首页|Recent Research from Jilin University Highlight Findings in Robotics (Versatile Robotic Welding System Integrating Laser Positioning, Trajectory Fitting and Real-time Tracking)
Recent Research from Jilin University Highlight Findings in Robotics (Versatile Robotic Welding System Integrating Laser Positioning, Trajectory Fitting and Real-time Tracking)
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Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Robotics. According to news reporting originat- ing in Changchun, People’s Republic of China, by NewsRx journalists, research stated, “The three welding modes of laser positioning, trajectory fitting and real-time tracking are key links for flexible robotic welding, because they directly affect the adaptability of welding robots for complex structural parts. Nevertheless, there are few studies on the integration of laser positioning, trajectory fitting and real-time tracking weld- ing.” Funders for this research include National Natural Science Foundation of China (NSFC), Key Scien- tific and Technological Research and Development Projects of Jilin Provincial Science and Technology Department, Jilin Province Metal Materials Advanced Welding Technology Innovation Team. The news reporters obtained a quote from the research from Jilin University, “Aiming at the drawback of that the robot cannot efficiently match the corresponding welding process between different working conditions such as short seam, spatial curve seam and long seam welding, a control scheme that can integrate the above three welding modes is proposed in this paper. First, the welding requirements of laser positioning, trajectory fitting and real-time tracking are transformed into a unified 3D coordinate recognition task for integrated welding, so the identification of welding points under three different processes can be obtained through an identical inference. The next, given the fast inference speed of YOLOv5, a joint ROI extraction algorithm with YOLOv5 as the core is applied. To compensate for YOLOv5 ‘ s inability to directly locate seam centers, iterative centerline unbiased detector (ICUD) and adaptive feature extraction algorithm (AFEA) are proposed without the need for complex image pre-processing and with very strong immunity to strong exposure and strong arc spatter. Finally, teaching trajectory correction model, B-spline curve fitting model, and 3D coordinate real-time tracking model are presented to enhance the adaptability of the welding robot and to flexibly match the appropriate welding mode in various working conditions. Experimental results indicate that the welding trajectory is basically consistent with the seam centerline when laser positioning and trajectory fitting welding.”
ChangchunPeople’s Republic of ChinaAsiaEmerging Tech- nologiesMachine LearningRobotRoboticsRobotsJilin University
NETL
NSTL
Elsevier
