Abstract
In the context of balanced automation systems, a generic architecture for evolutive supervision of robotized assembly tasks is presented. This architecture provides, at various abstraction levels, functions for dispatching actions, execution monitoring, and diagnosing and recovering from failures. A planning strategy and domain knowledge for nominal plan execution and error recovery is described. Through the use of machine learning techniques, the supervision architecture will be given capabilities to improve its performance over time. The participation of humans in the training and supervision activities is considered essential. The combination of human interactivity with automatic aspects (planning, learning,..) is discussed.
Original language | English |
---|---|
Title of host publication | Balanced Automation Systems |
Publisher | Springer |
Pages | 63-74 |
Number of pages | 12 |
ISBN (Print) | 0-412-72200-3 |
DOIs | |
Publication status | Published - Jan 1995 |
Event | BASYS'95 – IEEE/IFIP Int. Conf. On Balanced Automation Systems - Victoria, Brazil Duration: 23 Jul 1995 → 26 Jul 1995 |
Conference
Conference | BASYS'95 – IEEE/IFIP Int. Conf. On Balanced Automation Systems |
---|---|
Abbreviated title | BASYS'95 |
Country/Territory | Brazil |
City | Victoria |
Period | 23/07/95 → 26/07/95 |
Keywords
- Robotized assembly
- action sequence planning
- monitoring
- failure diagnosis
- machine learning