Gray-Box Model Identification and Payload Estimation for Delta Robots

Falezza, Fabio; Vesentini, Federico; Flumeri, Alessandro Di; Leopardi, Luca; Fiori, Gianni; Mistrorigo, Gianfrancesco; Muradore, Riccardo

doi:10.1016/j.ifacol.2020.12.1378

Cited by 2 publications

(2 citation statements)

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“…All identification schemes so far, especially for the family of Delta-like robots, share some similarities in terms of model simplifications. Due to the lightweight structure of the arms and struts, the slender struts (also called 'forearms') are often regarded as point-masses located at the end of the upper arm [6], [19]. In [14], [20], [21] half of the mass of a strut is added to the end of the upper arm and to the moving platform, respectively.…”

Section: B Model Simplificationmentioning

confidence: 99%

“…In [14], [20], [21] half of the mass of a strut is added to the end of the upper arm and to the moving platform, respectively. In [19] it is assumed that the center of mass (COM) of the upper arm is at the geometric center. The common assumption of the above references is that all upper arms and all struts are identical and can consequently be described by the same inertia parameters.…”

Section: B Model Simplificationmentioning

confidence: 99%

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Dedicated Dynamic Parameter Identification for Delta-Like Robots

Gnad,

Gattringer,

Müller

et al. 2024

IEEE Robot. Autom. Lett.

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Dynamics simulation of parallel kinematic manipulators (PKM) and non-linear control methods require a precisely identified dynamics model and explicit generalized mass matrix. Standard methods, which identify so-called dynamic baseparameters, are not sufficient to this end. Algorithms for identifying the complete set of dynamic parameters were proposed for serial manipulators. A dedicated identification method for PKM does not exist, however. Such a method is introduced here for the large class of Delta-like PKM exploiting the parallel structure and making use of model simplifications specific to this class. The proposed method guarantees physical consistency of the identified parameters, and in particular a positive definite generalized mass matrix. The method is applied to a simulated model with exactly known parameters, which allows for verification of the obtained dynamic parameters. The results show that the generalized mass matrix, the acceleration, and the Coriolis, gravitation and friction terms in the equations of motion (EOM) are well approximated. The second example is a real 4-DOF industrial Delta robot ABB IRB 360-6/1600. For this robot, a physically consistent set of inertia and friction parameters is identified from measurements. The method allows prescribing estimated parameters, but does not rely on such data, e.g. from manufacturer or CAD.

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Section: B Model Simplificationmentioning

confidence: 99%

Section: B Model Simplificationmentioning

confidence: 99%