首页|Research Conducted at Tianjin University of Technology and Education Has Provide d New Information about Robotics (Polarizing Image Fusion-based Pose-measuring A pproach Considering the Measuring Baseline for Hand-eye Calibration of a Scara R obot)
Research Conducted at Tianjin University of Technology and Education Has Provide d New Information about Robotics (Polarizing Image Fusion-based Pose-measuring A pproach Considering the Measuring Baseline for Hand-eye Calibration of a Scara R obot)
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Investigators publish new report on Ro botics. According to news reporting out of Tianjin, People's Republic of China, by NewsRx editors, research stated, "Polarized light technology can be applied t o pose measurement in the hand-eye calibration procedure of a SCARA robot. Howev er, an inappropriate polarizing angle will decrease the image sharpness, which c auses errors in the extracted pixel image coordinates of calibration points." Funders for this research include Tianjin Science and Technology Plan Project, T ianjin University Science and Technology Development Fund Project, Tianjin Scien ce and Technology Popularization Project. Our news journalists obtained a quote from the research from the Tianjin Univers ity of Technology and Education, "Moreover, the noise easily impacts the pose-me asuring accuracy and stability. Aiming at the abovementioned issues, we proposed a new pose-measuring approach for the SCARA robot's hand-eye calibration. First , the polarizing apparatus is used to obtain the polarizing images of the target from multiple polarizing angles, and the image fusion is completed according to the edge sharpness of the calibration points. Through the fused image, the imag e sharpness is ensured, and the imaging quality of the calibration points is imp roved. Second, the normal vector constraints created by the longest measuring ba selines composed of calibration points are considered extra constraints to const ruct the pose-measuring model. This enhances the anti-noise ability without incr easing the number of calibration points. Using our approach, the pose-measuring re-projection error is reduced by 87 %. Utilizing the hand-eye cali bration data acquired by our approach, the robot positioning error is reduced by 47 %. The positioning error of a target is less than 0.04 mm."
TianjinPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsTianjin University of Technology and Education
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