The DLR hand arm system

Grebenstein, Markus; Albu‐Schäffer, Alin; Bahls, Thomas; Chalon, Maxime; Eiberger, Oliver; Friedl, Werner; Gruber, Robin; Haddadin, Sami; Hagn, Ulrich; Haslinger, Robert; Höppner, Hannes; Jörg, Stefan; Nickl, Mathias; Nothhelfer, Alexander; Petit, Florian; Reill, Josef; Seitz, Nikolaus; Wimböck, Thomas; Wolf, Sebastian; Wüsthoff, Tilo; Hirzinger, Gerd

doi:10.1109/icra.2011.5980371

Cited by 399 publications

(275 citation statements)

References 25 publications

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“…The first tendon-driven robotic hands included the three-fingered Stanford/JPL (Stanford University/NASA Jet Propulsion Laboratory) hand in 1985 (Salisbury 1988(Salisbury , 1991 and the four-fingered MIT/Utah (Massachusetts Institute of Technology/Utah University) hand in 1986 (Jacobsen et al 1986). More recently, the German Aerospace Center (DLR) developed a tendon-driven hand (Grebenstein and van der Smagt 2008;Grebenstein et al 2011), kinematically shaped after the human hand. This hand has a theoretically larger configuration space than the human hand because of its independent joint architecture; each joint of the 19-DoF hand is controlled by two independent motors.…”

Section: Introductionmentioning

confidence: 99%

Human hand modelling: kinematics, dynamics, applications

et al. 2012

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An overview of mathematical modelling of the human hand is given. We consider hand models from a specific background: rather than studying hands for surgical or similar goals, we target at providing a set of tools with which human grasping and manipulation capabilities can be studied, and hand functionality can be described. We do this by investigating the human hand at various levels: (1) at the level of kinematics, focussing on the movement of the bones of the hand, not taking corresponding forces into account; (2) at the musculotendon structure, i.e. by looking at the part of the hand generating the forces and thus inducing the motion; and (3) at the combination of the two, resulting in hand dynam- ics as well as the underlying neurocontrol. Our purpose is to not only provide the reader with an overview of current human hand modelling approaches but also to fill the gaps with recent results and data, thus allowing for an encompassing picture.

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Section: Introductionmentioning

confidence: 99%

Human hand modelling: kinematics, dynamics, applications

et al. 2012

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“…5,6 Further examples of this novel class of robots are Barrett's WAM Arm, 2 Rethink Robotics Baxter, 3 or also more recently the class of variable stiffness/impedance robots. 7,8 Of course, the target domains envisaged for such novel solutions raise the key question of how to ensure human safety during pHRI. 9 As the physical contact between human and robot is the primary potential cause of injury in pHRI, its fundamentals have to be well understood and according countermeasures for its prevention are to be found.…”

Section: Introduction and Contributionmentioning

confidence: 99%

A tool for the evaluation of human lower arm injury: approach, experimental validation and application to safe robotics

Povse¹,

Haddadin

Belder

et al. 2015

Robotica

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SUMMARYThis paper treats the systematic injury analysis of lower arm robot-human impacts. For this purpose, a passive mechanical lower arm (PMLA) was developed that mimics the human impact response and is suitable for systematic impact testing and prediction of mild contusions and lacerations. A mathematical model of the passive human lower arm is adopted to the control of the PMLA. Its biofidelity is verified by a number of comparative impact experiments with the PMLA and a human volunteer. The respective dynamic impact responses show very good consistency and support the fact that the developed device may serve as a human substitute in safety analysis for the described conditions. The collision tests were performed with two different robots: the DLR Lightweight Robot III (LWR-III) and the EPSON PS3L industrial robot. The data acquired in the PMLA impact experiments were used to encapsulate the results in a robot independent safety curve, taking into account robot's reflected inertia, velocity and impact geometry. Safety curves define the velocity boundaries on robot motions based on the instantaneous manipulator dynamics and possible human injury due to unforeseen impacts.

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“…Contact force is generated when the hand hits the object, which needs to be dealt with. The contact force needs to be bounded to prevent any slippage or damage of the object [4,5,6]. …”

Section: Introductionmentioning

confidence: 99%

Robotic Grasping System Using Convolutional Neural Networks

Bezák¹,

Nikitin²,

Božek³

2014

AJME

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Object grasping by robot hands is challenging due to the hand and object modeling uncertainties, unknown contact type and object stiffness properties. To overcome these challenges, the essential purpose is to achieve the mathematical model of the robot hand, model the object and the contact between the object and the hand. In this paper, an intelligent hand-object contact model is developed for a coupled system assuming that the object properties are known. The control is simulated in the Matlab Simulink/ SimMechanics, Neural Network Toolbox and Computer Vision System Toolbox..

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The DLR hand arm system

Cited by 399 publications

References 25 publications

Human hand modelling: kinematics, dynamics, applications

Human hand modelling: kinematics, dynamics, applications

A tool for the evaluation of human lower arm injury: approach, experimental validation and application to safe robotics

Robotic Grasping System Using Convolutional Neural Networks

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