Modelling and identification of the da Vinci Research Kit robotic arms

Fontanelli, Giuseppe Andrea; Ficuciello, Fanny; Villani, Luigi; Siciliano, Bruno

doi:10.1109/iros.2017.8205948

Cited by 63 publications

(44 citation statements)

References 11 publications

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“…Other than the gravitational force, there exists significant static joint torque that needs to be modeled and compensated precisely to improve the GCC performance [11], [12]. This static torque is mainly created by the configuration-dependent and direction-dependent force from the electric cables adhered to each joint (Fig.…”

Section: ) Modeling Of the External Disturbance Forcementioning

confidence: 99%

“…[10] applied a standard Single-step Least Square Estimation (SLSE) approach to systematically identify the dynamic parameters. [11] applied the methods proposed by [13] to identify the dynamic parameters of the dVRK arms with physical consistency, using Semi-definite Programming and Linear Matrix Inequality techniques. However, all of the aforementioned work failed to model the significant external disturbance forces caused by electric cables.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental studies in [7], [10] showed that, with these methods, significant motion drifts at the MTM tip were often observed when the MTM was located far from its identification trajectory, preventing the reliable usage of these methods for the entire workspace. [11] proposed to compensate for the elastic force of the external electrical cables with a simple linear spring model. However, the significant unmodeled cable nonlinearities limit the effectiveness of the proposed method ( Fig.…”

Section: Introductionmentioning

confidence: 99%

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A Reliable Gravity Compensation Control Strategy for dVRK Robotic Arms With Nonlinear Disturbance Forces

Lin

Hui

Wang

et al. 2019

IEEE Robot. Autom. Lett.

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External disturbance forces caused by nonlinear springy electrical cables in the master tool manipulator (MTM) of the da Vinci Research Kit (dVRK) limits the usage of the existing gravity compensation methods. Significant motion drifts at the MTM tip are often observed when the MTM is located far from its identification trajectory, preventing the usage of these methods for the entire workspace reliably. In this letter, we propose a general and systematic framework to address the problems of the gravity compensation for the MTM of the dVRK. Particularly, high-order polynomial models were used to capture the highly nonlinear disturbance forces and integrated with the multi-step least square estimation framework. This method allows us to identify the parameters of both the gravitational and disturbance forces for each link sequentially, preventing residual error passing among the links of the MTM with uneven mass distribution. A corresponding gravity compensation controller was developed to compensate the gravitational and disturbance forces. The method was validated with extensive experiments in the majority of the manipulator's workspace, showing significant performance enhancements over existing methods. Finally, a deliverable software package in MAT-LAB and C++ was integrated with dVRK and published in the dVRK community for open-source research and development.Index Terms-Medical robots and systems, calibration and identification, surgical robotics: laparoscopy.

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Section: ) Modeling Of the External Disturbance Forcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Reliable Gravity Compensation Control Strategy for dVRK Robotic Arms With Nonlinear Disturbance Forces

Lin

Hui

Wang

et al. 2019

IEEE Robot. Autom. Lett.

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“…The relative prediction error of each motor joint is shown in Table III. The suffixes, -F and -Y, represent the modeling from [6] and our work, respectively.…”

Section: A Experimental Proceduresmentioning

confidence: 99%

“…Several studies have been reported regarding the dynamic model identification of the dVRK [6]- [9]. Fontanelli et al [6] identified the dynamic parameters of the Master Tool Manipulator (MTM) and Patient Side Manipulator (PSM) of the dVRK using the method proposed in [10]. In our previous work [8], we replicated the approach in [6] and identified the dynamic parameters related to the first three joints of the PSM.…”

Section: Introductionmentioning

confidence: 99%

A Convex Optimization-Based Dynamic Model Identification Package for the da Vinci Research Kit

Wang

Gondokaryono

Munawar

et al. 2019

IEEE Robot. Autom. Lett.

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The da Vinci Research Kit (dVRK) is a teleoperated surgical robotic system. For dynamic simulations and modelbased control, the dynamic model of the dVRK is required. We present an open-source dynamic model identification package for the dVRK, capable of modeling the parallelograms, springs, counterweight, and tendon couplings, which are inherent to the dVRK. A convex optimization-based method is used to identify the dynamic parameters of the dVRK subject to physical consistency. Experimental results show the effectiveness of the modeling and the robustness of the package. Although this software package is originally developed for the dVRK, it is feasible to apply it on other similar robots.

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A hybrid learning‐based hysteresis compensation strategy for surgical robots

Gao

Tan

Sun

2021

Robotics Computer Surgery

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Background The hysteretic forces arising from the electric cables that externally run along the robotic joints are the main disturbance to the precise parameter estimation of gravity compensation model, for the Master Tool Manipulator (MTM) of the da Vinci Research Kit (dVRK). Because such nonlinear disturbance forces and the gravitational forces are often hybrid and in the same magnitude. Methods A strategy is proposed to separate these two hybrid forces, and model them by individual learning‐based algorithms. A specially designed Elastic Hysteresis Neural Network model is employed to capture the hysteresis nature of disturbance forces. Results The experimental results show that our proposed strategy has higher compensation accuracy (78.64%–93.32%), and fewer real samples are required for model estimation (100 samples for each joint). Conclusions Our proposed gravity compensation strategy for the MTM of the dVRK shows great improvement over existing state‐of‐the‐arts methods through conducted comparative experiments.

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Modelling and identification of the da Vinci Research Kit robotic arms

Cited by 63 publications

References 11 publications

A Reliable Gravity Compensation Control Strategy for dVRK Robotic Arms With Nonlinear Disturbance Forces

A Reliable Gravity Compensation Control Strategy for dVRK Robotic Arms With Nonlinear Disturbance Forces

A Convex Optimization-Based Dynamic Model Identification Package for the da Vinci Research Kit

A hybrid learning‐based hysteresis compensation strategy for surgical robots

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