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This paper aims to develop a method to achieve automatic robotic welding and seam tracking so that three‐dimensional weld seam could be tracked without teaching and good welding formation could be accomplished.

Adaptive image processing method was used for various types of weld seam. Also the relationship between welding height and arc signal was calibrated. Through the decomposition and synthesis, three‐dimensional space type weld seam could be extracted and tracked well. The workpiece without teaching was finally tracked precisely and in a timely way with use of the fuzzy controller.

Composite sensing technology including arc and visual sensing had obvious advantages. Image processing method could be used for tracking plane weld seam efficiently while arc sensing could characterize welding height. Through the coupled controlling algorithm, arc sensing and visual sensing could be fused effectively.

How to couple information more accurately and quickly was still one of the most important problems in composite sensing technology.

Composite sensing technology could reduce costs to achieve weld seam instead such expensive device as laser sensor. The simulating parts of scalloped segment of bottom board for rockets were tracked in the project. Once more adaptive algorithms were developed, more complicated practical workpieces could be dealt with in robotic welding which promotes the application of industry robots.

A useful method for three‐dimensional space type weld seam tracking without teaching was developed. The whole procedure of adaptive image processing method was simple but efficient and robust. The coupled controlling strategy addressed could accomplish seam tracking by composite sensing technology.

This paper aims to propose a set of six-axis robot arm welding seam tracking experiment platform based on Halcon machine vision library to resolve the curve seam tracking issue.

Robot-based and image coordinate systems are converted based on the mathematical model of the three-dimensional measurement of structured light vision and conversion relations between robot-based and camera coordinate systems. An object tracking algorithm via weighted local cosine similarity is adopted to detect the seam feature points to prevent effectively the interference from arc and spatter. This algorithm models the target state variable and corresponding observation vector within the Bayes framework and finds the optimal region with highest similarity to the image-selected modules using cosine similarity.

The paper tests the approach and the experimental results show that using metal inert-gas (MIG) welding with maximum welding current of 200A can achieve real-time accurate curve seam tracking under strong arc light and splash. Minimal distance between laser stripe and welding molten pool can reach 15 mm, and sensor sampling frequency can reach 50 Hz.

Designing a set of six-axis robot arm welding seam tracking experiment platform with a system of structured light sensor based on Halcon machine vision library; and adding an object tracking algorithm to seam tracking system to detect image feature points. By this technology, this system can track the curve seam while welding.

This paper aims to propose a weld seam tracking method based on proximal policy optimization (PPO).

By constructing a neural network based on PPO and using the reference image block and the image block to be detected as the dual-channel input of the network, the method predicts the translation relation between the two images and corrects the location of feature points in the weld image. The localization accuracy estimation network (LAE-Net) is built to update the reference image block during the welding process, which is helpful to reduce the tracking error.

Off-line simulation results show that the proposed algorithm has strong robustness and performs well on the test set of curved seam images with strong noise. In the welding experiment, the movement of welding torch is stable, the molten material is uniform and smooth and the welding error is small, which can meet the requirements of industrial production.

The idea of image registration is applied to weld seam tracking, and the weld seam tracking network is built on the basis of PPO. In order to further improve the tracking accuracy, the LAE-Net is constructed and the reference images can be updated.

This paper presents the design and validation of a universal 6D seam tracking system that reduces the need of accurate robot trajectory programming and geometrical databases in robotic laser scanning. The 6D seam tracking system was developed in the flexible unified simulation environment, integrating software prototyping with mechanical virtual prototyping, based on physical experiments. The validation experiments showed that this system was both robust and reliable and should be able to manage a radius of curvature less than 200 mm. In the pre‐scanning mode, a radius of curvature down to 2 mm was managed for pipe intersections at 3 scans/mm, using a laser scanner with an accuracy of 0.015 mm.

This paper aims to develop a method to extract the weld seam from the welding image.

The initial step is to set the window for the region of the weld seam. Filter and edge‐operator are then applied to acquire edges of images. Based on the prior knowledge about characteristics of the weld seam, a series of routines is proposed to recognize the seam edges and calculate the seam representation.

The proposed method can be used to extract seams of different deviations from noise‐polluted images efficiently. Besides, the method is low time‐consuming and quick enough for real time processing.

Weld seam extraction is the key problem in passive vision based seam tracking technology. The proposed method can extract the weld seam even when the image is noisy, and it is quick enough to be applied in seam tracking technology. The method is expected to improve seam tracking results.

A useful method is developed for weld seam extraction from the noise‐polluted image based on prior knowledge of weld seam. The method is robust and quick enough for real time processing.

The purpose of this paper is to analyze the technology of capturing and processing weld images in real‐time, which is very important to the seam tracking and the weld quality control during the robotic gas tungsten arc welding (GTAW) process.

By analyzing some main parameters on the effect of image capturing, a passive vision sensor for welding robot was designed in order to capture clear and steady welding images. Based on the analysis of the characteristic of the welding images, a new improved Canny algorithm was proposed to detect the edges of seam and pool, and extract the seam and pool characteristic parameters. Finally, the image processing precision was verified by the random welding experiments.

It was found that the seam and pool images can be clearly acquired by using the passive vision system, and the welding image characteristic parameters were accurately extracted through processing. The experiment results show that the precision range of the image processing can be controlled about within ±0.169 mm, which can completely meet the requirement of real‐time seam tracking for welding robot.

This system will be applied to the industrial welding robot production during the GTAW process.

