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A MULTI-AGENT REINFORCEMENT LEARNING FRAMEWORK FOR INTELLIGENT MANUFACTURING WITH AUTONOMOUS MOBILE ROBOTS

Published online by Cambridge University Press:  27 July 2021

Akash Agrawal ,
Sung Jun Won ,
Tushar Sharma ,
Mayuri Deshpande  and
Christopher McComb
Akash Agrawal
Affiliation:
The Pennsylvania State University;
Sung Jun Won
Affiliation:
The Pennsylvania State University;
Tushar Sharma
Affiliation:
Siemens Technology
Mayuri Deshpande
Affiliation:
Siemens Technology
Christopher McComb*
Affiliation:
The Pennsylvania State University;
*
McComb, Christopher Carson, The Pennsylvania State University, School of Engineering Design, Technology, and Professional Programs, United States of America, [email protected]

Abstract

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Intelligent manufacturing (IM) embraces Industry 4.0 design principles to advance autonomy and increase manufacturing efficiency. However, many IM systems are created ad hoc, which limits the potential for generalizable design principles and operational guidelines. This work offers a standardizing framework for integrated job scheduling and navigation control in an autonomous mobile robot driven shop floor, an increasingly common IM paradigm. We specifically propose a multi-agent framework involving mobile robots, machines, humans. Like any cyberphysical system, the performance of IM systems is influenced by the construction of the underlying software platforms and the choice of the constituent algorithms. In this work, we demonstrate the use of reinforcement learning on a sub-system of the proposed framework and test its effectiveness in a dynamic scenario. The case study demonstrates collaboration amongst robots to maximize throughput and safety on the shop floor. Moreover, we observe nuanced behavior, including the ability to autonomously compensate for processing delays, and machine and robot failures in real time.

Keywords

Industry 4.0Machine learningArtificial intelligenceIntelligent Manufacturing Systems
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 161 - 170
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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