Research on high-precision localization method for transport robots in industrial environments based on Improved AMCL and QR code assistance

The application of handling robots in industrial environments has always been a research hotspot. This paper proposes a positioning scheme for handling robots based on improved adaptive Monte Carlo (AMCL) fusion of multiple sensors and QR code assistance, which can achieve high-precision positioning under low-cost conditions in industrial environments, in response to the positioning accuracy and cost issues of handling robots. Firstly, this article uses the Cartographer algorithm to fuse data from multiple sensors and improve map accuracy. Secondly, this article proposes an improved AMCL algorithm that integrates multiple sensors for localization, enhancing global localization accuracy. Then, in order to further improve the local positioning accuracy, the two-dimensional code assisted positioning system is activated to correct errors when approaching the work point, thereby achieving high-precision positioning near the work point. Meanwhile, utilizing the YOLO Fastest algorithm based on DNN inference framework to improve the efficiency of camera recognition of QR codes. Finally, the transport robot was tested in an industrial environment. The results show that the positioning error of the scheme in the x direction of the workstation point is ± 0.068 m, the positioning error in the y direction is ± 0.069 m, and the heading angle error is ± 0.107 rad. Experimental results have shown that this study helps promote the use of low-cost control methods to achieve high-precision positioning of handling robots in industrial environments.