Integration of task scheduling, action planning, and control in robotic manufacturing systems

This paper presents a novel approach for solving a challenging problem in the intelligent control of robotic manufacturing systems, i.e., the integration of low-level system sensing and control with high-level system behavior and perception. First, a newly developed event-based planning and control method will be introduced. It will then be extended to a robotic manufacturing system via a hybrid system approach. The tasks of a robotic manufacturing system usually consist of multiple segments of robotic actions, which involve both continuous and discrete types of actions. The max-plus algebra model has been proposed to model such a system. Combined with the event-based planning and control methods, both discrete and continuous actions can be planned and controlled based on the max-plus algebra model. More important, the interactions between discrete and continuous actions can be formulated analytically. A typical parts-sorting task in a robotic manufacturing system is used to illustrate the proposed approach. The experimental results clearly demonstrate the advantages of this method.