A neural/fuzzy optimal process model for robotic part assembly

A process model for part assembly task, using robotic manipulators, is introduced. A neural network control strategy, based on measured force and moment data, for avoiding jamming during part insertion is presented. Fuzzy set theory, well-suited to the management of uncertainty, is introduced to address the uncertainty problem associated with the part insertion procedure. The degree of uncertainty associated with the part insertion is used as an optimality criterion for a specific task execution. The proposed technique is applicable to a wide range of robotic tasks including part mating with various shaped parts.