tolerate sensor or motor failures in a robot system.Future work will expand the detection and tolerance routines and embedthe framework into a more exible expert system package.I w ould like to express my appreciation to Dr. Joseph Cavallaro and Dr. Ian Walker for their support and encouragement throughout the writing of this thesis.Their eccentric view of the world gave birth to this thesis.I am grateful for all the lively discussions which brought about many of the answers to questions within the research.Dr. Cheatham's willingness to be on my committee and patience throughout the revision process are also appreciated.I hope they are all ready for two more years!My deepest love and gratitude goes to my mother, father, and sister who did not complain of the frequent and frantic calls home.Their constant interest in my work and concern for my problems