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 language | English |
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| Pages | 2545-2552 |
| Number of pages | 8 |
| Publication status | Published - 1995 |
| Event | IEEE International Conference on Robotics and Automation - Nagoya, Japan Duration: 21 May 1995 → … |
Conference
| Conference | IEEE International Conference on Robotics and Automation |
|---|---|
| Country/Territory | Japan |
| City | Nagoya |
| Period | 21/05/95 → … |
Keywords
- Robot learning
- SCADA systems
- Computer simulation
- Concurrent engineering
- Failure analysis
- Flexible manufacturing systems
- Hierarchical systems
- Knowledge engineering
- Monitoring
- Performance
- Planning