Visual positioning accuracy is crucial for ensuring the successful execution of nut disassembly and assembly tasks by a fastener-nut disassembly and assembly robot. However, disturbances such as on-site lighting changes, abnormal surface conditions of nuts, and complex backgrounds formed by ballast in complex railway environments can lead to poor visual positioning accuracy of the fastener nuts, thereby affecting the success rate of the robot’s continuous disassembly and assembly operations. Additionally, the existing method of detecting fasteners first and then positioning nuts has poor applicability in the field. A direct positioning algorithm for spiral rail spikes that combines an improved Canny algorithm with shape feature similarity determination is proposed in response to these issues. Firstly, CLAHE enhances the image, reducing the impact of varying lighting conditions in outdoor work environments on image details. Then, to address the difficulties in extracting the edges of rail spikes caused by abnormal conditions such as water stains, rust, and oil stains on the nuts themselves, the Canny algorithm is improved through three stages, filtering optimization, gradient boosting, and adaptive thresholding, to reduce the impact of edge loss on subsequent rail spike positioning results. Finally, considering the issue of false fitting due to background interference, such as ballast in gradient Hough transformations, the differences in texture and shape features between the rail spike and interference areas are analyzed. The HOG is used to describe the shape features of the area to be screened, and the similarity between the screened area and the standard rail spike template features is compared based on the standard Euclidean distance to determine the rail spike area. Spiral rail spikes are discriminated based on shape features, and the center coordinates of the rail spike are obtained. Experiments were conducted using images collected from the field, and the results showed that the proposed algorithm, when faced with complex environments with multiple interferences, has a correct detection rate higher than 98% and a positioning error mean of 0.9 mm. It exhibits excellent interference resistance and meets the visual positioning accuracy requirements for robot nut disassembly and assembly operations in actual working environments.

Visual positioning accuracy is crucial for ensuring the successful execution of nut disassembly and assembly tasks by a fastener-nut disassembly and assembly robot. However, disturbances such as on-site lighting changes, abnormal surface conditions of nuts, and complex backgrounds formed by ballast in complex railway environments can lead to poor visual positioning accuracy of the fastener nuts, thereby affecting the success rate of the robot's continuous disassembly and assembly operations. Additionally, the existing method of detecting fasteners first and then positioning nuts has poor applicability in the field. A direct positioning algorithm for spiral rail spikes that combines an improved Canny algorithm with shape feature similarity determination is proposed in response to these issues. Firstly, CLAHE enhances the image, reducing the impact of varying lighting conditions in outdoor work environments on image details. Then, to address the difficulties in extracting the edges of rail spikes caused by abnormal conditions such as water stains, rust, and oil stains on the nuts themselves, the Canny algorithm is improved through three stages: filtering optimization, gradient boosting, and adaptive thresholding, to reduce the impact of edge loss on subsequent rail spike positioning results. Finally, considering the issue of false fitting due to background interference, such as ballast in gradient Hough transformations, the differences in texture and shape features between the rail spike and interference areas are analyzed. The HOG is used to describe the shape features of the area to be screened, and the similarity between the screened area and the standard rail spike template features is compared based on the standard Euclidean distance to determine the rail spike area. Spiral rail spikes are discriminated based on shape features, and the center coordinates of the rail spike are obtained. Experiments were conducted using images collected from the field, and the results showed that the proposed algorithm, when faced with complex environments with multiple interferences, has a correct detection rate higher than 98% and a positioning error mean of 0.9mm. It exhibits excellent interference resistance and meets the visual positioning accuracy requirements for robot nut disassembly and assembly operations in actual working environments.

During the maintenance of railway fasteners, there are issues with the current nut disassembly and assembly operation, including low efficiency, heavy reliance on manual labor, and high physical strain. A mechanical device has been designed to move along the railway track while identifying and locating the center of the nut to perform disassembly and assembly operations. First, based on the nut disassembly and assembly process and the operating environment, the structure of the equipment was designed. This machine can simultaneously disassemble and assemble all the nuts on a single rail tie and accommodate position errors and deviations of spiral spikes. Secondly, to verify the structural reliability of the designed machine, a static simulation analysis was conducted on the key load-bearing structures under extreme operating conditions. Based on the simulation results, a lightweight design was applied to the machine’s carrier platform. The performance of the nut assembly and disassembly mechanism was optimized based on the Kriging model and the Non-dominated Sorting Genetic Algorithm (NSGA-II). The optimized machine reduced its mass by 21.7% and increased its strength by more than 30%. A transient analysis was also conducted on the optimized machine structure, further validating its strength. Finally, based on the design and optimization results, a physical prototype of the nut disassembly machine was constructed and tested. The results show that the device can efficiently perform nut disassembly and assembly tasks on the railway track. Both the mechanical structure’s reliability and functionality meet the design objectives and requirements, demonstrating significant application value for promoting the intelligent maintenance of railways.