Reducing failure rates of robotic systems though inferred invariants monitoring

Hengle Jiang, Sebastian Elbaum, Carrick Detweiler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

System monitoring can help to detect abnormalities and avoid failures. Crafting monitors for today's robotic systems, however, can be very difficult due to the systems' inherent complexity. In this work we address this challenge through an approach that automatically infers system invariants and synthesizes those invariants into monitors. The approach is novel in that it derives invariants by observing the messages passed between system nodes and the invariants types are tailored to match the spatial, temporal, and operational attributes of robotic systems. Further, the generated monitor can be seamlessly integrated into systems built on top of publish-subscribe architectures. An application of the technique on a system consisting of a unmanned aerial vehicle (UAV) landing on a moving platform shows that it can significantly reduce the number of crashes in unexpected landing scenarios.

Original languageEnglish (US)
Title of host publicationIROS 2013
Subtitle of host publicationNew Horizon, Conference Digest - 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Pages1899-1906
Number of pages8
DOIs
StatePublished - 2013
Event2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013 - Tokyo, Japan
Duration: Nov 3 2013Nov 8 2013

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013
Country/TerritoryJapan
CityTokyo
Period11/3/1311/8/13

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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