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 language | English |
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| Title of host publication | IEEE International Conference on Intelligent Robots and Systems |
| Editors | Anon |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 197-203 |
| Number of pages | 7 |
| Volume | 3 |
| Publication status | Published - 1995 |
| Event | Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Part 3 (of 3) - Pittsburgh, PA, USA Duration: 5 Aug 1995 → 9 Aug 1995 |
Conference
| Conference | Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Part 3 (of 3) |
|---|---|
| City | Pittsburgh, PA, USA |
| Period | 5/08/95 → 9/08/95 |
Keywords
- PROLOG (programming language)
- Robot learning
- Computer system recovery
- Error detection
- Flexible manufacturing systems
- Knowledge representation