首页|Southwest Jiaotong University Researcher Updates Current Study Findings on Robot ics (Real-Time Space Trajectory Judgment for Industrial Robots in Welding Tasks)

Southwest Jiaotong University Researcher Updates Current Study Findings on Robot ics (Real-Time Space Trajectory Judgment for Industrial Robots in Welding Tasks)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ro botics. According to news reporting out of Chengdu, People’s Republic of China, by NewsRx editors, research stated, “In welding tasks, the repeated positioning precision of robots can generally reach the micron level, but the data of each a xis during each operation may vary.” Funders for this research include The Technology Development Project of Swjtu. Our news editors obtained a quote from the research from Southwest Jiaotong Univ ersity: “There may even be out-of-control situations where the robot does not ru n according to the set welding trajectory, which may cause the robot and equipme nt to collide and be damaged. Therefore, a real-time judgment method for the wel ding robot trajectory is proposed. Firstly, multiple sets of axis data are obtai ned by running the welding robot, and the phase of the data is aligned by using a proposed algorithm, and then the Kendall correlation coefficient is used to id entify and remove weak axis data. Secondly, the mean of multiple sets of axis da ta with strong correlation is calculated as the standard trajectory, and the tra jectory threshold of the robot is set using the m ± ns method based on the traje ctory deviation judgment sensitivity. Finally, the absolute difference between t he real-time axis trajectory and the standard trajectory is used to determine th e deviation of the running trajectory. When the deviation reaches the threshold, a forewarning starts.”

Southwest Jiaotong UniversityChengduPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano- robotRobotRobotics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.7)