A framework to integrate mobile manipulators as cyber–physical systems into existing production systems in the context of industry 4.0

Abstract

With the emergence of the Cyber–Physical Systems (CPSs) along with the fourth industrial revolution, the discussion of distributed architecture and coordination and extensive communication between all components of a system was raised. On the other hand, Modern industrial manufacturing relies heavily on flexible production. Autonomous mobile manipulators (MMs) have the potential to improve the flexibility of existing manufacturing environments. With the advantages associated with this technology, there is an upsurge in the demand for MMs. Thus, adding a new MM as a new CPS into a system following industry 4.0 requires a framework to help taking all the advantages from it. In this article, we proposed a framework to integrating MMs as CPSs into an existing production system. This integration requires comprehensive knowledge of the existing production process and control, which is a view to improve the performance of the existing production systems via new functionalities due to MMs. The next step of the framework is to make new MM operations compatible with the existing production control system. For this purpose, we define different parametric blocks to perform new operations and thus contribute to making the production line more autonomous. Considering all of these issues simultaneously is necessary for an efficient integration in smart production context. For a successful integration, we presented four principal dimensions of integration: Physical integration, communicational integration, functional and operational integration, and digital integration. Furthermore, the framework has been applied to a research production line called Platform 4.0 at Arts et Métiers. An MM from OMRON Company (called MoMa) has been integrated into the existing production system. Results from this real-world demonstration show that MoMa is capable of successfully increasing flexibility, autonomy, and efficiency of a production system using command signals from Manufacturing Execution System (MES).