Dynamic Identification of the Franka Emika Panda Robot With Retrieval of Feasible Parameters Using Penalty-Based Optimization - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Robotics and Automation Letters Year : 2019

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

Abstract

In this paper, 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.
Fichier principal
Vignette du fichier
IROS_19_Panda.pdf (1.22 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02265293 , version 1 (09-08-2019)

Identifiers

Cite

Claudio Gaz, Marco Cognetti, Alexander Oliva, Paolo Robuffo Giordano, Alessandro de Luca. Dynamic Identification of the Franka Emika Panda Robot With Retrieval of Feasible Parameters Using Penalty-Based Optimization. IEEE Robotics and Automation Letters, 2019, 4 (4), pp.4147-4154. ⟨10.1109/LRA.2019.2931248⟩. ⟨hal-02265293⟩
308 View
9053 Download

Altmetric

Share

Gmail Facebook X LinkedIn More