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.

本研究解决了在快速发展的全球电子商务背景下仓库自动拣货效率和准确性不足的问题。通过深度学习和强化学习技术的应用，论文提出了一种集成机器学习模型，显著提升了机器人拣货性能并适应复杂环境。研究表明，该方法有效降低了系统故障率，提升了物流效率，为未来物流自动化提供了创新解决方案和理论基础。

利用机器学习优化仓库机器人自动拣货系统