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.
This work has been funded in part by the European Community (Esprit projects B-Learn and FlexSys) and JNICT (project CIM-CASE and a Ph.D. scholarship).
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© 1995 Springer Science+Business Media Dordrecht
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Lopes, L.S., Camarinha-Matos, L.M. (1995). Planning, Training and Learning in Supervision of Flexible Assembly Systems. In: Camarinha-Matos, L., Afsarmanesh, H. (eds) Balanced Automation Systems. BASYS 1995. IFIP — The International Federation for Information Processing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-34910-7_7
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DOI: https://doi.org/10.1007/978-0-387-34910-7_7
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