Punching path optimization method for warp-knitted vamp based on machine vision and improved ant colony algorithm

Abstract

Aiming at the current problems of duplicated paths and low work efficiency in machine punching of warp knitted vamp marker points, this paper proposes a punching path planning method of machine vision combined with intelligent algorithms. The method can improve the timeliness of visual recognition of punching location by limiting the search area and similarity function threshold, and improve the ability of global search and adaptive adjustment in punching path planning by combining with the improved ant colony algorithm to calculate a more accurate and optimized path more efficiently. Through the visual recognition test and the simulation test of the improved ant colony algorithm, the results show that the template matching can correctly identify the positioning hole marker points for different styles, rotation directions and lighting conditions, and the recognition accuracy is 0.43 mm and the repeat positioning accuracy is 0.09 mm; meanwhile, the improved ant colony algorithm can effectively avoid the local optimal solution, which can improve the optimal rate of the result by about 38% and the algorithm can reduce the number of iterations of the optimal solution within 60 times, which greatly saves the calculation time of path planning. The method can be used to improve the efficiency of punching in the actual warp knit vamp punching.