Abstract
Bolt tightening is one of the typical robot screwing operations. In industry, various types of bolts, high repeatability, and frequent task switching have brought challenges for robots to screw bolts. This paper is based on the human-machine skill transfer method to realize the robot bolt screwing. First, the teaching data was aligned with the trajectory through Dynamic Time Warping (DTW), and then the Gaussian Mixture Model, Gaussian Mixture Regression (GMM-GMR) was used for feature extraction and trajectory information. The screwing trajectory was learned and fitted. Finally, method verification was carried out on the built platform. The results show that the robot based on GMM-GMR has acquired the skills of screwing operation.
Supported by the Joint Funds of the National Natural Science Foundation of China (Grant No. U2013204), Shandong Major Science and Technology Innovation Project (Grand No. 2019JZZY010429), Shandong Provincial Key Research and Development Program (Grand No. 2019TSLH0302).
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Man, Z., Fengming, L., Wei, Q., Yibin, L., Rui, S. (2021). Robot Bolt Skill Learning Based on GMM-GMR. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13014. Springer, Cham. https://doi.org/10.1007/978-3-030-89098-8_22
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