A methodology for setting grasping force for picking up an object with unknown weight, friction, and stiffness

Sugaiwa, Taisuke; Fujii, Genki; Iwata, Hiroyasu; Sugano, Shigeki

doi:10.1109/ichr.2010.5686331

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Sugaiwa et al [108] presented an algorithm to set the grasping force for an initially unknown object by measuring its physical properties. Their in-hand sensing approach uses the deflection of a passive mechanical element to detect the moment of deformation or slippage, which they combined with the applied forces and hand configuration to deduce specific properties.…”

Section: Other Control Strategiesmentioning

confidence: 99%

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

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This paper aims to provide a comprehensive survey of recent advancements in modelling and autonomous manipulation of non-rigid objects. It first summarizes the recent advances in sensing and modelling of such objects with a focus on describing the methods and technologies used to measure their shape and estimate their material and physical properties. Formal representations considered to predict the deformation resulting from manipulation of non-rigid objects are then investigated. The third part provides a survey of planning and control strategies exploited to operate dexterous robotic systems while performing various tasks on objects made of different non-rigid materials.

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Section: Other Control Strategiesmentioning

confidence: 99%

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

2018

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“…[1,2] Because the control for an HCR is based on the contact force with a human, not only is it important to collect the force data accurately, but soft contact while force sensing is also necessary for safety and security.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional force sensors used for some HCRs are covered with soft material, [1,2] such as polyurethane foam because their main unit is made of metal and quite rigid in terms of contact with a human user. However, the softness of the covering material affects the response of the sensor signal, which may reduce the performance of the force sensor.…”

Section: Introductionmentioning

confidence: 99%

Force estimation via physical properties of air cushion for the control of a human-cooperative robot: basic experiments

Lee

Nozawa²,

Watanabe

et al. 2015

Advanced Robotics

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For the control of a human-cooperative robot (HCR) using a soft material, we aim to verify, in this study, the usefulness of force estimation via physical properties of an air cushion. First, the physical model of the air cushion was represented by an air cylinder with a piston, which is subject to constraint by nonlinear elasticity. Second, the elastic properties of the air cushion were examined under the assumption that it can be formulated by a function of pneumatic pressure. The force applied to the air cushion was next estimated on the basis of the physical model, and the HCR was successfully controlled according to the estimated force.

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“…However, instead of employing geometrical, analytical or signal processing based approaches [2], [4], [5], [11], [12] we follow a kinesthetic learning approach for predicting slippage. In this sense, our work follows more closely approaches in which the robot first interacts with objects and assesses their contact and friction properties prior to executing tasks [13]. Our method also follows the motivation behind learning based approaches in order to deal with the issue of modeling errors and uncertainties in grasping [7], [8], [14].…”

Section: Figmentioning

confidence: 99%

“…To cope with this problem, [13] proposes a set of manipulation actions to estimate properties such as weight, stiffness and friction in order to set appropriate grasping forces.…”

Section: Related Workmentioning

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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A methodology for setting grasping force for picking up an object with unknown weight, friction, and stiffness

Cited by 9 publications

References 14 publications

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

Multi-Modal Sensing and Robotic Manipulation of Non-Rigid Objects: A Survey

Force estimation via physical properties of air cushion for the control of a human-cooperative robot: basic experiments

Predicting slippage and learning manipulation affordances through Gaussian Process regression

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