## 1 Introduction  
The footwear industry remains one of the most labor-intensive sectors in global manufacturing, relying heavily on skilled manual labor to manage complex processes, such as adhesive application on shoe uppers [1]. Although the trend toward automation has transformed many manufacturing sectors, the transition has been significantly slower in footwear production due to the use of flexible and deformable materials, including textiles, leather, and synthetic fabrics. These materials are difficult to handle with robotic systems because they are prone to shifting or warping, and they require high levels of dexterity and adaptability--qualities still challenging for most automation systems. Projects such as ROBOFOOT have highlighted the technical and operational challenges that hinder full-scale automation in shoemaking [2]. Past industrial attempts, such as a major project in Mexico [3], ultimately failed due to difficulties in managing deformable materials and accommodating diverse shoe geometries in automated systems. As a result,researchers have increasingly focused on the partial automation of specific manufacturing steps--particularly roughing and gluing--where robotic systems can offer tangible productivity and consistency gains. However, these applications require accurate trajectory information tailored to the type, size, and geometry of each shoe to control robotic paths effectively. Several approaches have been developed to extract gluing trajectories, including CAD-based offline planning, contact digitization, and vision-based 3D scanning. CAD-based methods can generate geometry-aware paths based on shoe last models [4; 5], but they often fail to accommodate manufacturing tolerances and deviations found in real-world shoes. Contact-based approaches use digitizers and mechanical fixtures to trace upper contours [6], offering simplicity and low cost but relying on manual operation. A number of studies have explored vision-based trajectory extraction for adhesive application. Some utilize 2D imaging or laser-line profile scanning to identify sole contours and spraying paths [7; 8; 9], while others employ structured light or stereo vision to reconstruct the 3D geometry of soles [10; 11; 12]. These techniques often work well for relatively flat, rigid surfaces but tend to struggle with complex or flexible upper geometries. Previous efforts in footwear automation have also explored early-stage processing [6], flexible gluing systems [13], and 3D-guided sealant dispensing [14]. However, these studies either lacked industrial-grade precision, real production deployment, or the ability to handle complex curved surfaces. In this work, we address these gaps by integrating a triple-view laser triangulation system with robotic path planning to enable precise adhesive spraying on varied lasted upper geometries, with proven throughput in real manufacturing settings. Further work has incorporated laser-based 3D scanning to extract edge or groove features on lasted uppers [15; 16; 17], but accuracy tends to degrade when handling soft or less rigid materials. Some systems have integrated structured-light cameras directly onto robotic end-effectors, enabling trajectory acquisition through dynamic scanning [13; 18]. However, these implementations are usually constrained to stationary workpieces and do not scale well to conveyor-based production. More recent developments have explored multi-robot coordination or offline scanning using rotating platforms to construct complete 3D models of the upper for path planning [19; 20]. While these systems are technically advanced, they are generally unsuitable for real-time production environments where adaptability and speed are critical. These limitations highlight the need for a more flexible and production-ready solution--one capable of capturing each upper's geometry on the fly, planning adaptive adhesive paths, and executing precise robotic spraying.