Towards intelligent robotic sole deburring: From burrs identification to path planning

Today, intelligent robotic manufacturing systems are reshaping the production industry. Using robots as actuators, multi-source sensors for perception, and Artificial Intelligence (AI) as decision-making systems, they can perform routine manufacturing tasks, surpassing the capabilities of traditional hard-programmed Computer Numerical Control (CNC) machinery. One specific challenge in footwear manufacturing is sole deburring, traditionally done manually by skilled workers. This paper focuses on developing a robust path-planning pipeline, comprising vision-based and Learning from Demonstrations (LfD) modules for autonomous deburring of soles. The vision-based module exploits Deep Learning (DL) techniques to handle key challenges such as precise segmentation of different soles types across diverse scenarios despite potential occlusions. Additionally, a novel method for burrs identification has been developed leveraging image processing and optimization techniques. Determining the optimal cutting tool orientation during sole deburring relies on human experience. The LfD module aims to impart this knowledge to the robot from videos of expert demonstrations, requiring adaptability to every new incoming sole that needs deburring. Experimental results showcase the method's performance and flexibility, underlining the potential to advance the field of the proposed approach.