首页|Findings from LUT University in Robotics Reported (System Identification and For ce Estimation of Robotic Manipulator Using Semirecursive Multibody Formulation)
Findings from LUT University in Robotics Reported (System Identification and For ce Estimation of Robotic Manipulator Using Semirecursive Multibody Formulation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics are disc ussed in a new report. According to news reporting from Lappeenranta, Finland, b y NewsRx journalists, research stated, "Force estimation in multibody dynamics r elies heavily on knowing the system model with a high level of accuracy. However , in complex mechatronic systems, such as robots or mobile machinery, the values of model parameters may be only roughly estimated based on design information, such as CAD data." Funders for this research include Flanders Make, KU Leuven Mechatronic System Dy namics. The news correspondents obtained a quote from the research from LUT University, "The errors in model parameters consequently have a direct effect on force estim ation accuracy because the estimator compensates the erroneous inertia, friction , and applied forces by changing the value of estimated external force. The obje ctive of this study is to present the workflow of system identification and stat e/force estimation of an open-loop multibody structure. The system identificatio n utilizes a linear regression identification method used in robotics adapted to the multibody framework. The semirecursive multibody formulation, in particular , is studied as a formulation for both system identification and force estimatio n. The multibody state/force estimator is constructed using extended Kalman filt er. The specific aim of this paper is to demonstrate the utilization of these pe r se known modeling, identification, and estimation tools to address their curre nt lack of integration as a complete toolchain in virtual sensing of multibody s ystems. The methodology of the study is tested with both artificial and experime ntal data of St & auml;ubli TX40 robotic manipulator. In the exper imental analysis, an openly available benchmark data set was used. Artificial data were created by running an inverse dynamics analysis with inertia and frictio n parameters taken from literature. The results show that the multibody inertia and friction parameters can be accurately identified and the identified model ca n be used to produce decent estimates of external forces. The proposed multibody system identification method itself opens new opportunities in tuning the multi body models used in product development. Moreover, effective use of system ident ification together with state estimation helps to build more accurate estimators ."
LappeenrantaFinlandEuropeEmerging TechnologiesMachine LearningRoboticsRobotsLUT University
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