The da Vinci Research Kit (DVRK) is a telerobotic surgical research platform endowed with an open controller that allows position, velocity and current control. We consider the problem of modelling and identification of both the Patient Side Manipulators (PSMs) and of the Master Tool Manipulators (MTMs) of the platform. This problem is relevant when realistic dynamic simulations have to be performed using standard software tools, but also for the design of model-based control laws, and for the implementation of sensorless strategies for collision detection or contact force estimation. A LMI-based approach is used for the identification of the robot dynamics in order to guarantee the physical feasibility of the parameters that is not ensured by standard least-squares methods. The identified models are validated experimentally.

Modelling and identification of the da Vinci Research Kit robotic arms / Fontanelli, G. A.; Ficuciello, F.; Villani, L.; Siciliano, B.. - 2017-:(2017), pp. 1464-1469. (Intervento presentato al convegno 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 tenutosi a Vancouver, Canada nel 2017) [10.1109/IROS.2017.8205948].

Modelling and identification of the da Vinci Research Kit robotic arms

Fontanelli, G. A.
;
Ficuciello, F.;Villani, L.;Siciliano, B.
2017

Abstract

The da Vinci Research Kit (DVRK) is a telerobotic surgical research platform endowed with an open controller that allows position, velocity and current control. We consider the problem of modelling and identification of both the Patient Side Manipulators (PSMs) and of the Master Tool Manipulators (MTMs) of the platform. This problem is relevant when realistic dynamic simulations have to be performed using standard software tools, but also for the design of model-based control laws, and for the implementation of sensorless strategies for collision detection or contact force estimation. A LMI-based approach is used for the identification of the robot dynamics in order to guarantee the physical feasibility of the parameters that is not ensured by standard least-squares methods. The identified models are validated experimentally.
2017
9781538626825
Modelling and identification of the da Vinci Research Kit robotic arms / Fontanelli, G. A.; Ficuciello, F.; Villani, L.; Siciliano, B.. - 2017-:(2017), pp. 1464-1469. (Intervento presentato al convegno 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 tenutosi a Vancouver, Canada nel 2017) [10.1109/IROS.2017.8205948].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/712146
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