On the common-mode and configuration-dependent stiffness control of multiple degrees of freedom hands

Garate, Virginia Ruiz; Tsagarakis, Nikos G.; Biechi, Antonio; Ajoudani, Arash

doi:10.1109/humanoids.2017.8239545

Cited by 4 publications

(8 citation statements)

References 27 publications

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“…This work completes and extends the preliminary study presented in Ruiz Garate et al ( 2017 ). Though the general idea remains the same, validation experiments have been performed, and several key factors of the control strategy have been improved.…”

Section: Introductionsupporting

confidence: 90%

“…Being K t represented by a 3D ellipsoid, differently from Ruiz Garate et al ( 2017 ), where the whole translational matrix numerical values were targeted, here we define the task requirements by means of high-level features of the grasp stiffness ellipsoid geometry. Our method is therefore designed to target, in descending order of importance:…”

Section: Methodsmentioning

confidence: 99%

“…These contact points are obtained analytically as the intersection between the fingertips of the virtual stabilized hand, modeled as spheres of a known radius r s , and the object. This method takes into account the fingertip volume, differing from the one in Ruiz Garate et al ( 2017 ), where contacts where only detectable if an intersection occurred between the the main axis of the last finger link and the object.…”

Section: Methodsmentioning

confidence: 99%

“…These objects are modeled as generic shapes: spheres, cuboids, or cylinders. With this generalization, the method is able to cover a wider variety of objects than it was possible in Ruiz Garate et al ( 2017 ), where only spheres where studied, and Ruiz Garate et al ( 2018 ), where yet no cylinders where available. Different grasps (2, 3, 4 fingertip grasps) are chosen depending on the shape and weight of the object.…”

Section: Methodsmentioning

confidence: 99%

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A Bio-inspired Grasp Stiffness Control for Robotic Hands

et al. 2018

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This work presents a bio-inspired grasp stiffness control for robotic hands based on the concepts of Common Mode Stiffness (CMS) and Configuration Dependent Stiffness (CDS). Using an ellipsoid representation of the desired grasp stiffness, the algorithm focuses on achieving its geometrical features. Based on preliminary knowledge of the fingers workspace, the method starts by exploring the possible hand poses that maintain the grasp contacts on the object. This outputs a first selection of feasible grasp configurations providing the base for the CDS control. Then, an optimization is performed to find the minimum joint stiffness (CMS control) that would stabilize these grasps. This joint stiffness can be increased afterwards depending on the task requirements. The algorithm finally chooses among all the found stable configurations the one that results in a better approximation of the desired grasp stiffness geometry (CDS). The proposed method results in a reduction of the control complexity, needing to independently regulate the joint positions, but requiring only one input to produce the desired joint stiffness. Moreover, the usage of the fingers pose to attain the desired grasp stiffness results in a more energy-efficient configuration than only relying on the joint stiffness (i.e., joint torques) modifications. The control strategy is evaluated using the fully actuated Allegro Hand while grasping a wide variety of objects. Different desired grasp stiffness profiles are selected to exemplify several stiffness geometries.

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Section: Introductionsupporting

confidence: 90%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A Bio-inspired Grasp Stiffness Control for Robotic Hands

et al. 2018

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“…The work presented here aims to exploit these concepts of CDS and CMS to regulate the grasp stiffness of a robotic hand while assuring its stability. This approach differs from the one in [19] first from the problem definition perspective. Being able to represent the grasp stiffness by an ellipsoid, the method proposed here focuses on reproducing its main geometrical features, instead of the overall stiffness matrix numerical values targeted before.…”

mentioning

confidence: 97%

Grasp Stiffness Control in Robotic Hands Through Coordinated Optimization of Pose and Joint Stiffness

Garate

Pozzi

Prattichizzo

et al. 2018

IEEE Robot. Autom. Lett.

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This work presents a novel controller for robotic hands that regulates the grasp stiffness by manipulating the pose and the finger joint stiffness of hands with multiple degrees of freedom while guaranteeing the grasp stability. The proposed approach is inspired by the observations in human motor behaviour that reveal a coordinated pattern of stiffening among the hand fingers, along with a predictive selection of the hand pose to achieve a reliable grasp. The first adjusts the magnitude of the grasp stiffness, while the latter manipulates its overall geometry (shape). The realization of a similar control approach in robotic hands can result in a reduction of the software complexity and also promote a novel mechanical design approach, in which the finger stiffness profiles of the hand are adjusted by only one active component. The proposed control is validated with the fully actuated Allegro Hand, while trying to achieve pre-defined grasp stiffness profiles or modifications of an initial one.

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