A machine learning approach to error detection and recovery in assembly

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.