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

    Gaz, Claudio and Cognetti, Marco and Oliva, Alexander and Robuffo Giordano, Paolo and De Luca, Alessandro (2019) Dynamic Identification of the Franka Emika Panda Robot With Retrieval of Feasible Parameters Using Penalty-Based Optimization. IEEE Robotics and Automation Letters, 4 (4). pp. 4147-4154. ISSN 2377-3774

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    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 NewtonEuler 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.

    Item Type: Article
    Keywords: Franka Emika Panda; dynamic identification; friction model; feasible physical parameters; nonlinear global optimization; penalty methods;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15321
    Identification Number:
    Depositing User: Marco Cognetti
    Date Deposited: 24 Jan 2022 15:19
    Journal or Publication Title: IEEE Robotics and Automation Letters
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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