Abstract
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.
Original language | English |
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Journal | Procedia Manufacturing |
Volume | 51 |
Pages (from-to) | 1462-1468 |
Number of pages | 7 |
ISSN | 2351-9789 |
DOIs | |
Publication status | Published - Nov 2020 |
Event | 30th International Conference on Flexible Automation and Intelligent Manufacturing - Athens, Greece Duration: 15 Jun 2021 → 18 Jun 2021 https://www.faimconference.org/ |
Conference
Conference | 30th International Conference on Flexible Automation and Intelligent Manufacturing |
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Country/Territory | Greece |
City | Athens |
Period | 15/06/2021 → 18/06/2021 |
Internet address |
Keywords
- Reinforcement Learning
- Deep Reinforcement Learning
- Artificial Intelligence
- Robotics
- Smart Manufacturing
- Proximal Policy Optimization
- Deep Learning