• An initial welding position guidance framework based on uncalibrated visual servoing and binocular cooperation is studied. The proposed method combines the advantages of passive and active vision sensors, and improves the flexibility of the vision system. • The global camera first controls the robot to approach the workpiece through the proposed EKF based uncalibrated visual servoing. Then, the derived cooperative motion equation will lead the robot towards the IWP. • 2 The features extraction algorithms for realizing UVS are studied. The method to estimate the desired position of welding torch is discussed. And a compression strategy for Yolo-v4 is designed to realize real-time welding torch detection, which can be an optional solution for target detection tasks on computational-resource-limited devices. • The simulation experiments demonstrate the feasibility and stability of the proposed guidance framework. The comparison experiments indicate that the framework can increase the flexibility of the vision system while maintaining satisfactory accuracy. Visual sensor based welding guidance technology has played an essential part in intelligentized robotic welding. However, scanning trajectory dependence of the laser vision sensor (LVS) and low accuracy of the passive vision sensor limit the application of single visual sensor based guidance methods in practical welding guidance. Aiming at this problem, a global camera is introduced to LVS system, and a novel initial welding point (IWP) guidance framework based on binocular cooperation is put forward. The robot is first controlled by the global camera to approach the workpiece through the proposed extended Kalman filter based uncalibrated visual servoing (UVS). Then, the derived cooperative equation of the LVS and the global camera will lead the robot toward the IWP. Subsequently, the feature extraction algorithms for UVS are designed. A compression strategy for Yolo-v4 is present to realize real-time welding torch detection in small computing platforms, and the estimation method of desired features is analyzed. Finally, the feasibility of the proposed algorithm is verified through experiments. The proposed framework combines the advantages of rich information for global camera and high precision for LVS, and could be a feasible solution to realize fully autonomous welding guidance. 

A novel visual guidance framework for robotic welding based on binocular cooperation

Three-dimensional (3D) zigzag-line welding seams are found extensively in the manufacturing of marine engineering equipment, heavy lifting equipment, and logistics transportation equipment. Currently, due to the large amount of calculation and poor real-time performance of 3D welding seam detection algorithms, real-time tracking of 3D zigzag-line welding seams is still a challenge especially in high-speed welding. For the abovementioned problems, we proposed a method for the extraction of the pose information of 3D zigzag-line welding seams based on laser displacement sensing and density-based clustering point cloud segmentation during robotic welding. after thee point cloud data of the 3D zigzag-line welding seams was obtained online by the laser displacement sensor, it was segmented using theρ-Approximate DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. In the experiment, high-speed welding was performed on typical low-carbon steel 3D zigzag-line welding seams using gas metal arc welding. The results showed that when the welding velocity was 1000 mm/min, the proposed method obtained a welding seam position detection error of less than 0.35 mm, a welding seam attitude estimation error of less than two degrees, and the running time of the main algorithm was within 120 ms. Thus, the online extraction of the pose information of 3D zigzag-line welding seams was achieved and the requirements of welding seam tracking were met.

Online Extraction of Pose Information of 3D Zigzag-Line Welding Seams for Welding Seam Tracking.

To address the limitations associated with the low intelligence of welding robots, a weld seam type identification and initial point guidance method based on deep neural network named WeldNet was proposed. By incorporating channel shuffling and an attention module, the size of the WeldNet model is reduced while preserving high detection accuracy. With the help of the proposed Center-Box annotation method, the optimized WeldNet network can not only automatically identify the type of welding workpieces at a frequency of 66 Hz, but also extract the initial point of the weld seam with an error of less than 1.63 pixels. Based on the principle of "monocular vision dual position shooting", automatic guidance of the initial point of the weld seam is achieved, which greatly improves the intelligence level of welding robots. The experimental results show that the method proposed can accurately identify various types of weld joints such as butt joints, lap joints, and fillet joints with a recognition rate of 99.6%, and the method can also guide the welding torch to align with the initial point of the weld seam with an error of just 0.85 mm. 

WeldNet: A deep learning based method for weld seam type identification and initial point guidance

The use of laser beam welding with robotic manipulators is expanding towards wider industrial applications as the system availability increases with reduced capital costs. Conventionally, laser welding requires high positioning and coupling accuracy. Due to the variability in the part geometry and positioning, as well as the thermal deformation that may occur during the process, joint position and fit-up are not always acceptable nor predictable a-priori if simple fixtures are used. This makes the passage from virtual CAD/CAM environment to real production site not trivial, limiting applications where short part preparations are a need like small-batch productions. Solutions that render the laser welding operations feasible for production series with non-stringent tolerances are required to serve a wider range of industrial applications. Such solutions should be able to track the seam as well as tolerating variable gaps formed between the parts to be joined. In this work, an online correction for robot trajectory based on a greyscale coaxial vision system with external illumination and an adaptive wobbling strategy are proposed as means to increase the overall flexibility of a manufacturing plant. The underlying vision algorithm and control architectures are presented; the robustness of the system to poor illumination conditions and variable reflection conditions is also discussed. The developed solution employed two control loops: the first is able to change the robot pose to follow varying trajectories; the second, able to vary the amplitude of circular wobbling as a function of the gap formed in butt-joint welds. Demonstrator cases on butt-joint welds with AISI 301 stainless steel with increased complexity were used to test the efficacy of the solution. The system was successfully tested on 2 mm thick, planar stainless-steel sheets at a maximum welding speed of 25 mm/s and yielded a maximum positioning and yaw-orientation errors of respectively 0.325 mm and 4.5°. Continuous welds could be achieved with up to 1 mm gaps and variable seam position with the developed control method. The acceptable weld quality could be maintained up to 0.6 mm gap in the employed autogenous welding configuration. 

Seam tracking and gap bridging during robotic laser beam welding via grayscale imaging and wobbling

3D scanning devices are traditionally employed in reverse engineering tasks that can be carried out semi-automatically, with user assistance. However, their application in manufacturing process control requires automatic point cloud segmentation and extraction of geometric primitives. In this paper, we propose a method for automatic recognition of planes and cylinders (most frequently encountered geometric primitives in mechanical engineering) from unstructured point clouds. The method is based on the scatter of data during least squares fitting of second order surfaces. It consists of three phases and the first phase represents automatic point cloud segmentation. The second phase deals with merging of over-segmented regions and surfaces parameters estimation, whereas the final phase provides extraction of recognized geometric primitives. The method is experimentally verified using three real-world case studies, and its performances are compared with two state of the art recognition algorithms. The results have shown that the proposed method outperforms alternative approaches in terms of appropriately recognized planes and cylinders without surface type confusion, as well as when the recognition of non-existent primitives is considered. In addition, the method determines surfaces parameters with high accuracy.

Automatic recognition of cylinders and planes from unstructured point clouds

Autonomous programming and adaptive filling of lap joint based on three-dimensional welding-seam model by laser scanning

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Autonomous programming and adaptive filling of lap joint based on three-dimensional welding-seam model by laser scanning