首页|Reports Outline Robotics Study Results from Xi’an Polytechnic University(A Wall Climbing Robot Based On Machine Vision for AutomaticWelding Seam Inspection)
Reports Outline Robotics Study Results from Xi’an Polytechnic University(A Wall Climbing Robot Based On Machine Vision for AutomaticWelding Seam Inspection)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ro botics. According to news reporting out of Xi’an,People’s Republic of China, by NewsRx editors, research stated, “With the ongoing progress of industrialtechn ology such as shipbuilding, the importance of weld quality in industrial product ion is becomingincreasingly prominent. Intelligent and automated welding seam i nspection robots are more efficient thantraditional manual inspection and can a void dangerous accidents.”Financial supporters for this research include Shaanxi Provincial Department of Education Key ScientificResearch Project, Graduate Innovation Fund Project of X i’an Polytechnic University.Our news journalists obtained a quote from the research from Xi’an Polytechnic U niversity, “This articledescribes the design of a welding seam inspection robot suitable for high-altitude ship operation. The robotuses machine vision and ob ject segmentation models to automatically detect the position of welding seams,and uses a cubic polynomial to fit the welding seam path. The upper and lower co mputers of the robotcommunicate through WIFI transmission and TCP protocol, whi ch can realize remote real-time detectionof weld surface defects. In addition, this article designs a permanent magnet adsorption structure for robothigh-alti tude wall climbing, which has been verified through simulation and experimental verification. Toverify the intelligence of the robot, this paper conducted perf ormance analysis experiments on weld linerecognition and tracking models and su rface defect models. The experimental results showed that theaverage detection accuracy of the weld line recognition and tracking algorithm was 96.8% , and the averagedetection accuracy of surface defects in the three types of we lds was 94.2%.”
Xi’anPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningMachine VisionRobotRoboticsXi’an Polytechnic University
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