Execution monitoring in assembly with learning capabilities

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

A generic architecture for execution supervision of Robotic 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. A discussion on the process of acquisition of such monitoring knowledge is made. Through the use of machine learning techniques, the supervision architecture will be given capabilities for improving its performance over time. Preliminary results of applying machine learning in this area are presented and planned extensions discussed.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
PublisherPubl by IEEE
Pages272-279
Number of pages8
Editionpt 1
ISBN (Print)0818653329
Publication statusPublished - 1994
EventProceedings of the 1994 IEEE International Conference on Robotics and Automation - San Diego, CA, USA
Duration: 8 May 199413 May 1994

Conference

ConferenceProceedings of the 1994 IEEE International Conference on Robotics and Automation
CitySan Diego, CA, USA
Period8/05/9413/05/94

Keywords

  • Robot learning
  • Systems analysis
  • Data acquisition
  • Failure (mechanical)
  • Failure analysis
  • Industrial robots
  • Motion control

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