Modeling and design of integrated sensing, planning and control in robotic manufacturing work-cells

Abstract

The analytical and robust integration of low-level system sensing and simple control with high-level system behavior and perception is a challenging problem in the study of intelligent control. This paper presents a novel and generic technique on modeling and design to solve the problem in an intelligent, robotic manufacturingsystem. We propose Max-Plus Algebra model combined with event-based planning and control to form an advanced mechanism to efficiently integrate task scheduling, sensing, planning and real-time execution so that task scheduling, which usually deals with discrete events, as well as action planning, which usually deals with continuous events, can be treated in a unified analytical model, and the design of task synchronization becomes much easier. More importantly, this unified analytic model naturally build up continuous interaction between discrete and continuous events. This characteristic of the model allows the designed automated system to intelligently cope with unexpected events and uncertainties during well-scheduled tasks and improves the robustness and reliability of the manufacturing system. A robotic manufacturing system working on a part-sorting task is used to illustrate the proposed approach. The experimental results successfully demonstrate the advantages of the proposed approach.