期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2024-08-20 |
| 作品名稱 | Integrating Heuristic Methods with Deep Reinforcement Learning for Online 3D Bin Packing Optimization |
| 作品名稱（其他語言） | |
| 著者 | Ching-Chang Wong; Tai-Ting Tsai; Can-Kun Ou |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Sensors 24(16), 5370 |
| 摘要 | This study proposes a method named Hybrid Heuristic Proximal Policy Optimization (HHPPO) to implement online 3D bin-packing tasks. Some heuristic algorithms for bin-packing and the Proximal Policy Optimization (PPO) algorithm of deep reinforcement learning are integrated to implement this method. In the heuristic algorithms for bin-packing, an extreme point priority sorting method is proposed to sort the generated extreme points according to their waste spaces to improve space utilization. In addition, a 3D grid representation of the space status of the container is used, and some partial support constraints are proposed to increase the possibilities for stacking objects and enhance overall space utilization. In the PPO algorithm, some heuristic algorithms are integrated, and the reward function and the action space of the policy network are designed so that the proposed method can effectively complete the online 3D bin-packing task. Some experimental results illustrate that the proposed method has good results in achieving online 3D bin-packing tasks in some simulation environments. In addition, an environment with image vision is constructed to show that the proposed method indeed enables an actual robot manipulator to successfully and effectively complete the bin-packing task in a real environment. |
| 關鍵字 | 3D bin-packing; deep reinforcement learning; proximal policy optimization; heuristic algorithms |
| 語言 | en_US |
| ISSN | 1424-8220 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | CHE |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127575 ) |