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.

翻译：随着全球电子商务的迅猛发展，物流行业对自动化的需求日益增长。本研究聚焦于仓库中的自动拣选系统，利用深度学习和强化学习技术，旨在提升拣选效率与准确性，同时降低系统故障率。通过实证分析，我们证明了这些技术在提升机器人拣选性能及其对复杂环境适应性方面的有效性。结果表明，集成的机器学习模型显著优于传统方法，能有效应对订单高峰期的处理挑战，减少操作失误，并提升整体物流效率。此外，通过分析环境因素，本研究进一步优化了系统设计，以确保其在多变条件下的高效稳定运行。此项研究不仅为物流自动化提供了创新解决方案，也为未来的技术发展与实际应用奠定了理论与实证基础。