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Estimating pose-dependent FRF in machining robots using multibody dynamics and Gaussian Process Regression
     
  
  
刊名:
Robotics and Computer Integrated Manufacturing: An International Journal of Manufacturing and Product and Process Development
作者:
Chen, Han
(Univ Victoria)
Ahmadi, Keivan
(Univ Victoria)
刊号:
737C0069
ISSN:
0736-5845
出版年:
2022
年卷期:
2022, vol.77
页码:
102354
总页数:
13
分类号:
TP24
关键词:
Robotic machining
;
Elastic joints
;
Gaussian Process Regression
;
STABILITY
;
CHATTER
;
IDENTIFICATION
参考中译:
语种:
eng
文摘:
Frequency Response Functions (FRF) of the robot at its TCP are essential for modeling and suppression of industrial robots' vibrations during machining operations. Because the robot's FRF change by posture, measuring the FRF experimentally (e.g. by modal testing) is not efficient, and predictive models are needed to obtain the FRF in arbitrary postures. Multibody dynamics models are efficient in estimating pose-dependent FRF, but they usually include a large number of inertial and joint elastic parameters that must be identified experimentally. Moreover, while the inertial parameters in the model are pose-independent, the joint elastic parameters vary significantly by posture. In this work, we present a new parameter identification method that improves the identifiability of multibody models by systematically imposing constraints according to the robot's rigid-body dynamics as well as its physical and geometrical properties. We then use Gaussian Process Regression (GPR) to model the variation of the joint elastic parameters by posture. This new approach in combining multibody modeling with data-driven modeling (GPR) is more generalizable than purely data-driven methods in predicting pose-dependent FRF because it considers the known physics of the system in predictions. The presented method is used to develop a flexible-joint multibody model for a KUKA KR90 robotic arm with a machining end-effector, and the FRF predicted by the model are compared to the FRF measured by impulse hammer tests to validate their accuracy.
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