Robot welding seam online grinding system based on laser vision guidance

Abstract Mobile welding robot with adaptive seam tracking ability can greatly improve the welding efficiency and quality, which has been extensively studied. To further improve the automation in multiple station welding, a novel intelligent mobile welding robot consists of a four-wheeled mobile platform and a collaborative manipulator is developed. Under the support of simultaneous localization and mapping (SLAM) technology, the robot is capable of automatically navigating to different stations to perform welding operation. To automatically detect the welding seam, a composite sensor system including an RGB-D camera and a laser vision sensor is creatively applied. Based on the sensor system, the multi-layer sensing strategy is performed to ensure the welding seam can be detected and tracked with high precision. By applying hybrid filter to the RGB-D camera measurement, the initial welding seam could be effectively extracted. Then a novel welding start point detection method is proposed. Meanwhile, to guarantee the tracking quality, a robust welding seam tracking algorithm based on laser vision sensor is presented to eliminate the tracking discrepancy caused by the platform parking error, through which the tracking trajectory can be corrected in real-time. The experimental results show that the robot can autonomously detect and track the welding seam effectively in different station. Also, the multiple station welding efficiency can be improved and quality can also be guaranteed.

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Robot welding seam tracking method based on passive vision for thin plate closed-gap butt welding

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-009-2349-4 ◽

2009 ◽

Vol 48 (9-12) ◽

pp. 945-953 ◽

Cited By ~ 37

Author(s):

Hongbo Ma ◽

Shanchun Wei ◽

Zhongxi Sheng ◽

Tao Lin ◽

Shanben Chen

Keyword(s):

Thin Plate ◽

Seam Tracking ◽

Welding Seam ◽

Butt Welding ◽

Tracking Method ◽

Robot Welding

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Modern tracking methods of the weld seams for robotic welding based on laser vision

Automation. Modern Techologies ◽

10.36652/0869-4931-2021-75-10-442-450 ◽

2021 ◽

Author(s):

Keyword(s):

Control System ◽

Seam Tracking ◽

Automatic Welding ◽

Robotic Welding ◽

Welding Seam ◽

Advantages And Disadvantages ◽

Laser Sensors ◽

Vision Sensors ◽

Laser Vision ◽

Weld Seams

Laser sensors with various technologies used to track weld seams during welding operations are discussed in detail Laser vision sensors provide full automation of welding robotic systems and real-time process monitoring. Reasonable selection of the control system for a robotic welding system with laser vision is represented. Based on the analysis of the advantages and disadvantages, the practical application of laser vision sensors in the process of automatic welding is predicted. Keywords weld seam tracking; laser vision sensor; robotic welding; seam recognition; pre-processing of images; structure of the control system

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A Efficient System based on Model Segmentation for Weld Seam Grinding Robot

10.21203/rs.3.rs-1043038/v1 ◽

2021 ◽

Author(s):

Jimin Ge ◽

Zhaohui Deng ◽

Zhongyang Li ◽

Wei Li ◽

Tao Liu ◽

...

Keyword(s):

Point Cloud ◽

Weld Seam ◽

Least Square Method ◽

Space Structure ◽

Least Square ◽

Robotic Welding ◽

Welding Seam ◽

Efficient System ◽

Extraction Algorithm ◽

Grinding System

Abstract Uneven surface quality often occurs when butt welds are manually grinding, so robotic weld grinding automation has become a fast-developing trend. Weld seam extraction and trajectory planning are important for automatic control of grinding process. However, most of the research on weld extraction is focused on before welding. Due to the irregular shape of the weld after welding, and too little work has been devoted to the weld identification after welding. Consequently, in this paper, a novel simple and efficient weld extraction algorithm is proposed, and the robot grinding path is planned. Firstly, a new flexible bracket structure for welding seam extraction is designed. Secondly, the weld seam section profile model is established, and the processing of spatial point cloud problem is transformed into the processing of two-dimensional point cloud problem. The least square method (LSM) based on threshold comparison is used to segment the weld seam, which greatly improved the processing speed and accuracy. Then the grinding path and pose are obtained according to the extracted weld space structure. Finally, a robotic welding seam automatic grinding system is built. Experiments show that the proposed method could well extract the irregular weld contour after welding and the grinding system built is reliable, which greatly improves the grinding efficiency.

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A Robust Detector for Automated Welding Seam Tracking System

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4049547 ◽

2021 ◽

Vol 143 (7) ◽

Author(s):

Yanbiao Zou ◽

Mingquan Zhu ◽

Xiangzhi Chen

Keyword(s):

Object Detection ◽

Weld Seam ◽

Tracking System ◽

Welding Current ◽

Detection Algorithm ◽

Seam Tracking ◽

Detection Accuracy ◽

Single Shot ◽

Welding Seam ◽

Laser Vision

Abstract Accurate locating of the weld seam under strong noise is the biggest challenge for automated welding. In this paper, we construct a robust seam detector on the framework of deep learning object detection algorithm. The representative object algorithm, a single shot multibox detector (SSD), is studied to establish the seam detector framework. The improved SSD is applied to seam detection. Under the SSD object detection framework, combined with the characteristics of the seam detection task, the multifeature combination network (MFCN) is proposed. The network comprehensively utilizes the local information and global information carried by the multilayer features to detect a weld seam and realizes the rapid and accurate detection of the weld seam. To solve the problem of single-frame seam image detection algorithm failure under continuous super-strong noise, the sequence image multifeature combination network (SMFCN) is proposed based on the MFCN detector. The recurrent neural network (RNN) is used to learn the temporal context information of convolutional features to accurately detect the seam under continuous super-noise. Experimental results show that the proposed seam detectors are extremely robust. The SMFCN can maintain extremely high detection accuracy under continuous super-strong noise. The welding results show that the laser vision seam tracking system using the SMFCN can ensure that the welding precision meets industrial requirements under a welding current of 150 A.

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