A multi-agent reinforcement learning framework for intelligent manufacturing with autonomous mobile robots

Akash Agrawal, Sung Jun Won, Tushar Sharma, Mayuri Deshpande, Christopher McComb

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)161-170
Number of pages10
JournalProceedings of the Design Society
StatePublished - 2021
Event23rd International Conference on Engineering Design, ICED 2021 - Gothenburg, Sweden
Duration: Aug 16 2021Aug 20 2021

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Software
  • Modeling and Simulation

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