Deep Reinforcement Learning for Robot Batching Optimization and Flow Control

Robot batching is an optimization problem found in many industrial applications. Current state-of-the-art approaches utilize a combination of heuristic based parameters and statistical analysis. This approach necessitates many tunable parameters, which again provides challenges when delivering syste...

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Bibliographic Details
Published in:Procedia manufacturing Vol. 51; pp. 1462 - 1468
Main Authors: Hildebrand, Max, Andersen, Rasmus S., Bøgh, Simon
Format: Journal Article
Language:English
Published: Elsevier B.V 2020
Subjects:
Artificial Intelligence in Smart Manufacturing
Deep Reinforcement Learning
Proximal Policy Optimization
Robot Batching
Deep Reinforcement Learning
Robot Batching
Artificial Intelligence in Smart Manufacturing
Proximal Policy Optimization
ISSN:2351-9789, 2351-9789
Online Access:Get full text
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Summary:Robot batching is an optimization problem found in many industrial applications. Current state-of-the-art approaches utilize a combination of heuristic based parameters and statistical analysis. This approach necessitates many tunable parameters, which again provides challenges when delivering systems to new customers. We challenge current state-of-the-art in statistical approaches by presenting a novel application of a policy gradient method for a Deep Reinforcement Learning (DRL/RL) agent. We have developed a Unity simulation framework of an existing robot-batching cell, on which a RL agent is able to successfully train and obtain a policy for performing robot batching, using a tabula rasa approach. The trained agent is capable of packaging 47.86% of 1218 total batches within the prescribed tolerances, with a positive give-away of 8.76%. The application of DRL in performing robot batching is to the authors knowledge the first of its kind.
ISSN:2351-9789
2351-9789
DOI:10.1016/j.promfg.2020.10.203