Automatic generation of control sequences for manufacturing systems based on partial order planning techniques

Abstract

This work presents an approach for the application of artificial intelligence planning techniques to the automatic generation of control sequences for manufacturing systems. These systems have some special features that must be considered in the planning process, but there are difficulties when the usual models of action are used to deal with these features. In this work, a specialized interval-based model of action is defined by extending the classic model of strips giving it more expressiveness so that it is able to deal with these features. In consequence, a specialized planning algorithm for this model of action, called machine, is defined based on a general partial order planning scheme, and it is able to obtain control sequences for manufacturing systems. These control sequences are actually the control program skeleton and may be easily translated into real control programs expressed as GRAFCET charts.