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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/19405
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| タイトル: | An Efficient Deep Reinforcement Learning Model for Online 3D Bin Packing Combining Object Rearrangement and Stable Placement |
| 著者: | Zhou, Peiwen
Gao, Ziyan
Li, Chenghao
Chong, Nak Young |
| キーワード: | 3D Bin Packing
Object Rearrangement
Placement Stability
Deep Reinforcement Learning |
| 発行日: | 2024-10-29 |
| 出版者: | Institute of Control, Robotics and Systems (ICROS) |
| 誌名: | 2024 24th International Conference on Control, Automation and Systems (ICCAS) |
| 開始ページ: | 964 |
| 終了ページ: | 969 |
| DOI: | 10.23919/ICCAS63016.2024.10773090 |
| 抄録: | This paper presents an efficient deep reinforcement learning (DRL) framework for online 3D bin packing (3DBPP). The 3D-BPP is an NP-hard problem significant in logistics, warehousing, and transportation, involving the optimal arrangement of objects inside a bin. Traditional heuristic algorithms often fail to address dynamic and physical constraints in real-time scenarios. We introduce a novel DRL framework that integrates a reliable physics heuristic algorithm and object rearrangement and stable placement. Our experiment show that the proposed framework achieves higher space utilization rates effectively minimizing the amount of wasted space with fewer training epochs. |
| Rights: | This is the author's version of the work. Copyright (C) ICROS. 2024 24th International Conference on Control, Automation and Systems (ICCAS 2024), 2024, pp. 964-969. DOI: 10.23919/ICCAS63016.2024.10773090. Personal use of this material is permitted. This material is posted here with permission of Institute of Control, Robotics and Systems (ICROS). |
| URI: | http://hdl.handle.net/10119/19405 |
| 資料タイプ: | author |
| 出現コレクション: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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このアイテムのファイル:
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| N-CHONG-I-1210-2.pdf | | 2596Kb | Adobe PDF | 見る/開く |
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