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Proceedings Paper

A Causal Reasoning Approach For Planning Error Recovery In Automated Manufacturing Systems
Author(s): Richard Kumaradjaja; Frank DiCesare; Geoff Goldbogen
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Paper Abstract

This paper addresses error recovery as a planning problem. A discussion of previous research in planning systems shows that plan generation has been emphasized, with some inclusion of error recovery as part of the plan. This paper extends this work to provide execution monitoring and plan generation for error recovery. A causal reasoning model is presented. During the task plan generation, the model will generate a network of nodes which consists of activities and states, called a causal net of activities and states. The same network will be used to monitor the execution of the task plan. Since the process is modelled based on its cause and effect relationships, the difficulties in detecting a failure and classifying the failure can be reduced. Once the failure has been properly classified, the system will generate the appropriate recovery plan based on the knowledge about the relationships between the state which represents the failure and the recovery task. Using this model, the process of imbedding the recovery task knowledge into the planning system can be done in conjunction with the process of building the task knowledge base. A simple manufacturing application example is presented.

Paper Details

Date Published: 9 February 1989
PDF: 10 pages
Proc. SPIE 1008, Expert Robots for Industrial Use, (9 February 1989); doi: 10.1117/12.949135
Show Author Affiliations
Richard Kumaradjaja, Rensselaer Polytechnic Institute (United States)
Frank DiCesare, Rensselaer Polytechnic Institute (United States)
Geoff Goldbogen, Rensselaer Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 1008:
Expert Robots for Industrial Use
David P. Casasent; Ernest L. Hall; Kenneth J. Stout, Editor(s)

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