Intelligent process model for robotic part assembly in a partially unstructured environment

Abstract

A process model for part assembly, using robotic manipulators, is introduced. Part-bringing, in an environment that contains obstacles, is accomplished by combining a neural network control strategy, co-ordinating with a fuzzy optimal process model to bring a part from an initial position to a destination (target) for the purpose of part insertion. Fuzzy set theory, well suited to the management of uncertainty, is introduced to address the uncertainty problem associated with the part-bringing procedure. The degree of uncertainty associated with the part-bringing is used as an optimality criterion, or cost function, e.g. minimum fuzzy entropy, for a specific task execution. The proposed technique is applicable not only to a wide range of robotic tasks including pick and place operations, but also to the control of unmanned aircraft or missiles.