A systematic strategy of pallet identification and picking based on deep learning techniques
文献类型：期刊论文
| 作者 | Y. Li; G. Ding; C. Li; S. Wang; Q. Zhao and Q. Song |
| 刊名 | Industrial Robot
 |
| 出版日期 | 2023 |
| 卷号 | 50期号:2页码:353-365 |
| ISSN号 | 0143991X |
| DOI | 10.1108/IR-05-2022-0123 |
| 英文摘要 | Purpose: This paper presents a comprehensive pallet-picking approach for forklift robots, comprising a pallet identification and localization algorithm (PILA) to detect and locate the pallet and a vehicle alignment algorithm (VAA) to align the vehicle fork arms with the targeted pallet. Design/methodology/approach: Opposing vision-based methods or point cloud data strategies, we utilize a low-cost RGB-D camera, and thus PILA exploits both RGB and depth data to quickly and precisely recognize and localize the pallet. The developed method guarantees a high identification rate from RGB images and more precise 3D localization information than a depth camera. Additionally, a deep neural network (DNN) method is applied to detect and locate the pallet in the RGB images. Specifically, the point cloud data is correlated with the labeled region of interest (RoI) in the RGB images, and the pallet's front-face plane is extracted from the point cloud. Furthermore, PILA introduces a universal geometrical rule to identify the pallet's center as a "T-shape" without depending on specific pallet types. Finally, VAA is proposed to implement the vehicle approaching and pallet picking operations as a "proof-of-concept" to test PILA’s performance. Findings: Experimentally, the orientation angle and centric location of the two kinds of pallets are investigated without any artificial marking. The results show that the pallet could be located with a three-dimensional localization accuracy of 1 cm and an angle resolution of 0.4 degrees at a distance of 3 m with the vehicle control algorithm. Research limitations/implications: PILA’s performance is limited by the current depth camera’s range ( © 2022, Emerald Publishing Limited. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/67651]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | Y. Li,G. Ding,C. Li,et al. A systematic strategy of pallet identification and picking based on deep learning techniques[J]. Industrial Robot,2023,50(2):353-365. |
| APA | Y. Li,G. Ding,C. Li,S. Wang,&Q. Zhao and Q. Song.(2023).A systematic strategy of pallet identification and picking based on deep learning techniques.Industrial Robot,50(2),353-365. |
| MLA | Y. Li,et al."A systematic strategy of pallet identification and picking based on deep learning techniques".Industrial Robot 50.2(2023):353-365. |
入库方式： OAI收割
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。