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http://hdl.handle.net/10119/19405
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| Title: | An Efficient Deep Reinforcement Learning Model for Online 3D Bin Packing Combining Object Rearrangement and Stable Placement |
| Authors: | Zhou, Peiwen
Gao, Ziyan
Li, Chenghao
Chong, Nak Young |
| Keywords: | 3D Bin Packing
Object Rearrangement
Placement Stability
Deep Reinforcement Learning |
| Issue Date: | 2024-10-29 |
| Publisher: | Institute of Control, Robotics and Systems (ICROS) |
| Magazine name: | 2024 24th International Conference on Control, Automation and Systems (ICCAS) |
| Start page: | 964 |
| End page: | 969 |
| DOI: | 10.23919/ICCAS63016.2024.10773090 |
| Abstract: | 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 |
| Material Type: | author |
| Appears in Collections: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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| N-CHONG-I-1210-2.pdf | | 2596Kb | Adobe PDF | View/Open |
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