# Colored 3D Path Extraction Based on Depth-RGB Sensor for Welding Robot Trajectory Generation
###### Abstract
[13] present a multifunctional monocular visual sensor based on combined laser structured lights, which has the functions such as the detection of the welding groove cross-sectional parameters, application for the joint tracking, the detection of the welding torch height, the measuring of the weld bead appearance, and the monitoring of welding process in real time. Other approaches for real-time processing are described by [PERSON] et al. [14] to compute the position of the laser beam and the seam in 3D during welding with a camera and illumination laser in order to equalize the brightness of the keyhole and the surrounding area. [PERSON] et al. [15] acquire the 3D information by multiple segment laser scanning. The weld features are extracted by cubic smoothing spline, to detect the characteristic parameters of weld lap joint with a deviation lower than 0.4 mm. Another research topic in robotic vision is systems that acquire images from two optical devices. In this sense, [PERSON] et al. [16] propose a Canny detector, where the two parallel edges captured in a butt v-joint are used to fitting the value of the start welding position. In a similar way, [PERSON] et al. [17] use a Hough transform to detect the outside boundary of the weldment so that the background can be removed. In weld tracking systems, [PERSON] et al. [18] use two normal charge-coupled device cameras to capture clear images from two directions--one is used to measure the root gap, and the other is used to measure the geometric parameters of the weld pool. Nowadays, owing to the precision of sensors and to have a complete understanding of the environment, 3D reconstruction techniques have been explored. In reconstruction with laser systems, 3D point cloud data are used to reconstruct welding seam, through the point cloud and guided by a neural network proposed by [PERSON] et al. [19], which can obtain the equations and initial points of the weld seam. The test results of the guidance prove that the extraction error is less than 0.6 mm, meeting actual production demand. In stereo vision, [PERSON] et al. [20] propose a 3D path teaching method to improve the efficiency of teaching playback based on a stereo-structured light vision system using a seam extraction algorithm, which could achieve fast and accurate seam extraction to modify the model of the weld seam. Their system could well realize a fast and accurate 3D path teaching of a welding robot. Experiment results show a measurement resolution less than 0.7 mm and are suitable for V-type butt joint before welding [21]. In point clouds acquired with RGB-D sensors, [PERSON] et al. [22] use an ASUS Xtion sensor to register and integrate the point cloud with the CAD model to perform an offline programming system for sealant dispensing robot. On the other hand, [PERSON] et al. [23] use an Intel family camera to detect and generate the trajectory with an algorithm based on the gradient of the edge intensity in the point cloud. However, the main limitation of the proposals found in the literature is that they seek to find a solution to a particular type of weld seam. Global path extraction systems are in the process of development; therefore, we find that the integration of color and segmentation of these data have not been the subject of research in welding robotics as a global acquisition system. In this work, a color point cloud segmentation method was implemented to extract 3D paths for robot trajectory generation. The developed system consists of a RealSense D435 sensor, a low-cost device that incorporates technologies such as stereo vision and RGB sensor, with which the 3D reconstruction of a point cloud that incorporates the color of the work object, with this color information a series of filters are applied in the HSV color space to segment the region of interest where the weld bead is expected to be applied. Once captured the zone, a spline cubic interpolation is executed to calculate the path that smoothest the trajectory of the welding points that would require a robotic manipulator.