Machine learning approach to error detection and recovery in assembly

L. Seabra Lopes, L. M. Camarinha-Matos

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

14 Citations (Scopus)

Abstract

Research results concerning error detection and recovery in robotized assembly systems, key components of flexible manufacturing systems, are presented. A planning strategy and domain knowledge for nominal plan execution and for error recovery is described. A supervision architecture provides, at different levels of abstraction, functions for dispatching actions, monitoring their execution, and diagnosing and recovering from failures. Through the use of machine learning techniques, the supervision architecture will be given capabilities for improving its performance over time. Particular attention is given to the inductive generation of structured classification knowledge for diagnosis.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
Editors Anon
PublisherIEEE
Pages197-203
Number of pages7
Volume3
Publication statusPublished - 1995
EventProceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Part 3 (of 3) - Pittsburgh, PA, USA
Duration: 5 Aug 19959 Aug 1995

Conference

ConferenceProceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Part 3 (of 3)
CityPittsburgh, PA, USA
Period5/08/959/08/95

Keywords

  • PROLOG (programming language)
  • Robot learning
  • Computer system recovery
  • Error detection
  • Flexible manufacturing systems
  • Knowledge representation

Fingerprint Dive into the research topics of 'Machine learning approach to error detection and recovery in assembly'. Together they form a unique fingerprint.

  • Cite this

    Lopes, L. S., & Camarinha-Matos, L. M. (1995). Machine learning approach to error detection and recovery in assembly. In Anon (Ed.), IEEE International Conference on Intelligent Robots and Systems (Vol. 3, pp. 197-203). IEEE.