It is very important for the type of teaching‐playback robots with the passive vision that the real‐time images of seam and pool are acquired clearly and processed accurately during the robotic welding process, which helps determine follow‐up seam track and the control of welding quality.

The traditional vision system cannot automatically adjust the feature point extraction method according to the type of welding seam. In addition, the robot cannot self-correct the laying position error or machining error. To solve this problem, this paper aims to propose a hierarchical visual model to achieve automatic arc welding guidance.

The hierarchical visual model proposed in this paper is divided into two layers: welding seam classification layer and feature point extraction layer. In the welding seam classification layer, the SegNet network model is trained to identify the welding seam type, and the prediction mask is obtained to segment the corresponding point clouds. In the feature point extraction layer, the scanning path is determined by the point cloud obtained from the upper layer to correct laying position error. The feature points extraction method is automatically determined to correct machining error based on the type of welding seam. Furthermore, the corresponding specific method to extract the feature points for each type of welding seam is proposed. The proposed visual model is experimentally validated, and the feature points extraction results as well as seam tracking error are finally analyzed.

The experimental results show that the algorithm can well accomplish welding seam classification, feature points extraction and seam tracking with high precision. The prediction mask accuracy is above 90% for three types of welding seam. The proposed feature points extraction method for each type of welding seam can achieve sub-pixel feature extraction. For the three types of welding seam, the maximum seam tracking error is 0.33–0.41 mm, and the average seam tracking error is 0.11–0.22 mm.

The main innovation of this paper is that a hierarchical visual model for robotic arc welding is proposed, which is suitable for various types of welding seam. The proposed visual model well achieves welding seam classification, feature point extraction and error correction, which improves the automation level of robot welding.

Automatic robots can improve the efficiency of liquefied petroleum gas (LPG) tank inspection and maintenance, but it is difficult to achieve high-precision spatial positioning and navigation on tank surfaces. The purpose of this paper is to develop a spatial positioning robotic system for tank inspection. The robot can accurately identify and track weld paths. The positioning system can complete robot’s spatial positioning on tank surfaces.

A tank inspection robot with curvature-adaptive transmission mechanisms is designed in this study. A weld path recognition method based on deep learning is proposed to accurately identify and extract weld paths. Integrated multiple sensors, the positioning system is developed to improve the robot’s spatial positioning accuracy. Experiments are conducted on a cylindrical tank to test weld seam tracking accuracy and spatial positioning performance of the robotic system. The practicality of the robotic system is then verified in field tests.

The robot can accurately identify and track weld seams with a maximum drift angle of 4° and a maximum offset distance of ±30 mm. The positioning system has excellent positioning accuracy and stability. The maximum angle and height errors are 3° and 0.08 m, respectively.

The positioning system can improve the autonomous performance of inspection robots and solve the problems of weld path recognition and spatial positioning. Application of the robotic system can promote the automatic inspection and maintenance of LPG tanks.

This paper aims to develop a feasible visual weld detection method to solve the problems in multi-layer welding detection (e.g. cover pass welding detection) for seam tracking and non-destructive testing. It seeks for an adaptive and accurate way to determine the edge between the seam and the base metal in the grayscale image of weld automatically. This paper tries to contribute to next-generation real-time robotic welding systems for multi-layer welding.

This paper opted for invariant moments to characterize the seam and the base metal for classification purposes. The properties of invariant moments, such as high degree of self-similarity and separation, affine invariance and repetition invariance, were discussed to verify the adaptability of the invariant moment in weld detection. Then, a weld detection method based on invariant moments was proposed to extract the edge between the seam and the base metal, including image division, invariant moment features extraction, K-Means adaptive thresholding, maximum connected domain detection and edge position extraction.

This paper highlights the significance of high degree of self-similarity and separation, affine invariance and repetition invariance of the invariant moment for weld detection. An adaptive, effective and accurate method is proposed to detect the edge between the seam and the base metal based on invariant moments.

It is necessary to verify the applicability of the proposed method in variable welding conditions further. Further works will focus on the establishment of a real-time seam tracking system during the whole multi-layer/multi-pass welding process based on such adaptive visual features.

This paper includes the implications for development of an adaptive and real-time weld detection method, which is expected to be applied to online seam tracking in multi-layer welding.

This paper presents an accurate weld detection method in multi-layer welding, overcoming difficulties in effectiveness, adaptability and efficiency of existing weld detection methods.

The purpose of this paper is to design the localization and tracking algorithms for our mobile welding robot to carry out the large steel structure welding operations in industrial environment.

Extended Kalman filter, considering the bicycle-modeled robot, is adopted in the localization algorithm. The position and orientation of our mobile welding robot is estimated using the feedback of the laser sensor and the robot motion commands history. A backstepping variable is involved in the tracking algorithm. By introducing a specifically selected Lyapunov function, we proved the tracking algorithm using Barbalat Lemma, which leads the errors of estimated robot states to converge to zero.

The experiments show that the proposed localization method is fast and accurate and the tracking algorithm is robust to track straight lines, circles and other typical industrial curve shapes. The proposed localization and tracking algorithm could be used, but not limited to the mobile welding.

Localization problem which is neglected in previous research is very important in mobile welding. The proposed localization algorithm could estimate the robot states timely and accurately, and no additional sensors are needed. Furthermore, using the estimated robot states, we proposed and proved a tracking algorithm for bicycle-modeled mobile robots which could be used in welding as well as other industrial operation scenarios.