The sliding of robot fingers under combined torsion and shear loading

Howe, Robert D.; Kao, Imin; Cutkosky, Mark R.

doi:10.1109/robot.1988.12032

Cited by 91 publications

(42 citation statements)

References 3 publications

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“…There is a significant amount of work on modeling the relationship between forces and motions for sliding and rolling contact interactions [5,12,13,14,15], all providing in some way or another contact models, characterizations of the geometric and frictional constraints induced by contact, and their effects on grasp stability and object motion.…”

Section: Related Workmentioning

confidence: 99%

Prehensile pushing: In-hand manipulation with push-primitives

Chavan-Dafle

Rodríguez

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-This paper explores the manipulation of a grasped object by pushing it against its environment. Relying on precise arm motions and detailed models of frictional contact, prehensile pushing enables dexterous manipulation with simple manipulators, such as those currently available in industrial settings, and those likely affordable by service and field robots.This paper is concerned with the mechanics of the forceful interaction between a gripper, a grasped object, and its environment. In particular, we describe the quasi-dynamic motion of an object held by a set of point, line, or planar rigid frictional contacts and forced by an external pusher (the environment). Our model predicts the force required by the external pusher to "break" the equilibrium of the grasp and estimates the instantaneous motion of the object in the grasp. It also captures interesting behaviors such as the constraining effect of line or planar contacts and the guiding effect of the pusher's motion on the objects's motion.We evaluate the algorithm with three primitive prehensile pushing actions-straight sliding, pivoting, and rolling-with the potential to combine into a broader in-hand manipulation capability.

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Section: Related Workmentioning

confidence: 99%

Prehensile pushing: In-hand manipulation with push-primitives

Chavan-Dafle

Rodríguez

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Theoretical studies of soft-finger contact have reported that the frictional limit attributed to the tangential force and the frictional limit resulting from the twisting moment are not dependent on each other, but they are linearly additive as in Eq. 9 (Howe et al 1988;Omata 1991). Therefore the larger tangential force a finger generates the smaller twisting moment the finger resists…”

Section: Resistive Momentsmentioning

confidence: 99%

Prehension Synergies in Three Dimensions

Shim

Latash

Zatsiorsky

2005

Journal of Neurophysiology

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. The goal of this study was to investigate the conjoint changes of digit forces/moments in 3 dimensions during static prehension under external torques acting on the object in one plane. The experimental paradigm was similar to holding a book vertically in the air where the center of mass of the book is located farther from the hand than the points of digit contacts. Three force and 3 moment components from each digit were recorded during static prehension of a customized handle. Subjects produced forces and moments in all 3 directions, although the external torques were exerted on the handheld object about only the Z-axis. The 3-dimensional response to a 2-dimensional task was explained by the cause-effect chain effects prompted by the noncollinearity of the normal forces of the thumb and the 4 fingers (represented by the "virtual finger"). Because the forces are not collinear (not along the same line), they generate moments of force about X-and Y-axes that are negated by the finger forces along the Y-and X-directions. The magnitudes of forces produced by lateral fingers (index and little) with longer moment arms were larger compared with the central fingers (middle and ring). At the virtual finger (an imaginary digit whose mechanical action is equivalent to the summed action of the 4 fingers) level, the relative contribution of different fractions of the resistive moment produced by subjects did not depend on the torque magnitude. We conclude that the CNS 1) solves a planar prehension task by producing forces and moments in all 3 directions, 2) uses mechanical advantage of fingers, and 3) shares the total torque among finger forces and moments in a particular way disregarding the torque magnitude.

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“…However, slippage still occurs at the point in which the friction torque reaches its maximum value, which we denote as τ slip . In order to achieve a more general physical model for prediction, we take into consideration the effect of both rotational and translational friction forces as discussed in [5], [16]. When an object is subject to both rotational and translational shears, the translational and rotational friction components become correlated as shown in Fig.…”

Section: A Friction Modelmentioning

confidence: 99%

Predicting slippage and learning manipulation affordances through Gaussian Process regression

Viña

Bekiroglu

Smith

et al. 2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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Abstract-Object grasping is commonly followed by some form of object manipulation -either when using the grasped object as a tool or actively changing its position in the hand through in-hand manipulation to afford further interaction. In this process, slippage may occur due to inappropriate contact forces, various types of noise and/or due to the unexpected interaction or collision with the environment.In this paper, we study the problem of identifying continuous bounds on the forces and torques that can be applied on a grasped object before slippage occurs. We model the problem as kinesthetic rather than cutaneous learning given that the measurements originate from a wrist mounted force-torque sensor. Given the continuous output, this regression problem is solved using a Gaussian Process approach.We demonstrate a dual armed humanoid robot that can autonomously learn force and torque bounds and use these to execute actions on objects such as sliding and pushing. We show that the model can be used not only for the detection of maximum allowable forces and torques but also for potentially identifying what types of tasks, denoted as manipulation affordances, a specific grasp configuration allows. The latter can then be used to either avoid specific motions or as a simple step of achieving in-hand manipulation of objects through interaction with the environment.

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The sliding of robot fingers under combined torsion and shear loading

Cited by 91 publications

References 3 publications

Prehensile pushing: In-hand manipulation with push-primitives

Prehensile pushing: In-hand manipulation with push-primitives

Prehension Synergies in Three Dimensions

Predicting slippage and learning manipulation affordances through Gaussian Process regression

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