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.
Deep Reinforcement Learning for Robot Batching Optimization and Flow Control
2020-11-01
Hildebrand , M , Andersen , R S & Bøgh , S 2020 , ' Deep Reinforcement Learning for Robot Batching Optimization and Flow Control ' , Procedia Manufacturing , vol. 51 , pp. 1462-1468 . https://doi.org/10.1016/j.promfg.2020.10.203
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
Investigating exploration for deep reinforcement learning of concentric tube robot control
BASE | 2020
|Europäisches Patentamt | 2022
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