Trajectory planning for shoe sole glue spraying based on cutting plane and multi-scale neighborhood normal vector estimation

This paper focuses on the trajectory planning of glue spraying on shoe sole sidewalls, addressing issues such as low spraying efficiency, high health risks from adhesives, low spraying precision. A method based on cutting plane slicing and multi-scale neighborhood normal vector estimation (TP-MNVE) is proposed. Curvature features and tensor voting are introduced to remove point cloud noise. Boundary search calipers are used to search heel and toe, cutting plane slicing from heel to toe provides an extended line description (XLD) and identifies the highest points on both sides of the contour curves. These highest points, combined with the angle threshold of the spraying process, determine the glue spraying trajectory on the sidewalls. The surface variation of the shoe sole sidewall is incorporated to develop a multi-scale neighborhood weighted normal vector estimation function, yielding the normal vectors of trajectory points. Experimental results on sample shoe soles show that the number of denoised point clouds is reduced by 16,990, with a signal-to-noise ratio (SNR) of 42.156 and entropy of 3.19 × 10^5, outperforming Laplacian, bilateral, and median filtering methods. Boundary segmentation of the toe and heel is accurate, and the cutting plane extraction for the glue spraying trajectory is stable. The trajectory accuracy is 0.931 mm, the root mean square (RMS) of the deviation angle between the estimated and theoretical normal vectors is 0.66°, processing time is less than 1.6 s. This method solves the challenges of glue spraying for various shoe sole styles and promotes automation in the textile industry.