Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

SAGETRACK

Sign In to gain access to subscriptions and/or personal tools.
The International Journal of Robotics Research
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via ISI Web of Science (2)
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Murphy, R. R.
Right arrow Articles by Hershberger, D.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

Handling Sensing Failures in Autonomous Mobile Robots

Robin R. Murphy

Computer Science and Engineering, University of South Florida, Tampa, FL 33620-5399, USAmurphy{at}csee.usf.edu

Dave Hershberger

The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USAhersh{at}ri.cmu.edu

This article details the SFX-EH architecture for handling sensing failures in autonomous mobile robots. The SFX-EH uses novel extensions to the generate-and-test method to classify failures with only a partial causal model of the sensor/environment/task interactions for the robot. The generate-and-test methodology exploits the ability of the robot as a physically situated agent to actively test assumptions about the state of sensors, condition of the environment, and validity of task constraints. The SFX-EH uses the type of failure to determine the appropriate recovery strategy: reconfiguration of the logical sensor or logical behavior, recalibration of the sensor or actuator, and corrective actions. The system bypasses classification if all hypotheses lead to the same recovery strategy. Results of the SFX-EH running on two robots with different sensor suites and tasks are presented, demonstrating intelligent failure recovery within a modular, portable implementation.

The International Journal of Robotics Research, Vol. 18, No. 4, 382-400 (1999)
DOI: 10.1177/02783649922066286
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?