Robotics & Machine Learning Daily News2024,Issue(Feb.28) :50-51.DOI:10.1007/s00170-024-12973-6

New Robotics Findings Has Been Reported by Investigators at School of Mechanical Engineering (Positioning Error Calibration of Six-axis Robot Based On Sub-identification Space)

Robotics & Machine Learning Daily News2024,Issue(Feb.28) :50-51.DOI:10.1007/s00170-024-12973-6

New Robotics Findings Has Been Reported by Investigators at School of Mechanical Engineering (Positioning Error Calibration of Six-axis Robot Based On Sub-identification Space)

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Abstract

Data detailed on Robotics have been presented. According to news originating from Tianjin, People's Republic of China, by NewsRx correspondents, research stated, "A novel subspace-based positioning error calibration method is proposed for six-axis industrial robots. This method divides the entire workspace of the robot into two sub-identification spaces to achieve error dimensionality reduction."Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the School of Mechanical Engineering, "A spherical S-shaped trajectory with multi-axis linkage is proposed by using a double ball bar (DBB). The error measurement mode combining three-axis and six-axis linkage is adopted to effectively simplify the error identification process and improve the calibration accuracy. In order to evaluate the influence of various errors on the positioning error of the robot end-effector, based on the robot kinematics calibration model, the sensitivity analysis of each axis error is carried out by uniaxial and multi-axis linkage. Compared with the last three axes, the error of the first three axes has a greater impact on the positioning error of the robot end-effector. The installation error of the DBB is eliminated by fitting three orthogonal plane circular trajectories."

Key words

Tianjin/People's Republic of China/Asia/Emerging Technologies/Machine Learning/Robot/Robotics/School of Mechanical Engineering

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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