This paper presents a method for automatic seam following of two overlapping carbon fiber mats based on laser scans. We introduce a novel approach that combines one existing and two newly developed edge detection methods in a two out of three voting scheme to obtain high edge tracking robustness. The experimental results demonstrate the feasibility of a fully automated, sensor-guided robotic stitching process. The seam can be located within 1.0 mm at a detection rate of 99.3%.

Nowadays, robust and light-weight parts used in the automobile and aeronautics industry are made of carbon fibres. To increase the mechanical toughness of the parts the carbon fibres are stitched in the preforming process using a sewing robot. However, current systems miss high flexibility and rely on manual programming of each part. The main target of this work is to develop an automatic system that autonomously sets the structure strengthening seams. Therefore, a rapid and flexible following of the carbon textile edges is required. Due to the black and reflective carbon fibres a laser-stripe sensor is necessary and the processing of the range data is a challenging task. The paper proposes a real time approach where different edge detection methodologies are combined in a voting scheme to increase the edge tracking robustness. The experimental results demonstrate the feasibility of a fully automated, sensor-guided robotic sewing process. The seam can be located to within 0.65mm at a detection rate of 99.3% for individual scans.