Research on Navigation Technology of Indoor Warehousing and Logistics Robots

Abstract

With the surging demand for warehousing, indoor logistics robots have become the key to enhancing logistics efficiency. In the current highly dynamic warehouse operation environment, the traditional navigation methods based on fixed paths, such as magnetic strips and QR codes, require the pre-laying of guidance facilities. Problems such as path conflicts and location failures often occur. It is difficult to meet the requirements of smart warehousing. The existing Simultaneous Localization and Mapping robot navigation technology has made considerable progress, but still faces core challenges such as dynamic interference and computational efficiency. This paper focuses on the navigation technology of warehouse robots. By systematically analyzing the performance of visual Simultaneous Localization and Mapping (SLAM), laser SLAM, and multi-sensor fusion technology, it reveals the advantages and disadvantages of various algorithms in terms of accuracy, robustness, and adaptability. Research findings show that deep learning and multi-sensor fusion can significantly enhance the stability of the system in complex environments, but the contradiction between algorithm complexity and real-time performance still needs to be resolved. The research suggests that in the future, lightweight neural network models should be developed and combined with new types of sensors to achieve more efficient and reliable autonomous navigation systems, providing new ideas for the construction of smart logistics.