Inductive generation of diagnostic knowledge for autonomous assembly

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

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

A generic architecture for evolutive supervision of robotized assembly tasks is presented. This architecture provides, at different levels of abstraction, functions for dispatching actions, monitoring their execution, and diagnosing and recovering from failures. Modeling execution failures through taxonomies and causal networks plays a central role in diagnosis and recovery. 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. The applied methodologies, performed experiments and obtained results are described in detail.

Original languageEnglish
Pages2545-2552
Number of pages8
Publication statusPublished - 1995
EventIEEE International Conference on Robotics and Automation - Nagoya, Japan
Duration: 21 May 1995 → …

Conference

ConferenceIEEE International Conference on Robotics and Automation
CountryJapan
CityNagoya
Period21/05/95 → …

Keywords

  • Robot learning
  • SCADA systems
  • Computer simulation
  • Concurrent engineering
  • Failure analysis
  • Flexible manufacturing systems
  • Hierarchical systems
  • Knowledge engineering
  • Monitoring
  • Performance
  • Planning

Fingerprint Dive into the research topics of 'Inductive generation of diagnostic knowledge for autonomous assembly'. Together they form a unique fingerprint.

  • Cite this

    Lopes, L. S., & Camarinha-Matos, L. M. (1995). Inductive generation of diagnostic knowledge for autonomous assembly. 2545-2552. Paper presented at IEEE International Conference on Robotics and Automation, Nagoya, Japan.