Towards intelligent execution supervision for flexible assembly systems

Research results concerning error detection and recovery in robotized assembly systems, key components of flexible manufacturing systems, are presented. The approach to the integration of services and the modelling of tasks, resources and enviroment is described. A planning strategy and domain knowledge for nominal plan execution and for error recovery is presented. 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.