Execution monitoring in assembly with learning capabilities
- 17 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
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.Keywords
This publication has 2 references indexed in Scilit:
- Learning error-recovery strategies in telerobotic systemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A failure recovery scheme for assembly workcellsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002