In this letter, we address the problem of extracting a feasible set of dynamic parameters characterizing the dynamics of a robot manipulator. We start by identifying through an ordinary least squares approach the dynamic coefficients that linearly parametrize the model. From these, we retrieve a set of feasible link parameters (mass, position of center of mass, inertia) that is fundamental for more realistic dynamic simulations or when implementing in real time robot control laws using recursive Newton-Euler algorithms. The resulting problem is solved by means of an optimization method that incorporates constraints on the physical consistency of the dynamic parameters, including the triangle inequality of the link inertia tensors as well as other user-defined, possibly nonlinear constraints. The approach is developed for the increasingly popular Panda robot by Franka Emika, identifying for the first time its dynamic coefficients, an accurate joint friction model, and a set of feasible dynamic parameters. Validation of the identified dynamic model and of the retrieved feasible parameters is presented for the inverse dynamics problem using, respectively, a Lagrangian approach and Newton-Euler computations.

Dynamic Identification of the Franka Emika Panda Robot With Retrieval of Feasible Parameters Using Penalty-Based Optimization / Gaz, Claudio; Cognetti, Marco; Oliva, Alexander; Robuffo Giordano, Paolo; De Luca, Alessandro. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 4:4(2019), pp. 4147-4154. [10.1109/LRA.2019.2931248]

Dynamic Identification of the Franka Emika Panda Robot With Retrieval of Feasible Parameters Using Penalty-Based Optimization

Gaz, Claudio
;
De Luca, Alessandro
2019

Abstract

In this letter, we address the problem of extracting a feasible set of dynamic parameters characterizing the dynamics of a robot manipulator. We start by identifying through an ordinary least squares approach the dynamic coefficients that linearly parametrize the model. From these, we retrieve a set of feasible link parameters (mass, position of center of mass, inertia) that is fundamental for more realistic dynamic simulations or when implementing in real time robot control laws using recursive Newton-Euler algorithms. The resulting problem is solved by means of an optimization method that incorporates constraints on the physical consistency of the dynamic parameters, including the triangle inequality of the link inertia tensors as well as other user-defined, possibly nonlinear constraints. The approach is developed for the increasingly popular Panda robot by Franka Emika, identifying for the first time its dynamic coefficients, an accurate joint friction model, and a set of feasible dynamic parameters. Validation of the identified dynamic model and of the retrieved feasible parameters is presented for the inverse dynamics problem using, respectively, a Lagrangian approach and Newton-Euler computations.
2019
Franka Emika Panda; dynamic identification; friction model; feasible physical parameters; nonlinear global optimization; penalty methods
01 Pubblicazione su rivista::01a Articolo in rivista
Dynamic Identification of the Franka Emika Panda Robot With Retrieval of Feasible Parameters Using Penalty-Based Optimization / Gaz, Claudio; Cognetti, Marco; Oliva, Alexander; Robuffo Giordano, Paolo; De Luca, Alessandro. - In: IEEE ROBOTICS AND AUTOMATION LETTERS. - ISSN 2377-3766. - 4:4(2019), pp. 4147-4154. [10.1109/LRA.2019.2931248]
File allegati a questo prodotto
File Dimensione Formato  
Gaz_Dynamic-Identification_2019.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.57 MB
Formato Adobe PDF
1.57 MB Adobe PDF   Contatta l'autore
Gaz_Postprint_Dynamic-Identification_2019.pdf

Open Access dal 02/07/2020

Note: https://ieeexplore.ieee.org/document/8772145
Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.37 MB
Formato Adobe PDF
1.37 MB Adobe PDF
Gaz_Postprint_Dynamic-Identification_2019_SupplementaryMaterial.pdf

solo gestori archivio

Tipologia: Altro materiale allegato
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.6 MB
Formato Adobe PDF
2.6 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1356777
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 142
  • ???jsp.display-item.citation.isi??? 119
social impact