Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866915781960269824 |
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| author | Li, Keqin Wang, Jin Wu, Xubo Peng, Xirui Chang, Runmian Deng, Xiaoyu Kang, Yiwen Yang, Yue Ni, Fanghao Hong, Bo |
| author_facet | Li, Keqin Wang, Jin Wu, Xubo Peng, Xirui Chang, Runmian Deng, Xiaoyu Kang, Yiwen Yang, Yue Ni, Fanghao Hong, Bo |
| contents | With the rapid growth of global e-commerce, the demand for automation in the logistics industry is increasing. This study focuses on automated picking systems in warehouses, utilizing deep learning and reinforcement learning technologies to enhance picking efficiency and accuracy while reducing system failure rates. Through empirical analysis, we demonstrate the effectiveness of these technologies in improving robot picking performance and adaptability to complex environments. The results show that the integrated machine learning model significantly outperforms traditional methods, effectively addressing the challenges of peak order processing, reducing operational errors, and improving overall logistics efficiency. Additionally, by analyzing environmental factors, this study further optimizes system design to ensure efficient and stable operation under variable conditions. This research not only provides innovative solutions for logistics automation but also offers a theoretical and empirical foundation for future technological development and application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning Li, Keqin Wang, Jin Wu, Xubo Peng, Xirui Chang, Runmian Deng, Xiaoyu Kang, Yiwen Yang, Yue Ni, Fanghao Hong, Bo Robotics Artificial Intelligence With the rapid growth of global e-commerce, the demand for automation in the logistics industry is increasing. This study focuses on automated picking systems in warehouses, utilizing deep learning and reinforcement learning technologies to enhance picking efficiency and accuracy while reducing system failure rates. Through empirical analysis, we demonstrate the effectiveness of these technologies in improving robot picking performance and adaptability to complex environments. The results show that the integrated machine learning model significantly outperforms traditional methods, effectively addressing the challenges of peak order processing, reducing operational errors, and improving overall logistics efficiency. Additionally, by analyzing environmental factors, this study further optimizes system design to ensure efficient and stable operation under variable conditions. This research not only provides innovative solutions for logistics automation but also offers a theoretical and empirical foundation for future technological development and application. |
| title | Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2408.16633 |